statistics thesis topics

Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them.

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time.

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

Literacy rate in a city.
Abortion and pregnancy rate in the USA.
Eating disorders in the citizens.
Parent role in self-esteem and confidence of the student.
Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

Statistical analysis for legs and head injuries in Football.
Statistical analysis for shoulder and knee injuries in MotoGP.
Deep statistical evaluation for the doping test in sports from the past decade.
Statistical observation on the performance of athletes in the last Olympics.
Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
Statistical evolution to find out the suicide reason among students and adults.
Statistics analysis to find out the effect of divorce on children in a country.
Psychology affects women because of the gender gap in specific country areas.
Statistics analysis to find out the cause of online bullying in students’ lives.
In Psychology, PTSD and descriptive tendencies are discussed.
The function of researchers in statistical testing and probability.
Acceptable significance and probability thresholds in clinical Psychology.
The utilization of hypothesis and the role of P 0.05 for improved comprehension.
What types of statistical data are typically rejected in psychology?
The application of basic statistical principles and reasoning in psychological analysis.
The role of correlation is when several psychological concepts are at risk.
Actual case study learning and modeling are used to generate statistical reports.
In psychology, naturalistic observation is used as a research sample.
How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

Does education have a deep impact on the financial success of an individual?
The investment in digital technology is having a meaningful return for corporations?
The gap of financial wealth between rich and poor in the USA.
A statistical approach to identify the effects of high-frequency trading in financial markets.
Statistics analysis to determine the impact of the multi-agent model in financial markets.

Personalized Medicine Statistics Research Topics

Statistical analysis on the effect of methamphetamine on substance abusers.
Deep research on the impact of the Corona vaccine on the Omnicrone variant.
Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
Statistics analysis to identify the role of genes in the child’s overall immunity.
What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

Generic vs private education is one of the best for the students and has better financial return.
Psychology vs physiology: which leads the person not to quit their addictions?
Effect of breastmilk vs packed milk on the infant child overall development
Which causes more accidents: male alcoholics vs female alcoholics.
What causes the student not to reveal the cyberbullying in front of their parents in most cases.

Easy Statistics Research Topics

Application of statistics in the world of data science
Statistics for finance: how statistics is helping the company to grow their finance
Advantages and disadvantages of Radar chart
Minor marriages in south-east Asia and African countries.
Discussion of ANOVA and correlation.
What statistical methods are most effective for active sports?
When measuring the correctness of college tests, a ranking statistical approach is used.
Statistics play an important role in Data Mining operations.
The practical application of heat estimation in engineering fields.
In the field of speech recognition, statistical analysis is used.
Estimating probiotics: how much time is necessary for an accurate statistical sample?
How will the United States population grow in the next twenty years?
The legislation and statistical reports deal with contentious issues.
The application of empirical entropy approaches with online grammar checking.
Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

Uses of statistics in chemometrics
Statistics in business analytics and business intelligence
Importance of statistics in physics.
Deep discussion about multivariate statistics
Uses of Statistics in machine learning

Survey Topics for Statistics

Gather the data of the most qualified professionals in a specific area.
Survey the time wasted by the students in watching Tvs or Netflix.
Have a survey the fully vaccinated people in the USA
Gather information on the effect of a government survey on the life of citizens
Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

Have a deep decision of Bayes theorems
Discuss the Bayesian hierarchical models
Analysis of the process of Japanese restaurants.
Deep analysis of Lévy’s continuity theorem
Analysis of the principle of maximum entropy

AP Statistics Topics

Discuss about the importance of econometrics
Analyze the pros and cons of Probit Model
Types of probability models and their uses
Deep discussion of ortho stochastic matrix
Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics

National income and the regulation of cryptocurrency.
The benefits and drawbacks of regression analysis.
How can estimate methods be used to correct statistical differences?
Mathematical prediction models vs observation tactics.
In sociology research, there is bias in quantitative data analysis.
Inferential analytical approaches vs. descriptive statistics.
How reliable are AI-based methods in statistical analysis?
The internet news reporting and the fluctuations: statistics reports.
The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

Role of statistics in business in 2023
Importance of business statistics and analytics
What is the role of central tendency and dispersion in statistics
Best process of sampling business data.
Importance of statistics in big data.
The characteristics of business data sampling: benefits and cons of software solutions.
How may two different business tasks be tackled concurrently using linear regression analysis?
In economic data relations, index numbers, random probability, and correctness are all important.
The advantages of a dataset approach to statistics in programming statistics.
Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

Evaluate the role of John Tukey’s contribution to statistics.
The role of statistics to improve ADHD treatment.
The uses and timeline of probability in statistics.
Deep analysis of Gertrude Cox’s experimental design in statistics.
Discuss about Florence Nightingale in statistics.
What sorts of music do college students prefer?
The Main Effect of Different Subjects on Student Performance.
The Importance of Analytics in Statistics Research.
The Influence of a Better Student in Class.
Do extracurricular activities help in the transformation of personalities?
Backbenchers’ Impact on Class Performance.
Medication’s Importance in Class Performance.
Are e-books better than traditional books?
Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further.

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

Introduction of a problem
Methodology explanation and choice.
Statistical research itself is in the main part (Body Part).
Samples deviations and variables.
Lastly, statistical interpretation is your last part (conclusion).

Note: Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific.

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project.

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects.

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic.

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more.

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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Home » 500+ Statistics Research Topics

500+ Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

Analysis of the effectiveness of different marketing strategies on consumer behavior.
An investigation into the relationship between economic growth and environmental sustainability.
A study of the effects of social media on mental health and well-being.
A comparative analysis of the educational outcomes of public and private schools.
The impact of climate change on agriculture and food security.
A survey of the prevalence and causes of workplace stress in different industries.
A statistical analysis of crime rates in urban and rural areas.
An evaluation of the effectiveness of alternative medicine treatments.
A study of the relationship between income inequality and health outcomes.
A comparative analysis of the effectiveness of different weight loss programs.
An investigation into the factors that affect job satisfaction among employees.
A statistical analysis of the relationship between poverty and crime.
A study of the factors that influence the success of small businesses.
A survey of the prevalence and causes of childhood obesity.
An evaluation of the effectiveness of drug addiction treatment programs.
A statistical analysis of the relationship between gender and leadership in organizations.
A study of the relationship between parental involvement and academic achievement.
An investigation into the causes and consequences of income inequality.
A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
A survey of the prevalence and causes of substance abuse among teenagers.
An evaluation of the effectiveness of online education compared to traditional classroom learning.
A statistical analysis of the impact of globalization on different industries.
A study of the relationship between social media use and political polarization.
An investigation into the factors that influence customer loyalty in the retail industry.
A comparative analysis of the effectiveness of different types of advertising.
A survey of the prevalence and causes of workplace discrimination.
An evaluation of the effectiveness of different types of employee training programs.
A statistical analysis of the relationship between air pollution and health outcomes.
A study of the factors that affect employee turnover rates.
An investigation into the causes and consequences of income mobility.
A comparative analysis of the effectiveness of different types of leadership styles.
A survey of the prevalence and causes of mental health disorders among college students.
An evaluation of the effectiveness of different types of cancer treatments.
A statistical analysis of the impact of social media influencers on consumer behavior.
A study of the factors that influence the adoption of renewable energy sources.
An investigation into the relationship between alcohol consumption and health outcomes.
A comparative analysis of the effectiveness of different types of conflict resolution strategies.
A survey of the prevalence and causes of childhood poverty.
An evaluation of the effectiveness of different types of diversity training programs.
A statistical analysis of the relationship between immigration and economic growth.
A study of the factors that influence customer satisfaction in the service industry.
An investigation into the causes and consequences of urbanization.
A comparative analysis of the effectiveness of different types of economic policies.
A survey of the prevalence and causes of elder abuse.
An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
A statistical analysis of the impact of automation on different industries.
A study of the factors that influence employee productivity in the workplace.
An investigation into the causes and consequences of gentrification.
A comparative analysis of the effectiveness of different types of humanitarian aid.
A survey of the prevalence and causes of homelessness.
Exploring the relationship between socioeconomic status and access to healthcare services
An analysis of the relationship between parental education level and children’s academic performance.
Exploring the effects of different statistical models on prediction accuracy in machine learning.
The Impact of Social Media on Consumer Behavior: A Statistical Analysis
Bayesian hierarchical modeling for network data analysis
Spatial statistics and modeling for environmental data
Nonparametric methods for time series analysis
Bayesian inference for high-dimensional data analysis
Multivariate analysis for genetic data
Machine learning methods for predicting financial markets
Causal inference in observational studies
Sampling design and estimation for complex surveys
Robust statistical methods for outlier detection
Statistical inference for large-scale simulations
Survival analysis and its applications in medical research
Mixture models for clustering and classification
Time-varying coefficient models for longitudinal data
Multilevel modeling for complex data structures
Graphical modeling and Bayesian networks
Experimental design for clinical trials
Inference for network data using stochastic block models
Nonlinear regression modeling for data with complex structures
Statistical learning for social network analysis
Time series forecasting using deep learning methods
Model selection and variable importance in high-dimensional data
Spatial point process modeling for environmental data
Bayesian spatial modeling for disease mapping
Functional data analysis for longitudinal studies
Bayesian network meta-analysis
Statistical methods for big data analysis
Mixed-effects models for longitudinal data
Clustering algorithms for text data
Bayesian modeling for spatiotemporal data
Multivariate analysis for ecological data
Statistical analysis of genomic data
Bayesian network inference for gene regulatory networks
Principal component analysis for high-dimensional data
Time series analysis of financial data
Multivariate survival analysis for complex outcomes
Nonparametric estimation of causal effects
Bayesian network analysis of complex systems
Statistical inference for multilevel network data
Generalized linear mixed models for non-normal data
Bayesian inference for dynamic systems
Latent variable modeling for categorical data
Statistical inference for social network data
Regression models for panel data
Bayesian spatiotemporal modeling for climate data
Predictive modeling for customer behavior analysis
Nonlinear time series analysis for ecological systems
Statistical modeling for image analysis
Bayesian hierarchical modeling for longitudinal data
Network-based clustering for high-dimensional data
Bayesian spatial modeling for ecological systems.
Analysis of the Effect of Climate Change on Crop Yields: A Case Study
Examining the Relationship Between Physical Activity and Mental Health in Young Adults
A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
Investigating the Effect of Online Learning on Student Performance in Mathematics
A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
Analyzing the Effect of Social Support on Mental Health in Elderly Populations
A Comparative Study of Different Methods for Forecasting Stock Prices
Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
Examining the Effect of Parental Involvement on Student Achievement in Elementary School
Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
A Study of the Relationship Between Personality Traits and Academic Achievement
Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
Investigating the Effect of Mobile Advertising on Consumer Behavior
Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
Analyzing the Relationship Between Workplace Diversity and Organizational Performance
Investigating the Effect of Gamification on Learning and Motivation in Education
A Study of the Relationship Between Social Support and Depression in Cancer Patients
Examining the Impact of Technology on the Travel Industry Using Statistical Methods
Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
Identifying the Key Factors Affecting Student Success in Online Learning Environments
Analyzing the Relationship Between Employee Engagement and Organizational Commitment
Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
Investigating the Effect of Social Support on Postpartum Depression in New Mothers
Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
Identifying factors that influence voting behavior and election outcomes using statistical methods.
Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
Developing new statistical models to better understand the spread of infectious diseases.
Analyzing the impact of climate change on global food production using statistical methods.
Identifying patterns and trends in social media data using statistical methods.
Investigating the relationship between social networks and mental health using statistical methods.
Developing new statistical models to predict financial market trends and identify investment opportunities.
Analyzing the effectiveness of different educational programs and interventions using statistical methods.
Investigating the impact of environmental factors on public health using statistical methods.
Developing new statistical models to analyze complex biological systems and identify new drug targets.
Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
Investigating the relationship between diet and health outcomes using statistical methods.
Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
Investigating the effectiveness of different medical treatments using statistical methods.
Developing new statistical models to analyze the impact of air pollution on public health.
Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
Investigating the impact of automation on the job market using statistical methods.
Developing new statistical models to analyze climate data and predict future climate trends.
Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
Investigating the impact of social media on political discourse using statistical methods.
Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
Developing new statistical models to analyze the relationship between human behavior and the environment.
Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
Investigating the impact of immigration on labor market outcomes using statistical methods.
Developing new statistical models to analyze the relationship between social determinants and health outcomes.
Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
Investigating the effectiveness of different marketing strategies using statistical methods.
Developing new statistical models to analyze the relationship between air pollution and climate change.
Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
Investigating the impact of social media on mental health using statistical methods.
Developing new statistical models to analyze the impact of transportation on the environment.
Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
Investigating the impact of social networks on political participation using statistical methods.
Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
Investigating the impact of technological change on the labor market using statistical methods.
Developing new statistical models to analyze the impact of climate change on agriculture.
Investigating the impact of social media on mental health: A longitudinal study.
A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
Examining the relationship between sleep duration and productivity among college students.
A study of the factors that influence employee job satisfaction in the tech industry.
Analyzing the relationship between income level and health outcomes among low-income populations.
Investigating the effectiveness of online learning platforms for high school students.
A study of the factors that contribute to success in online entrepreneurship.
Analyzing the impact of climate change on agricultural productivity in developing countries.
A comparison of different statistical models for predicting stock market trends.
Examining the impact of sports on mental health: A cross-sectional study.
A study of the factors that influence employee retention in the hospitality industry.
Analyzing the impact of cultural differences on international business negotiations.
Investigating the effectiveness of different weight loss interventions for obese individuals.
A study of the relationship between personality traits and academic achievement.
Examining the impact of technology on job displacement: A longitudinal study.
A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
Analyzing the impact of environmental regulations on corporate profitability.
Investigating the effectiveness of different types of therapy for treating depression.
A study of the factors that contribute to success in e-commerce.
Examining the relationship between social support and mental health in the elderly population.
A comparison of different statistical methods for analyzing complex survey data.
Analyzing the impact of employee diversity on organizational performance.
Investigating the effectiveness of different types of exercise for improving cardiovascular health.
A study of the relationship between emotional intelligence and job performance.
Examining the impact of work-life balance on employee well-being.
A comparison of the effectiveness of different types of financial education programs for low-income populations.
Analyzing the impact of air pollution on respiratory health in urban areas.
Investigating the relationship between personality traits and leadership effectiveness.
A study of the factors that influence consumer behavior in the luxury goods market.
Examining the impact of social networks on political participation: A cross-sectional study.
A comparison of different statistical methods for analyzing survival data.
Analyzing the impact of government policies on income inequality.
Investigating the effectiveness of different types of counseling for substance abuse.
A study of the relationship between cultural values and consumer behavior.
Examining the impact of technology on privacy: A longitudinal study.
A comparison of the effectiveness of different types of online marketing strategies.
Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
Investigating the effectiveness of different types of education interventions for improving financial literacy.
A study of the factors that contribute to success in social entrepreneurship.
Examining the impact of gender diversity on board performance in publicly-traded companies.
A comparison of different statistical methods for analyzing panel data.
Analyzing the impact of employee involvement in decision-making on organizational performance.
Investigating the effectiveness of different types of treatment for anxiety disorders.
A study of the relationship between cultural values and entrepreneurial success.
Examining the impact of technology on the labor market: A longitudinal study.
A comparison of the effectiveness of different types of direct mail campaigns.
Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
Analyzing the effectiveness of different educational interventions in improving student performance
Investigating the impact of climate change on food production and food security
Identifying factors that influence employee satisfaction and productivity in the workplace
Examining the prevalence and causes of mental health disorders in different populations
Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
Analyzing the prevalence and consequences of substance abuse in different communities
Investigating the relationship between social media use and mental health outcomes
Examining the role of genetics in the development of different diseases
Identifying factors that contribute to the gender wage gap in different industries
Analyzing the effectiveness of different policing strategies in reducing crime rates
Investigating the impact of immigration on economic growth and development
Examining the prevalence and causes of domestic violence in different populations
Evaluating the effectiveness of different interventions for treating addiction
Analyzing the prevalence and impact of childhood obesity on health outcomes
Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
Examining the effects of different types of exercise on physical and mental health outcomes
Identifying factors that influence voter behavior and political participation
Analyzing the prevalence and impact of sleep disorders on health outcomes
Investigating the effectiveness of different educational interventions in improving health outcomes
Examining the impact of environmental pollution on public health outcomes
Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
Analyzing the prevalence and causes of homelessness in different communities
Investigating the relationship between race and health outcomes
Examining the impact of social support networks on health outcomes
Identifying factors that contribute to income inequality in different regions
Analyzing the prevalence and impact of workplace stress on employee health outcomes
Investigating the relationship between education and income levels in different communities
Examining the effects of different types of technology on mental health outcomes
Evaluating the effectiveness of different interventions for reducing healthcare costs
Analyzing the prevalence and impact of chronic pain on health outcomes
Investigating the relationship between urbanization and public health outcomes
Examining the effects of different types of drugs on health outcomes
Identifying factors that contribute to educational attainment in different populations
Analyzing the prevalence and causes of food insecurity in different communities
Investigating the relationship between race and crime rates
Examining the impact of social media on political participation and engagement
Evaluating the effectiveness of different interventions for reducing poverty levels
Analyzing the prevalence and impact of stress on mental health outcomes
Investigating the relationship between religion and health outcomes
Examining the effects of different types of parenting styles on child development outcomes
Identifying factors that contribute to political polarization in different regions
Analyzing the prevalence and causes of teenage pregnancy in different communities
Investigating the impact of globalization on economic growth and development
Examining the prevalence and impact of social isolation on mental health outcomes
Evaluating the effectiveness of different interventions for reducing gun violence
Analyzing the prevalence and impact of bullying on mental health outcomes
Investigating the relationship between immigration and crime rates
Examining the effects of different types of diets on health outcomes
Identifying factors that contribute to social inequality in different regions
Bayesian inference for high-dimensional models
Analysis of longitudinal data with missing values
Nonparametric regression with functional predictors
Estimation and inference for copula models
Statistical methods for neuroimaging data analysis
Robust methods for high-dimensional data analysis
Analysis of spatially correlated data
Bayesian nonparametric modeling
Statistical methods for network data
Optimal experimental design for nonlinear models
Multivariate time series analysis
Inference for partially identified models
Statistical learning for personalized medicine
Statistical inference for rare events
High-dimensional mediation analysis
Analysis of multi-omics data
Nonparametric regression with mixed types of predictors
Estimation and inference for graphical models
Statistical inference for infectious disease dynamics
Robust methods for high-dimensional covariance matrix estimation
Analysis of spatio-temporal data
Bayesian modeling for ecological data
Multivariate spatial point pattern analysis
Statistical methods for functional magnetic resonance imaging (fMRI) data
Nonparametric estimation of conditional distributions
Statistical methods for spatial econometrics
Inference for stochastic processes
Bayesian spatiotemporal modeling
High-dimensional causal inference
Analysis of data from complex survey designs
Bayesian nonparametric survival analysis
Statistical methods for fMRI connectivity analysis
Spatial quantile regression
Statistical modeling for climate data
Estimation and inference for item response models
Bayesian model selection and averaging
High-dimensional principal component analysis
Analysis of data from clinical trials with noncompliance
Nonparametric regression with censored data
Statistical methods for functional data analysis
Inference for network models
Bayesian nonparametric clustering
High-dimensional classification
Analysis of ecological network data
Statistical modeling for time-to-event data with multiple events
Estimation and inference for nonparametric density estimation
Bayesian nonparametric regression with time-varying coefficients
Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

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Researcher, Academic Writer, Web developer

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120 Statistical Research Topics: Explore Up-to-date Trends

Statistical Research Topics Latest Trends & Techniques

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

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Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

Predictive Healthcare Modeling with Machine Learning
Analyzing Online Education During COVID-19 Epidemic
Modeling How Climate Change Affects Natural Disasters
Essential Elements Influencing Personnel Productivity
Social Media Influence on Customer Choices and Behavior
Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
Financial Markets and Stock Price Predictions
Statistical Analysis of Voting-related Behaviors
An Analysis of Public Transportation Usage Trends in Urban Areas
How Can Public Health Education Reduce Air Pollution?
Statistical Analysis of Suicide In Adolescents and Adults
A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

Major Factors Influencing College Students’ Academic Performance
Social Media and How It Defines thee Mental Health of Students
Evaluation of the Elements Influencing Student Engagement and Retention
An Examination of Extracurricular Activities On Academic Success
Does Parental Involvement Determine Academic Achievement of Kids?
Examining How Technology Affects Improving Educational Performance
Factors That Motivate Students’ Involvement In Online Learning
The Impact of Socioeconomic Status On Academic Performance
Does Criticism Enhance Student Performance?
Student-Centered Learning and Improved Performance
A Cursory Look At Students’ Career Goals and Major Life Decisions
Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

Financial Data And Stock Price Forecasting
Investigation of Variables Influencing Students’ Grades
What Causes Traffic Flow and Congestion In Urban Areas?
How to Guarantee Customer Retention In the Retail Sector
Using Epidemiological Data to Model the Spread of Infectious Diseases
Does Direct Advertisement Affect Consumer Preferences and Behavior?
How to Predict and Adapt to Climate Change
Using Spatial Statistics to Analyze Trends and Patterns In Crime
Examination of the Elements Influencing Workplace Morale and Productivity
Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
How Many Percent Get Married After Their Degree Programs?
A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

Analysis of the Variables Affecting A Startup’s Success
The Valid Connection Between Mental Health and Social Media Use
Different Teaching Strategies and Academic Performance
Factors Influencing Employee Satisfaction In Different Work Environments
The Impact of Public Policy On Different Population Groups
Reviewing Different Health Outcomes and Incomes
Different Marketing Tactics for Good Service Promotion
What Influences Results In Different Sports Competitions?
Differentiating Elements Affecting Students’ Performance In A Given Subject
Internal Communication and Building An Effective Workplace
Does the Use of Business Technologies Boost Workers’ Output?
The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

How Global Pandemic Affects Local Businesses
Investigating the Link Between Income and Health Outcomes In a Demography
Key Motivators for Student’s Performance In a Particular Academic Program
Evaluating the Success of a Promotional Plan Over Others
Continuous Social Media Use and Impact On Mental Health
Does Culture Impact the Religious Beliefs of Certain Groups?
Key Indicators of War and How to Manage These Indicators
An Overview of War As a Money Laundering Scheme
How Implementations Guarantee Effectiveness of Laws In Rural Areas
Performance of Students In War-torn Areas
Key Indicators For Measuring the Success of Your Venture
How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students will want to write in include:

The Most Affected Age Demography By the Covid-19 Pandemic
The Health Outcomes Peculiar to a Specific Demography
Unusual Ways to Enhance Student Performance In a Classroom
How Marketing Efforts Can Determine Promotional Outputs
Can Mental Health Solutions Be Provided On Social Media?
Assessing How Certain Species Are Affected By Climate Change.
What Influences Voter Turnouts In Different Elections?
How Many People Have Used Physical Exercises to Improve Mental Health
How Financial Circumstances Can Determine Criminal Activities
Ways DUI Laws Can Reduce Road Accidents
Examining the Connection Between Corruption and Underdevelopment In Africa
What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

Retail Client Behaviors and Weather Trends
The Impact of Marketing Initiatives On Sales and Customer Retention
How Socioeconomic Factors Determine Crime Rates In Different Locations
Public and Private School Students: Who Performs Better?
How Fitness Affects the Mental Health of People In Different Ages
Focus On the Unbanked Employees Globally
Does Getting Involve In a Kid’s Life Make Them Better?
Dietary Decisions and a Healthy Life
Managing Diabetes and High Blood Pressure of a Specific Group
How to Engage Different Learning Methods for Effectiveness
Understudying the Sleeping Habits of Specific Age Groups
How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

Understanding Consumer Spending and Behavior In Different Regions
Why Some People in Certain Areas Live Longer than Others
Comparative Analysis of Different Customer Behaviors
Do Social Media Businesses Benefit More than Physical Businesses?
Does a Healthy Work Environment Guarantee Productivity?
The Impact of Ethnicity and Religion On Voting Patterns
Does Financial Literacy Guarantee Better Money Management?
Cultural Identities and Behavioral Patterns
How Religious Orientation Determines Social Media Use
The Growing Need for Economists Globally
Getting Started with Businesses On Social Media
Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

Consumer Satisfaction-Related Variables on E-Commerce Websites
Obesity Rates and Socioeconomic Status In Developed Countries
How Marketing Strategies Can Make or Mar Sales Performance
The Correlation Between Increased Income and Happiness In Various Nations
Regression Models and Forecasting Home Prices
Climate Change Affecting Agricultural Production In Specific Areas
A Study of Employee Satisfaction In the Healthcare Industry
Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
Predicting the Risk of Default Among Credit Card Holders In Different Regions
Why Crime Rates Are Increasing In Urban Areas than Rural Areas
Statistical Evaluation of Methamphetamine’s Impact On Drug Users
Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

Social Media’s Effects On Consumer Behavior
The Correlation Between Urban Crime Rates and Poverty Levels
Physical Exercise and Mental Health Consequences
Predictive Modeling In the Financial Markets
How Minimum Wage Regulations Impact Employment Rates
Healthcare Outcomes and Access Across Various Socioeconomic Groups
How High School Students’ Environment Affect Academic Performance
Automated Technology and Employment Loss
Environmental Elements and Their Effects On Public Health
Various Advertising Tactics and How They Influence Customer Behavior
Political Polarization And Economic Inequality
Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

Mathematics and Statistics Theses and Dissertations

Theses/dissertations from 2024 2024.

The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan

On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest

Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo

Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain

Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang

Theses/Dissertations from 2023 2023

Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani

Applied Analysis for Learning Architectures , Himanshu Singh

Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze

Theses/Dissertations from 2022 2022

New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana

Advances and Applications of Optimal Polynomial Approximants , Raymond Centner

Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty

On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly

Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He

Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias

Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi

A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman

Theses/Dissertations from 2021 2021

Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri

Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi

Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou

Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando

Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu

Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang

Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang

Theses/Dissertations from 2020 2020

Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi

Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun

Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu

On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman

Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek

Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen

Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan

Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop

On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink

Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang

Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala

Theses/Dissertations from 2019 2019

Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian

Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar

Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil

Fractional Random Weighted Bootstrapping for Classiﬁcation on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter

Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz

Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi

Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi

Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos

The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva

Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak

Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich

An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado

Power Graphs of Quasigroups , DayVon L. Walker

Theses/Dissertations from 2018 2018

Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed

Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai

A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah

Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa

Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields

Generalizations of Quandles and their cohomologies , Matthew J. Green

Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu

Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou

Human Activity Recognition Based on Transfer Learning , Jinyong Pang

Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham

Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel

Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova

Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang

Theses/Dissertations from 2017 2017

Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack

Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon

On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill

Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill

Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly

Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera

Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao

Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati

Dynamics of Multicultural Social Networks , Kristina B. Hilton

Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi

Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally

Patterns in Words Related to DNA Rearrangements , Lukas Nabergall

Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na

Schreier Graphs of Thompson's Group T , Allen Pennington

Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya

Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo

Real-time Classiﬁcation of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi

Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou

Theses/Dissertations from 2016 2016

A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea

Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery

Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman

On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr

Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim

Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano

Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru

Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park

Leonard Systems and their Friends , Jonathan Spiewak

Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun

Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu

Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu

Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classiﬁcation and Clustering , Xing Wang

On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd

Theses/Dissertations from 2015 2015

Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen

Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko

Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana

Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf

Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner

Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao

Theses/Dissertations from 2014 2014

Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo

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Statistics Research Topics: Ideas & Questions

June 16, 2023

Looking for research topics in statistics? Whether you’re a student working on a class project or a researcher in need of inspiration, finding the right topic can be challenging. With numerous areas to explore in statistics, narrowing down your options can be overwhelming. But with some creativity and research, you can find an interesting and relevant topic. This article offers ideas and examples of statistics research topics to consider, so let’s dive in!

Statistics Research: What It Comprises

The data collected by statistics research can be quantitative (numbers) or qualitative (text). The data can also be presented in tables or graphs for easy understanding by the audience. However, it is not always necessary to present the data in the form of tables or graphs, as sometimes the raw data can be good enough to convey the message from the researcher.

In statistics projects, the researchers usually design experiments to test specific hypotheses about a population’s characteristics or behavior. For example, suppose you want to know whether people who wear glasses will have better eyesight than those who don’t wear glasses. In that case, you need to collect information about their vision before and after wearing glasses (experimental group) and compare their vision with those who do not wear glasses (control group). You would then find out whether there was any difference between these two groups with respect to eyesight improvement due to wearing glasses.

Tips on How to Choose a Statistics Research Topic

Firstly, remember that a good statistics topic should interest you and also have a substantial amount of data available for analysis. Once you have decided on your topic, you can collect data for your study using secondary sources or conducting primary research through surveys or interviews.

You can also use search engines like Google or Yahoo! to find information about your topic of interest. You can use keywords like “income disparity” or “inequality causes” to find relevant websites on which you can find information related to your topic of interest.

Next, consider what types of questions your supervisor would like answered with this data type. For example, if you’re looking at crime rates in your city, maybe they would like to know which areas have higher crime rates than others to plan police patrols accordingly. Or maybe they just want to know whether there’s any correlation between high crime rates and low-income neighborhoods (there probably will be).

Feel free to select any topic and try our free AI essay generator to craft your essay.

Statistics Research Topics in Business

Understanding the factors that influence consumer purchase decisions in the technology industry
Advertising and sales revenue: a time-series analysis
The effectiveness of customer loyalty programs in increasing customer retention and revenue
The relationship between employee job satisfaction and productivity
The factors that contribute to employee turnover in the hospitality industry
Product quality on customer satisfaction and loyalty: a longitudinal study
The application of social media marketing in increasing brand awareness and customer engagement
Corporate social responsibility (CSR) initiatives and brand reputation: a meta-analysis
Understanding the factors that influence customer satisfaction in the restaurant industry
E-commerce on traditional brick-and-mortar retail sales: a comparative analysis
The effectiveness of supply chain management strategies in reducing operational costs and improving efficiency
The relationship between market competition and innovation: a cross-country analysis
Understanding the factors that influence employee motivation and engagement in the workplace
Business analytics on strategic decision-making: a case study approach
The effectiveness of performance-based incentives in increasing employee productivity and job satisfaction
Organizational performance dependence on employee diversity and organizational performance
Understanding the factors that contribute to startup success in the technology industry
The impact of pricing strategies on sales revenue and profitability
The effectiveness of corporate training programs in improving employee skill development and performance
The relationship between brand image and customer loyalty

Research Topics in Applied Statistics

The impact of educational attainment on income level
The effectiveness of different advertising strategies in increasing sales
The relationship between socioeconomic status and health outcomes
The effectiveness of different teaching methods in promoting academic success
The impact of job training programs on employment rates
The relationship between crime rates and community demographics
Different medication dosages in treating a particular condition
The influence of environmental pollutants on health outcomes
The interconnection between access to healthcare and health outcomes
The effectiveness of different weight loss programs in promoting weight loss
The impact of social support on mental health outcomes
The relationship between demographic factors and political affiliation
The effectiveness of different exercise programs in promoting physical fitness
The influence of parenting styles on child behavior
The relationship between diet and chronic disease risk
Different smoking cessation programs for promoting smoking cessation
The impact of public transportation on urban development
The relationship between technology usage and social isolation
The effectiveness of different stress reduction techniques in reducing stress levels
The influence of climate change on crop

Statistics Research Topics in Psychology

The correlation between childhood trauma and adult depression
The effectiveness of cognitive-behavioral therapy in treating anxiety disorders
The impact of social media on self-esteem and body image in adolescents
Personality traits and job satisfaction: how are they related?
The prevalence and predictors of bullying in schools
The effects of sleep deprivation on cognitive performance
The role of parenting styles in the development of emotional intelligence
The effectiveness of mindfulness-based interventions in reducing stress and anxiety
The impact of childhood abuse on adult relationship satisfaction
The influence of social support on coping with chronic illness
The factors that contribute to successful aging
The prevalence and predictors of addiction relapse
The impact of cultural factors on mental health diagnosis and treatment
Exercise and mental health: in which way are they connected?
The effectiveness of art therapy in treating trauma-related disorders
The prevalence and predictors of eating disorders in college students
The influence of attachment styles on romantic relationships
The effectiveness of group therapy in treating substance abuse disorders
The prevalence and predictors of postpartum depression
The impact of childhood socioeconomic

Sports Statistics Research Topics

The relationship between player performance and team success in the National Football League (NFL)
Understanding the factors that influence home-field advantage in professional soccer
The impact of game-day weather conditions on player performance in Major League Baseball (MLB)
The effectiveness of different training regimens in improving endurance and performance in long-distance running
The relationship between athlete injury history and future injury risk in professional basketball
The impact of crowd noise on team performance in college football
The effectiveness of sports psychology interventions in improving athlete performance and mental health
The relationship between player height and success in professional basketball: a regression analysis
Understanding the factors that contribute to the development of youth soccer players in the United States
The influence of playing surface on injury rates in professional football: a longitudinal study
The effectiveness of pre-game routines in improving athlete performance in tennis
The relationship between athletic ability and academic success among college athletes
Understanding the factors that influence injury risk and recovery time in professional hockey players
The impact of in-game statistics on coaching decisions in professional basketball
The effectiveness of different dietary regimens in improving athlete performance in endurance sports
The relationship between athlete sleep habits and performance: a longitudinal study
Understanding the factors that influence athlete endorsement deals and sponsorships in professional sports
The influence of stadium design on crowd noise levels and player performance in college football
The effectiveness of different strength training regimens in improving athlete performance in track and field events
The relationship between player salary and team success in professional baseball: a longitudinal analysis

Survey Methods Statistics Research Topics

Understanding the factors that influence response rates in online surveys
The effectiveness of different survey question formats in eliciting accurate and reliable responses
The relationship between survey mode (phone, online, mail) and response quality in political polling
The impact of incentives on survey response rates and data quality
Understanding the factors that contribute to respondent satisfaction in surveys
The effectiveness of different sampling methods in achieving representative samples in survey research
The relationship between survey item order and response bias: a meta-analysis
The impact of social desirability bias on survey responses: a longitudinal study
Understanding the factors that influence survey question wording and response bias
The effectiveness of different visual aids in improving respondent comprehension and response quality
The relationship between survey timing and response rate: a comparative analysis
The impact of interviewer characteristics on survey response quality in face-to-face surveys
Understanding the factors that contribute to nonresponse bias in survey research
The effectiveness of different response scales in measuring attitudes and perceptions in surveys
The relationship between survey length and respondent engagement: a cross-sectional analysis
The influence of skip patterns on survey response quality and completion rates
Understanding the factors that influence survey item nonresponse and item refusal rates
The effectiveness of pre-testing and piloting surveys in improving data quality and reliability
The relationship between survey administration and response quality: a comparative analysis of phone, online, and in-person surveys
The impact of survey fatigue on response quality and data completeness: a longitudinal study

As mentioned above, statistics is the science of collecting and analyzing data to draw conclusions and make predictions. To conduct a proper statistical analysis, you must first define your research question, gather data from various sources, analyze the information, and draw conclusions based on the results.

This process can be challenging for many people who do not have an extensive background in statistics. However, it does not have to be so tricky if you use our professional Custom Writing help. Our writers are highly qualified professionals who will work with you to develop a clear understanding of your research problem and then guide you through every step of the process. We will also ensure that your paper follows all academic standards to meet all requirements for originality and quality.

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Xiaojie Mao - “Machine learning methods for data-driven decision making : contextual optimization, causal inference, and algorithmic fairness”

Dissertation Advisor: Nathan Kallus and Madeleine Udell

Initial job placement: Tsinghua University, China

Xin Bing - “Structured latent factor models : Identifiability, estimation, inference and prediction”

Initial job placement: Cambridge (postdoc), University of Toronto

Yang Liu - “Nonparametric regression and density estimation on a network"

Dissertation Advisor: David Ruppert and Peter Frazier

Initial job placement: Research Analyst - Cubist Systematic Strategies

Skyler Seto - “Learning from less : improving and understanding model selection in penalized machine learning problems”

Initial job placement: Machine Learning Researcher - Apple

Jiekun Feng - “Markov chain, Markov decision process, and deep reinforcement learning with applications to hospital management and real-time ride-hailing”

Initial job placement:

Wenyu Zhang - “Methods for change point detection in sequential data”

Initial job placement: Research Scientist - Institute for Infocomm Research

Liao Zhu - “The adaptive multi-factor model and the financial market"

Initial job placement: Quantitative Researcher - Two Sigma

Xiaoyun Quan - “Latent Gaussian copula model for high dimensional mixed data, and its applications”

Dissertation Advisor: James Booth and Martin Wells

Praphruetpong (Ben) Athiwaratkun - "Density representations for words and hierarchical data"

Dissertation Advisor: Andrew Wilson

Initial job placement: AI Scientist - AWS AI Labs

Yiming Sun - “High dimensional data analysis with dependency and under limited memory”

Dissertation Advisor: Sumanta Basu and Madeleine Udell

Zi Ye - “Functional single index model and jensen effect"

Dissertation Advisor: Giles Hooker

Initial job placement: Data & Applied Scientist - Microsoft

Hui Fen (Sarah) Tan - “Interpretable approaches to opening up black-box models”

Dissertation Advisor: Giles Hooker and Martin Wells

Daniel E. Gilbert - “Luck, fairness and Bayesian tensor completion”

Yichen zhou - “asymptotics and interpretability of decision trees and decision tree ensemblesg”.

Initial job placement: Data Scientist - Google

Ze Jin - “Measuring statistical dependence and its applications in machine learning”

Initial job placement: Research Scientist, Facebook Integrity Ranking & ML - Facebook

Xiaohan Yan - “Statistical learning for structural patterns with trees”

Dissertation Advisor: Jacob Bien

Initial job placement: Senior Data Scientist - Microsoft

Guo Yu - “High-dimensional structured regression using convex optimization”

Dan kowal - "bayesian methods for functional and time series data".

Dissertation Advisor: David Matteson and David Ruppert

Initial job placement: assistant professor, Department of Statistics, Rice University

Keegan Kang - "Data Dependent Random Projections"

David sinclair - "model selection results for high dimensional graphical models on binary and count data with applications to fmri and genomics", liu, yanning – "statistical issues in the design and analysis of clinical trials".

Dissertation Advisor: Bruce Turnbull

Nicholson, William Bertil – "Tools for Modeling Sparse Vector Autoregressions"

Tupper, laura lindley – "topics in classification and clustering of high-dimensional data", chetelat, didier – "high-dimensional inference by unbiased risk estimation".

Initial Job Placement: Assistant Professor Universite de Montreal, Montreal, Canada

Gaynanova, Irina – "Estimation Of Sparse Low-Dimensional Linear Projections"

Dissertation Advisor: James Booth

Initial Job Placement: Assistant Professor, Texas A&M, College Station, TX

Mentch, Lucas – "Ensemble Trees and CLTS: Statistical Inference in Machine Learning"

Initial Job Placement: Assistant Professor, University of Pittsburgh, Pittsburgh, PA

Risk, Ben – "Topics in Independent Component Analysis, Likelihood Component Analysis, and Spatiotemporal Mixed Modeling"

Dissertation Advisors: David Matteson and David Ruppert

Initial Job Placement: Postdoctoral Fellow, University of North Carolina, Chapel Hill, NC

Zhao, Yue – "Contributions to the Statistical Inference for the Semiparametric Elliptical Copula Model"

Disseration Advisor: Marten Wegkamp

Initial Job Placement: Postoctoral Fellow, McGill University, Montreal, Canada

Chen, Maximillian Gene – "Dimension Reduction and Inferential Procedures for Images"

Dissertation Advisor: Martin Wells

Earls, Cecelia – Bayesian hierarchical Gaussian process models for functional data analysis

Dissertation Advisor: Giles Hooker

Initial Job Placement: Lecturer, Cornell University, Ithaca, NY

Li, James Yi-Wei – "Tensor (Multidimensional Array) Decomposition, Regression, and Software for Statistics and Machine Learning"

Initial Job Placement: Research Scientist, Yahoo Labs

Schneider, Matthew John – "Three Papers on Time Series Forecasting and Data Privacy"

Dissertation Advisor: John Abowd

Initial Job Placement: Assistant Professor, Northwestern University, Evanston, IL

Thorbergsson, Leifur – "Experimental design for partially observed Markov decision processes"

Initial Job Placement: Data Scientist, Memorial Sloan Kettering Cancer Center, New York, NY

Wan, Muting – "Model-Based Classification with Applications to High-Dimensional Data in Bioinformatics"

Initial Job Placement: Senior Associate, 1010 Data, New York, NY

Johnson, Lynn Marie – "Topics in Linear Models: Methods for Clustered, Censored Data and Two-Stage Sampling Designs"

Dissertation Advisor: Robert Strawderman

Initial Job Placement: Statistical Consultant, Cornell, Statistical Consulting Unit, Ithaca, NY

Tecuapetla Gomez, Inder Rafael – "Asymptotic Inference for Locally Stationary Processes"

Initial Job Placement: Postdoctoral Fellow, Georg-August-Universitat Gottigen, Gottigen, Germany.

Bar, Haim – "Parallel Testing, and Variable Selection -- a Mixture-Model Approach with Applications in Biostatistics"

Dissertation Advisor: James Booth

Initial Job Placement: Postdoc, Department of Medicine, Weill Medical Center, New York, NY

Cunningham, Caitlin – "Markov Methods for Identifying ChIP-seq Peaks"

Initial Job Placement: Assistant Professor, Le Moyne College, Syracuse, NY

Ji, Pengsheng – "Selected Topics in Nonparametric Testing and Variable Selection for High Dimensional Data"

Dissertation Advisor: Michael Nussbaum

Initial Job Placement: Assistant Professor, University of Georgia, Athens, GA

Morris, Darcy Steeg – "Methods for Multivariate Longitudinal Count and Duration Models with Applications in Economics"

Dissertation Advisor: Francesca Molinari

Initial Job Placement: Research Mathematical Statistician, Center for Statistical Research and Methodology, U.S. Census Bureau, Washington DC

Narayanan, Rajendran – "Shrinkage Estimation for Penalised Regression, Loss Estimation and Topics on Largest Eigenvalue Distributions"

Initial Job Placement: Visiting Scientist, Indian Statistical Institute, Kolkata, India

Xiao, Luo – "Topics in Bivariate Spline Smoothing"

Dissertation Advisor: David Ruppert

Initial Job Placement: Postdoc, Johns Hopkins University, Baltimore, MD

Zeber, David – "Extremal Properties of Markov Chains and the Conditional Extreme Value Model"

Dissertation Advisor: Sidney Resnick

Initial Job Placement: Data Analyst, Mozilla, San Francisco, CA

Clement, David – "Estimating equation methods for longitudinal and survival data"

Dissertation Advisor: Robert Strawderman

Initial Job Placement: Quantitative Analyst, Smartodds, London UK

Eilertson, Kirsten – "Estimation and inference of random effect models with applications to population genetics and proteomics"

Dissertation Advisor: Carlos Bustamante

Initial Job Placement: Biostatistician, The J. David Gladstone Institutes, San Francisco CA

Grabchak, Michael – "Tempered stable distributions: properties and extensions"

Dissertation Advisor: Gennady Samorodnitsky

Initial Job Placement: Assistant Professor, UNC Charlotte, Charlotte NC

Li, Yingxing – "Aspects of penalized splines"

Initial Job Placement: Assistant Professor, The Wang Yanan Institute for Studies in Economics, Xiamen University

Lopez Oliveros, Luis – "Modeling end-user behavior in data networks"

Dissertation Advisor: Sidney Resnick

Initial Job Placement: Consultant, Murex North America, New York NY

Ma, Xin – "Statistical Methods for Genome Variant Calling and Population Genetic Inference from Next-Generation Sequencing Data"

Initial Job Placement: Postdoc, Stanford University, Stanford CA

Kormaksson, Matthias – "Dynamic path analysis and model based clustering of microarray data"

Dissertation Advisor: James Booth

Initial Job Placement: Postdoc, Department of Public Health, Weill Cornell Medical College, New York NY

Schifano, Elizabeth – "Topics in penalized estimation"

Initial Job Placement: Postdoc, Department of Biostatistics, Harvard University, Boston MA

Hanlon, Bret – "High-dimensional data analysis"

Dissertation Advisor: Anand Vidyashankar

Shaby, Benjamin – "Tools for hard bayesian computations"

Initial Job Placement: Postdoc, SAMSI, Durham NC

Zipunnikov, Vadim – "Topics on generalized linear mixed models"

Initial Job Placement: Postdoc, Department of Biostatistics, Johns Hopkins University, Baltimore MD

Barger, Kathryn Jo-Anne – "Objective bayesian estimation for the number of classes in a population using Jeffreys and reference priors"

Dissertation Advisor: John Bunge

Initial Job Placement: Pfizer Incorporated

Chan, Serena Suewei – "Robust and efficient inference for linear mixed models using skew-normal distributions"

Initial Job Placement: Statistician, Takeda Pharmaceuticles, Deerfield IL

Lin, Haizhi – "Distressed debt prices and recovery rate estimation"

Dissertation Advisor: Martin Wells

Initial Job Placement: Associate, Fixed Income Department, Credit Suisse Securities (USA), New York, NY

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

Research topics and ideas about data science and big data analytics

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

Developing machine learning models for real-time fraud detection in online transactions.
The use of big data analytics in predicting and managing urban traffic flow.
Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
The application of predictive analytics in personalizing cancer treatment plans.
Analyzing consumer behavior through big data to enhance retail marketing strategies.
The role of data science in optimizing renewable energy generation from wind farms.
Developing natural language processing algorithms for real-time news aggregation and summarization.
The application of big data in monitoring and predicting epidemic outbreaks.
Investigating the use of machine learning in automating credit scoring for microfinance.
The role of data analytics in improving patient care in telemedicine.
Developing AI-driven models for predictive maintenance in the manufacturing industry.
The use of big data analytics in enhancing cybersecurity threat intelligence.
Investigating the impact of sentiment analysis on brand reputation management.
The application of data science in optimizing logistics and supply chain operations.
Developing deep learning techniques for image recognition in medical diagnostics.
The role of big data in analyzing climate change impacts on agricultural productivity.
Investigating the use of data analytics in optimizing energy consumption in smart buildings.
The application of machine learning in detecting plagiarism in academic works.
Analyzing social media data for trends in political opinion and electoral predictions.
The role of big data in enhancing sports performance analytics.
Developing data-driven strategies for effective water resource management.
The use of big data in improving customer experience in the banking sector.
Investigating the application of data science in fraud detection in insurance claims.
The role of predictive analytics in financial market risk assessment.
Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

The application of big data in public transportation systems for route optimization.
Investigating the impact of big data analytics on e-commerce recommendation systems.
The use of data mining techniques in understanding consumer preferences in the entertainment industry.
Developing predictive models for real estate pricing and market trends.
The role of big data in tracking and managing environmental pollution.
Investigating the use of data analytics in improving airline operational efficiency.
The application of machine learning in optimizing pharmaceutical drug discovery.
Analyzing online customer reviews to inform product development in the tech industry.
The role of data science in crime prediction and prevention strategies.
Developing models for analyzing financial time series data for investment strategies.
The use of big data in assessing the impact of educational policies on student performance.
Investigating the effectiveness of data visualization techniques in business reporting.
The application of data analytics in human resource management and talent acquisition.
Developing algorithms for anomaly detection in network traffic data.
The role of machine learning in enhancing personalized online learning experiences.
Investigating the use of big data in urban planning and smart city development.
The application of predictive analytics in weather forecasting and disaster management.
Analyzing consumer data to drive innovations in the automotive industry.
The role of data science in optimizing content delivery networks for streaming services.
Developing machine learning models for automated text classification in legal documents.
The use of big data in tracking global supply chain disruptions.
Investigating the application of data analytics in personalized nutrition and fitness.
The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
Developing predictive models for customer churn in the telecommunications industry.
The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
An Essay on How Data Science Can Strengthen Business (Santos, 2023)
A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

Research Topic Kickstarter - Need Help Finding A Research Topic?

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Knowledge Base

The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
Null hypothesis: Parental income and GPA have no relationship with each other in college students.
Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
Experimental
Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

Variable	Type of data
Age	Quantitative (ratio)
Gender	Categorical (nominal)
Race or ethnicity	Categorical (nominal)
Baseline test scores	Quantitative (interval)
Final test scores	Quantitative (interval)


Parental income	Quantitative (ratio)
GPA	Quantitative (interval)

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

Probability sampling: every member of the population has a chance of being selected for the study through random selection.
Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

your sample is representative of the population you’re generalizing your findings to.
your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

Will you have resources to advertise your study widely, including outside of your university setting?
Will you have the means to recruit a diverse sample that represents a broad population?
Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

Organizing data from each variable in frequency distribution tables .
Displaying data from a key variable in a bar chart to view the distribution of responses.
Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

Mode : the most popular response or value in the data set.
Median : the value in the exact middle of the data set when ordered from low to high.
Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

Range : the highest value minus the lowest value of the data set.
Interquartile range : the range of the middle half of the data set.
Standard deviation : the average distance between each value in your data set and the mean.
Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

	Pretest scores	Posttest scores
Mean	68.44	75.25
Standard deviation	9.43	9.88
Variance	88.96	97.96
Range	36.25	45.12
	30

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

	Parental income (USD)	GPA
Mean	62,100	3.12
Standard deviation	15,000	0.45
Variance	225,000,000	0.16
Range	8,000–378,000	2.64–4.00
	653

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

Estimation: calculating population parameters based on sample statistics.
Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

A point estimate : a value that represents your best guess of the exact parameter.
An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

A test statistic tells you how much your data differs from the null hypothesis of the test.
A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

Comparison tests assess group differences in outcomes.
Regression tests assess cause-and-effect relationships between variables.
Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

A simple linear regression includes one predictor variable and one outcome variable.
A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
A z test is for exactly 1 or 2 groups when the sample is large.
An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

If you have only one sample that you want to compare to a population mean, use a one-sample test .
If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
If you expect a difference between groups in a specific direction, use a one-tailed test .
If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

a t value (test statistic) of 3.00
a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

a t value of 3.08
a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval

Methodology

Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Likert scale

Research bias

Implicit bias
Framing effect
Cognitive bias
Placebo effect
Hawthorne effect
Hostile attribution bias
Affect heuristic

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2023
Title	Author	Supervisor
Statistical Methods for the Analysis and Prediction of Hierarchical Time Series Data with Applications to Demography
Exponential Family Models for Rich Preference Ranking Data
Bayesian methods for variable selection		,
Statistical methods for genomic sequencing data
Inference and Estimation for Network Data
Mixture models to fit heavy-tailed, heterogeneous or sparse data		,
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Estimating subnational health and demographic indicators using complex survey data
Interpretation and Validation for unsupervised learning

2022
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Likelihood-based haplotype frequency modeling using variable-order Markov chains
Statistical Divergences for Learning and Inference: Limit Laws and Non-Asymptotic Bounds		,
Causal Structure Learning in High Dimensions		,
Missing Data Methods for Observational Health Dataset
Methods, Models, and Interpretations for Spatial-Temporal Public Health Applications
Statistical Methods for Clustering and High Dimensional Time Series Analysis
Geometric algorithms for interpretable manifold learning

2021
Title	Author	Supervisor
Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest		,
Statistical modeling of long memory and uncontrolled effects in neural recordings
Distribution-free consistent tests of independence via marginal and multivariate ranks
Causality, Fairness, and Information in Peer Review		,
Subnational Estimation of Period Child Mortality in a Low and Middle Income Countries Context
Progress in nonparametric minimax estimation and high dimensional hypothesis testing		,
Likelihood Analysis of Causal Models
Bayesian Models in Population Projections and Climate Change Forecast

2020
Title	Author	Supervisor
Statistical Methods for Adaptive Immune Receptor Repertoire Analysis and Comparison
Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data
Representation Learning for Partitioning Problems
Estimation and Inference in Changepoint Models
Space-Time Contour Models for Sea Ice Forecasting		,
Non-Gaussian Graphical Models: Estimation with Score Matching and Causal Discovery under Zero-Inflation		,
Scalable Learning in Latent State Sequence Models

2019
Title	Author	Supervisor
Latent Variable Models for Prediction & Inference with Proxy Network Measures
Bayesian Hierarchical Models and Moment Bounds for High-Dimensional Time Series		,
Estimation and testing under shape constraints		,
Inferring network structure from partially observed graphs
Fitting Stochastics Epidemic Models to Multiple Data Types
Realized genome sharing in random effects models for quantitative genetic traits
Large-Scale B Cell Receptor Sequence Analysis Using Phylogenetics and Machine Learning
Statistical Methods for Manifold Recovery and C^ (1, 1) Regression on Manifolds

2018
Title	Author	Supervisor
Topics in Statistics and Convex Geometry: Rounding, Sampling, and Interpolation
Estimation and Testing Following Model Selection
Topics on Least Squares Estimation
Discovering Interaction in Multivariate Time Series
Nonparametric inference on monotone functions, with applications to observational studies
Bayesian Methods for Graphical Models with Limited Data
Model-Based Penalized Regression
Parameter Identification and Assessment of Independence in Multivariate Statistical Modeling
Preferential sampling and model checking in phylodynamic inference
Linear Structural Equation Models with Non-Gaussian Errors: Estimation and Discovery
Coevolution Regression and Composite Likelihood Estimation for Social Networks

2017
Title	Author	Supervisor
"Scalable Methods for the Inference of Identity by Descent"
"Applications of Robust Statistical Methods in Quantitative Finance"
"Scalable Manifold Learning and Related Topics"
"Topics in Graph Clustering"
"Methods for Estimation and Inference for High-Dimensional Models"		,

2016
Title	Author	Supervisor
"Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling"
"Space-Time Smoothing Models for Surveillance and Complex Survey Data"
"Testing Independence in High Dimensions & Identifiability of Graphical Models"
"Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes"
"Bayesian Methods for Inferring Gene Regulatory Networks"		,
"Finite Sampling Exponential Bounds"
"Finite Population Inference for Causal Parameters"
"Projection and Estimation of International Migration"

2015
Title	Author	Supervisor
"Theory and Methods for Tensor Data"
"Discrete-Time Threshold Regression for Survival Data with Time-Dependent Covariates"
"Degeneracy, Duration, and Co-Evolution: Extending Exponential Random Graph Models (ERGM) for Social Network Analysis"
"The Likelihood Pivot: Performing Inference with Confidence"
"Lord's Paradox and Targeted Interventions: The Case of Special Education"		,
"Bayesian Modeling of a High Resolution Housing Price Index"
"Phylogenetic Stochastic Mapping"

2014
Title	Author	Supervisor
"R-Squared Inference Under Non-Normal Error"
"Monte Carlo Estimation of Identity by Descent in Populations"
"Bayesian Spatial and Temporal Methods for Public Health Data"		,
"Functional Quantitative Genetics and the Missing Heritability Problem"
"Predictive Modeling of Cholera Outbreaks in Bangladesh"		,
"Gravimetric Anomaly Detection Using Compressed Sensing"

2013
Title	Author	Supervisor
"Statistical Inference Using Kronecker Structured Covariance"
"Learning and Manifolds: Leveraging the Intrinsic Geometry"
"An Algorithmic Framework for High Dimensional Regression with Dependent Variables"
"Bayesian Population Reconstruction: A Method for Estimating Age- and Sex-Specific Vital Rates and Population Counts with Uncertainty from Fragmentary Data"
"Bayesian Nonparametric Inference of Effective Population Size Trajectories from Genomic Data"
"Modeling Heterogeneity Within and Between Matrices and Arrays"
"Shape-Constrained Inference for Concave-Transformed Densities and their Modes"

2012
Title	Author	Supervisor
"Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data"
"Tests for Differences between Least Squares and Robust Regression Parameter Estimates and Related To Pics"
"Bayesian Modeling of Health Data in Space and Time"
"Coordinate-Free Exponential Families on Contingency Tables"		,

2011
Title	Author	Supervisor
"Statistical Approaches to Analyze Mass Spectrometry Data Graduating Year"		,
"Seeing the trees through the forest; a competition model for growth and mortality"
"Bayesian Inference of Exponential-family Random Graph Models for Social Networks"
"Statistical Models for Estimating and Predicting HIV/AIDS Epidemics"
"Modeling the Game of Soccer Using Potential Functions"
"Parametrizations of Discrete Graphical Models"
"A Bayesian Surveillance System for Detecting Clusters of Non-Infectious Diseases"

2010
Title	Author	Supervisor
"Convex analysis methods in shape constrained estimation."
"Estimating social contact networks to improve epidemic simulation models"
"Multivariate Geostatistics and Geostatistical Model Averaging"
"Covariance estimation in the Presence of Diverse Types of Data"
"Portfolio Optimization with Tail Risk Measures and Non-Normal Returns"

2009
Title	Author	Supervisor
"Statistical Models for Social Network Data and Processes"
"Models for Heterogeneity in Heterosexual Partnership Networks"
"A comparison of alternative methodologies for estimation of HIV incidence"
"Bayesian Model Averaging and Multivariate Conditional Independence Structures"
"Conditional tests for localizing trait genes"
"Combining and Evaluating Probabilistic Forecasts"
"Probabilistic weather forecasting using Bayesian model averaging"
"Statistical Analysis of Portfolio Risk and Performance Measures: the Influence Function Approach"
"Factor Model Monte Carlo Methods for General Fund-of-Funds Portfolio Management"

2008
Title	Author	Supervisor
"Statistical methods for peptide and protein identification using mass spectrometry"
"Inference from partially-observed network data"
"Models and Inference of Transmission of DNA Methylation Patterns in Mammalian Somatic Cells"
"Estimates and projections of the total fertility rate"
"Nonparametric estimation of multivariate monotone densities"
"Learning transcriptional regulatory networks from the integration of heterogeneous high-throughout data"
"Extensions of Latent Class Transition Models with Application to Chronic Disability Survey Data"
"Statistical Solutions to Some Problems in Medical Imaging"		,

2007
Title	Author	Supervisor
""Up-and-Down" and the Percentile-Finding Problem"
"Statistical Methodology for Longitudinal Social Network Data"
"Probabilistic weather forecasting with spatial dependence"
"Wavelet variance analysis for time series and random fields"		,
"Bayesian hierarchical curve registration"

2006
Title	Author	Supervisor
"Goodness-of-fit statistics based on phi-divergences"
"An efficient and flexible model for patterns of population genetic variation"
"Learning in Spectral Clustering"
"Variable selection and other extensions of the mixture model clustering framework"
"Algorithms for Estimating the Cluster Tree of a Density"
"Likelihood inference for population structure, using the coalescent"
"Exploring rates and patterns of variability in gene conversion and crossover in the human genome"
"Alleviating ecological bias in generalized linear models and optimal design with subsample data"		,
"Nonparametric estimation for current status data with competing risks"		,

2005
Title	Author	Supervisor
"Bayesian robust analysis of gene expression microarray data"
"Alternative models for estimating genetic maps from pedigree data"
"Allele-sharing methods for linkage detection using extended pedigrees"
"Robust estimation of factor models in finance"
"Using the structure of d-connecting paths as a qualitative measure of the strength of dependence"		,
"Alternative estimators of wavelet variance"		, ,

2004
Title	Author	Supervisor
"Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models"		,
"Nonparametric estimation of a k-monotone density: A new asymptotic distribution theory"

2003
Title	Author	Supervisor
"The genetic structure of related recombinant lines"
"Joint relationship inference from three or more individuals in the presence of genotyping error"
"Personal characteristics and covariate measurement error in disease risk estimation"		,
"Model based and hybrid clustering of large datasets"		,

2002
Title	Author	Supervisor
"Generalized linear mixed models: development and comparison of different estimation methods"
"Practical importance sampling methods for finite mixture models and multiple imputation"
"Applying graphical models to partially observed data-generating processes"		,

2001
Title	Author	Supervisor
"Bayesian inference for deterministic simulation models for environmental assessment"
"Modeling recessive lethals: An explanation for excess sharing in siblings"
"Estimation with bivariate interval censored data"
"Latent models for cross-covariance"		,

2000
Title	Author	Supervisor
"A model selection approach to partially linear regression"
"Wavelet-based estimation for trend contaminated long memory processes"		,
"Global covariance modeling: A deformation approach to anisotropy"
"Likelihood inference for parameteric models of dispersal"
"Bayesian inference in hidden stochastic population processes"
"Logic regression and statistical issues related to the protein folding problem"		,
"Likelihood ratio inference in regular and non-regular problems"
"Estimating the association between airborne particulate matter and elderly mortality in Seattle, Washington using Bayesian Model Averaging"		,
"Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts"		,
"Detecting and extracting complex patterns from images and realizations of spatial point processes"

1999
Title	Author	Supervisor
"Statistical approaches to distinct value estimation"		,
"Generalization of boosting algorithms and applications of Bayesian inference for massive datasets"		,
"Bayesian inference for noninvertible deterministic simulation models, with application to bowhead whale assessment"
"Monte Carlo likelihood calculation for identity by descent data"
"Fast automatic unsupervised image segmentation and curve detection in spatial point processes"
"Semiparametric inference based on estimating equations in regressions models for two phase outcome dependent sampling"		,
"Capture-recapture estimation of bowhead whale population size using photo-identification data"		,
"Lifetime and disease onset distributions from incomplete observations"

1998
Title	Author	Supervisor
"Additive mixture models for multichannel image data"
"Application of ridge regression for improved estimation of parameters in compartmental models"
"Bayesian modeling of highly structured systems using Markov chain Monte Carlo"
"Assessing nonstationary time series using wavelets"		,
"Lattice conditional independence models for incomplete multivariate data and for seemingly unrelated regressions"		,
"Estimation for counting processes with incomplete data"
"Regularization techniques for linear regression with a large set of carriers"
"Large sample theory for pseudo maximum likelihood estimates in semiparametric models"

1997
Title	Author	Supervisor
"A new learning procedure in acyclic directed graphs"
"Phylogenies via conditional independence modeling"
"Bayesian model averaging in censored survival models"
"Bayesian information retrieval"
"Statistical inference for partially observed markov population processes"
"Tools for the advancement of undergraduate statistics education"

1996
Title	Author	Supervisor
"Bootstrapping functional m-estimators"
"Variability estimation in linear inverse problems"
"Inference in a discrete parameter space"

1995
Title	Author	Supervisor
"Estimation of heterogeneous space-time covariance"
"Semiparametric estimation of major gene and random environmental effects for age of onset"
"Statistical analysis of biological monitoring data: State-space models for species compositions"

1994
Title	Author	Supervisor
"Estimation in regression models with interval censoring"
"Spatial applications of Markov chain Monte Carlo for bayesian inference"
"Accounting for model uncertainty in linear regression"
"Robust estimation in point processes"
"Multilevel modeling of discrete event history data using Markov chain Monte Carlo methods"

1993
Title	Author	Supervisor
"A Bayesian framework and importance sampling methods for synthesizing multiple sources of evidence and uncertainty linked by a complex mechanistic model"
"State-space modeling of salmon migration and Monte Carlo Alternatives to the Kalman filter"
"The Poisson clumping heuristic and the survival of genome in small pedigrees"
"Markov chain Monte Carlo estimates of probabilities on complex structures"
"A class of stochastic models for relating synoptic atmospheric patterns to local hydrologic phenomena"

1992
Title	Author	Supervisor
"Bayesian methods for the analysis of misclassified or incomplete multivariate discrete data"
"Auxiliary and missing covariate problems in failure time regression analysis"
"A high order hidden markov model"

1991
Title	Author	Supervisor
"General-weights bootstrap of the empirical process"
"The weighted likelihood bootstrap and an algorithm for prepivoting"

1990
Title	Author	Supervisor
"Modelling agricultural field trials in the presence of outliers and fertility jumps"
"Modeling and bootstrapping for non-gaussian time series"
"Genetic restoration on complex pedigrees"
"Incorporating covariates into a beta-binomial model with applications to medicare policy: A Bayes/empirical Bayes approach"
"Likelihood and exponential families"

1989
Title	Author	Supervisor
"Estimation of mixing and mixed distributions"
"Classical inference in spatial statistics"

1988
Title	Author	Supervisor
"Exploratory methods for censored data"
"Aspects of robust analysis in designed experiments"
"Diagnostics for time series models"
"Constrained cluster analysis and image understanding"

1987
Title	Author	Supervisor
"Kullback-Leibler estimation of probability measures with an application to clustering"
"Time series models for continuous proportions"
"The data viewer: A program for graphical data analysis"
"Additive principal components: A method for estimating additive constraints with small variance from multivariate data"

1986
Title	Author	Supervisor
"Estimation for infinite variance autoregressive processes"
"A computer system for Monte Carlo experimentation"

1985
Title	Author	Supervisor
"Robust estimation for the errors-in-variables model"
"Robust statistics on compact metric spaces"
"Weak convergence and a law of the iterated logarithm for processes indexed by points in a metric space"

1983
Title	Author	Supervisor
"The statistics of long memory processes"

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What do senior theses in Statistics look like?

This is a brief overview of thesis writing; for more information, please see our website here . Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors:

1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research extends or relates to previous related work.

2. An analysis of a complex data set that advances understanding in a related field, such as public health, economics, government, or genetics. Such a thesis may rely entirely on existing methods, but should give useful results and insights into an interesting applied problem.

3. An analysis of a complex data set in which new methods or modifications of published methods are required. While the thesis does not necessarily contain an extensive mathematical study of the new methods, it should contain strong plausibility arguments or simulations supporting the use of the new methods.

A good thesis is clear, readable, and well-motivated, justifying the applicability of the methods used rather than, for example, mechanically running regressions without discussing the assumptions (and whether they are plausible), performing diagnostics, and checking whether the conclusions make sense.

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School of Mathematics & Statistics

Postgraduate research study
Statistics Thesis Topics
About the School
Postgraduate Research Courses
Mathematics Thesis Topics

Statistics thesis topics

Below are sample topics available for prospective postgraduate research students. These sample topics do not contain every possible project; they are aimed at giving an impression of the breadth of different topics available. Most prospective supervisors would be more than happy to discuss projects not listed below.

Funded projects are projects with project-specific funding. Funding for other projects is usally available on a competitive basis.

Modelling in Space and Time - Example Research Projects

Information about postgraduate research opportunities and how to apply can be found on the Postgraduate Research Study page . Below is a selection of projects that could be undertaken with our group.

Evaluating probabilistic forecasts in high-dimensional settings (PhD)

Supervisors: Jethro Browell Relevant research groups: Modelling in Space and Time , Computational Statistics , Applied Probability and Stochastic Processes

Many decisions are informed by forecasts, and almost all forecasts are uncertain to some degree. Probabilistic forecasts quantify uncertainty to help improve decision-making and are playing an important role in fields including weather forecasting, economics, energy, and public policy. Evaluating the quality of past forecasts is essential to give forecasters and forecast users confidence in their current predictions, and to compare the performance of forecasting systems.

While the principles of probabilistic forecast evaluation have been established over the past 15 years, most notably that of “ sharpness subject to calibration/reliability” , we lack a complete toolkit for applying these principles in many situations, especially those that arise in high-dimensional settings. Furthermore, forecast evaluation must be interpretable by forecast users as well as expert forecasts, and assigning value to marginal improvements in forecast quality remains a challenge in many sectors.

This PhD will develop new statistical methods for probabilistic forecast evaluation considering some of the following issues:

Verifying probabilistic calibration conditional on relevant covariates
Skill scores for multivariate probabilistic forecasts where “ideal” performance is unknowable
Assigning value to marginal forecast improvement though the convolution of utility functions and Murphey Diagrams
Development of the concept of “anticipated verification” and “predicting the of uncertainty of future forecasts”
Decomposing forecast misspecification (e.g. into spatial and temporal components)
Evaluation of Conformal Predictions

Good knowledge of multivariate statistics is essential, prior knowledge of probabilistic forecasting and forecast evaluation would be an advantage.

Adaptive probabilistic forecasting (PhD)

Supervisors: Jethro Browell Relevant research groups: Modelling in Space and Time , Computational Statistics , Applied Probability and Stochastic Processes

Data-driven predictive models depend on the representativeness of data used in model selection and estimation. However, many processes change over time meaning that recent data is more representative than old data. In this situation, predictive models should track these changes, which is the aim of “online” or “adaptive” algorithms. Furthermore, many users of forecasts require probabilistic forecasts, which quantify uncertainty, to inform their decision-making. Existing adaptive methods such as Recursive Least Squares, the Kalman Filter have been very successful for adaptive point forecasting, but adaptive probabilistic forecasting has received little attention. This PhD will develop methods for adaptive probabilistic forecasting from a theoretical perspective and with a view to apply these methods to problems in at least one application area to be determined.

In the context of adaptive probabilistic forecasting, this PhD may consider:

Online estimation of Generalised Additive Models for Location Scale and Shape
Online/adaptive (multivariate) time series prediction
Online aggregation (of experts, or hierarchies)

A good knowledge of methods for time series analysis and regression is essential, familiarity with flexible regression (GAMs) and distributional regression (GAMLSS/quantile regression) would be an advantage.

The evolution of shape (PhD)

Supervisors: Vincent Macaulay Relevant research groups: Bayesian Modelling and Inference , Modelling in Space and Time , Statistical Modelling for Biology, Genetics and *omics

Shapes of objects change in time. Organisms evolve and in the process change form: humans and chimpanzees derive from some common ancestor presumably different from either in shape. Designed objects are no different: an Art Deco tea pot from the 1920s might share some features with one from Ikea in 2010, but they are different. Mathematical models of evolution for certain data types, like the strings of As, Gs , Cs and Ts in our evolving DNA, are quite mature and allow us to learn about the relationships of the objects (their phylogeny or family tree), about the changes that happen to them in time (the evolutionary process) and about the ways objects were configured in the past (the ancestral states), by statistical techniques like phylogenetic analysis. Such techniques for shape data are still in their infancy. This project will develop novel statistical inference approaches (in a Bayesian context) for complex data objects, like functions, surfaces and shapes, using Gaussian-process models, with potential application in fields as diverse as language evolution, morphometrics and industrial design.

New methods for analysis of migratory navigation (PhD)

Supervisors: Janine Illian Relevant research groups: Modelling in Space and Time , Bayesian Modelling and Inference , Computational Statistics , Environmental, Ecological Sciences and Sustainability

Joint project with Dr Urška Demšar (University of St Andrews)

Migratory birds travel annually across vast expanses of oceans and continents to reach their destination with incredible accuracy. How they are able to do this using only locally available cues is still not fully understood. Migratory navigation consists of two processes: birds either identify the direction in which to fly (compass orientation) or the location where they are at a specific moment in time (geographic positioning). One of the possible ways they do this is to use information from the Earth’s magnetic field in the so-called geomagnetic navigation (Mouritsen 2018). While there is substantial evidence (both physiological and behavioural) that they do sense magnetic field (Deutschlander and Beason 2014), we however still do not know exactly which of the components of the field they use for orientation or positioning. We also do not understand how rapid changes in the field affect movement behaviour.

There is a possibility that birds can sense these rapid large changes and that this may affect their navigational process. To study this, we need to link accurate data on Earth’s magnetic field with animal tracking data. This has only become possible very recently through new spatial data science advances: we developed the MagGeo tool, which links contemporaneous geomagnetic data from Swarm satellites of the European Space Agency with animal tracking data (Benitez Paez et al. 2021).

Linking geomagnetic data to animal tracking data however creates a highly-dimensional data set, which is difficult to explore. Typical analyses of contextual environmental information in ecology include representing contextual variables as co-variates in relatively simple statistical models (Brum Bastos et al. 2021), but this is not sufficient for studying detailed navigational behaviour. This project will analyse complex spatio-temporal data using computationally efficient statistical model fitting approches in a Bayesian context.

This project is fully based on open data to support reproducibility and open science. We will test our new methods by annotating publicly available bird tracking data (e.g. from repositories such as Movebank.org), using the open MagGeo tool and implementing our new methods as Free and Open Source Software (R/Python).

Benitez Paez F, Brum Bastos VdS, Beggan CD, Long JA and Demšar U, 2021. Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration. Movement Ecology , 9:31. https://doi.org/10.1186/s40462-021-00268-4

Brum Bastos VdS, Łos M, Long JA, Nelson T and Demšar U, 2021, Context-aware movement analysis in ecology: a systematic review. International Journal of Geographic Information Science , https://doi.org/10.1080/13658816.2021.1962528

Deutschlander ME and Beason RC, 2014. Avian navigation and geographic positioning. Journal of Field Ornithology , 85(2):111–133. https://doi.org/10.1111/jofo.12055

Integrated spatio-temporal modelling for environmental data (PhD)

Supervisors: Janine Illian Relevant research groups: Modelling in Space and Time , Bayesian Modelling and Inference , Computational Statistics , Environmental, Ecological Sciences and Sustainability

(Jointly supervised by Peter Henrys, CEH)

The last decade has seen a proliferation of environmental data with vast quantities of information available from various sources. This has been due to a number of different factors including: the advent of sensor technologies; the provision of remotely sensed data from both drones and satellites; and the explosion in citizen science initiatives. These data represent a step change in the resolution of available data across space and time - sensors can be streaming data at a resolution of seconds whereas citizen science observations can be in the hundreds of thousands.

Over the same period, the resources available for traditional field surveys have decreased dramatically whilst logistical issues (such as access to sites, ) have increased. This has severely impacted the ability for field survey campaigns to collect data at high spatial and temporal resolutions. It is exactly this sort of information that is required to fit models that can quantify and predict the spread of invasive species, for example.

Whilst we have seen an explosion of data across various sources, there is no single source that provides both the spatial and temporal intensity that may be required when fitting complex spatio-temporal models (cf invasive species example) - each has its own advantages and benefits in terms of information content. There is therefore potentially huge benefit in beginning together data from these different sources within a consistent framework to exploit the benefits each offers and to understand processes at unprecedented resolutions/scales that would be impossible to monitor.

Current approaches to combining data in this way are typically very bespoke and involve complex model structures that are not reusable outside of the particular application area. What is needed is an overarching generic methodological framework and associated software solutions to implement such analyses. Not only would such a framework provide the methodological basis to enable researchers to benefit from this big data revolution, but also the capability to change such analyses from being stand alone research projects in their own right, to more operational, standard analytical routines.

FInally, such dynamic, integrated analyses could feedback into data collection initiatives to ensure optimal allocation of effort for traditional surveys or optimal power management for sensor networks. The major step change being that this optimal allocation of effort is conditional on other data that is available. So, for example, given the coverage and intensity of the citizen science data, where should we optimally send our paid surveyors? The idea is that information is collected at times and locations that provide the greatest benefit in understanding the underpinning stochastic processes. These two major issues - integrated analyses and adaptive sampling - ensure that environmental monitoring is fit for purpose and scientists, policy and industry can benefit from the big data revolution.

This project will develop an integrated statistical modelling strategy that provides a single modelling framework for enabling quantification of ecosystem goods and services while accounting for the fundamental differences in different data streams. Data collected at different spatial resolutions can be used within the same model through projecting it into continuous space and projecting it back into the landscape level of interest. As a result, decisions can be made at the relevant spatial scale and uncertainty is propagated through, facilitating appropriate decision making.

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (PhD)

(jointly supervised by Esther Jones and Adam Butler, BIOSS)

Assessing the impacts of offshore renewable developments on marine wildlife is a critical component of the consenting process. A NERC-funded project, ECOWINGS, will provide a step-change in analysing predator-prey dynamics in the marine environment, collecting data across trophic levels against a backdrop of developing wind farms and climate change. Aerial survey and GPS data from multiple species of seabirds will be collected contemporaneously alongside prey data available over the whole water column from an automated surface vehicle and underwater drone.

These methods of data collection will generate 3D space and time profiles of predators and prey, creating a rich source of information and enormous potential for modelling and interrogation. The data present a unique opportunity for experimental design across a dynamic and changing marine ecosystem, which is heavily influenced by local and global anthropogenic activities. However, these data have complex intrinsic spatio-temporal properties, which are challenging to analyse. Significant statistical methods development could be achieved using this system as a case study, contributing to the scientific knowledge base not only in offshore renewables but more generally in the many circumstances where patchy ecological spatio-temporal data are available.

This PhD project will develop spatio-temporal modelling methodology that will allow user to anaylse these exciting - and complex - data sets and help inform our knowledge on the impact of off-shore renewable on wildlife.

Analysis of spatially correlated functional data objects (PhD)

Supervisors: Surajit Ray Relevant research groups: Modelling in Space and Time , Computational Statistics , Nonparametric and Semi-parametric Statistics , Imaging, Image Processing and Image Analysis

Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:

robust statistical models for incorporating temporal and spatial dependencies in functional data
developing reliable prediction and interpolation techniques for dependent functional data
developing inferential framework for testing hypotheses related to simplified dependent structures
analysing sparsely observed functional data by borrowing information from neighbours
visualisation of data summaries associated with dependent functional data
Clustering of functional data

Estimating the effects of air pollution on human health (PhD)

Supervisors: Duncan Lee Relevant research groups: Modelling in Space and Time , Biostatistics, Epidemiology and Health Applications

The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to extended areas such as cities or electoral wards, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.

Mapping disease risk in space and time (PhD)

Disease risk varies over space and time, due to similar variation in environmental exposures such as air pollution and risk inducing behaviours such as smoking. Modelling the spatio-temporal pattern in disease risk is known as disease mapping, and the aims are to: quantify the spatial pattern in disease risk to determine the extent of health inequalities, determine whether there has been any increase or reduction in the risk over time, identify the locations of clusters of areas at elevated risk, and quantify the impact of exposures, such as air pollution, on disease risk. I am working on all these related problems at present, and I have PhD projects in all these areas.

Bayesian Mixture Models for Spatio-Temporal Data (PhD)

Supervisors: Craig Anderson Relevant research groups: Modelling in Space and Time , Bayesian Modelling and Inference , Biostatistics, Epidemiology and Health Applications

The prevalence of disease is typically not constant across space – instead the risk tends to vary from one region to another. Some of this variability may be down to environmental conditions, but many of them are driven by socio-economic differences between regions, with poorer regions tending to have worse health than wealthier regions. For example, within the the Greater Glasgow and Clyde region, where the World Health Organisation noted that life expectancy ranges from 54 in Calton to 82 in Lenzie, despite these areas being less than 10 miles apart. There is substantial value to health professionals and policymakers in identifying some of the causes behind these localised health inequalities.

Disease mapping is a field of statistical epidemiology which focuses on estimating the patterns of disease risk across a geographical region. The main goal of such mapping is typically to identify regions of high disease risk so that relevant public health interventions can be made. This project involves the development of statistical models which will enhance our understanding regional differences in the risk of suffering from major diseases by focusing on these localised health inequalities.

Standard Bayesian hierarchical models with a conditional autoregressive prior are frequently used for risk estimation in this context, but these models assume a smooth risk surface which is often not appropriate in practice. In reality, it will often be the case that different regions have vastly different risk profiles and require different data generating functions as a result.

In this work we propose a mixture model based approach which allows different sub-populations to be represented by different underlying statistical distributions within a single modelling framework. By integrating CAR models into mixture models, researchers can simultaneously account for spatial dependencies and identify distinct disease patterns within subpopulations.

Bayesian Modelling and Inference - Example Research Projects

Modelling genetic variation (msc/phd).

Supervisors: Vincent Macaulay Relevant research groups: Bayesian Modelling and Inference , Statistical Modelling for Biology, Genetics and *omics

Variation in the distribution of different DNA sequences across individuals has been shaped by many processes which can be modelled probabilistically, processes such as demographic factors like prehistoric population movements, or natural selection. This project involves developing new techniques for teasing out information on those processes from the wealth of raw data that is now being generated by high-throughput genetic assays, and is likely to involve computationally-intensive sampling techniques to approximate the posterior distribution of parameters of interest. The characterization of the amount of population structure on different geographical scales will influence the design of experiments to identify the genetic variants that increase risk of complex diseases, such as diabetes or heart disease.

The evolution of shape (PhD)

Supervisors: Vincent Macaulay Relevant research groups: Bayesian Modelling and Inference , Modelling in Space and Time , Statistical Modelling for Biology, Genetics and *omics

New methods for analysis of migratory navigation (PhD)

Integrated spatio-temporal modelling for environmental data (phd), statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (phd).

Bayesian variable selection for genetic and genomic studies (PhD)

Supervisors: Mayetri Gupta Relevant research groups: Bayesian Modelling and Inference , Computational Statistics , Statistical Modelling for Biology, Genetics and *omics

An important issue in high-dimensional regression problems is the accurate and efficient estimation of models when, compared to the number of data points, a substantially larger number of potential predictors are present. Further complications arise with correlated predictors, leading to the breakdown of standard statistical models for inference; and the uncertain definition of the outcome variable, which is often a varying composition of several different observable traits. Examples of such problems arise in many scenarios in genomics- in determining expression patterns of genes that may be responsible for a type of cancer; and in determining which genetic mutations lead to higher risks for occurrence of a disease. This project involves developing broad and improved Bayesian methodologies for efficient inference in high-dimensional regression-type problems with complex multivariate outcomes, with a focus on genetic data applications.

The successful candidate should have a strong background in methodological and applied Statistics, expert skills in relevant statistical software or programming languages (such as R, C/C++/Python), and also have a deep interest in developing knowledge in cross-disciplinary topics in genomics. The candidate will be expected to consolidate and master an extensive range of topics in modern Statistical theory and applications during their PhD, including advanced Bayesian modelling and computation, latent variable models, machine learning, and methods for Big Data. The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate for four years.

Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis (PhD)

Supervisors: Mayetri Gupta Relevant research groups: Bayesian Modelling and Inference , Computational Statistics , Statistical Modelling for Biology, Genetics and *omics , Biostatistics, Epidemiology and Health Applications

In recent years, many different computational methods to analyse biological data have been established: including DNA (Genomics), RNA (Transcriptomics), Proteins (proteomics) and Metabolomics, that captures more dynamic events. These methods were refined by the advent of single cell technology, where it is now possible to capture the transcriptomics profile of single cells, spatial arrangements of cells from flow methods or imaging methods like functional magnetic resonance imaging. At the same time, these OMICS data can be complemented with clinical data – measurement of patients, like age, smoking status, phenotype of disease or drug treatment. It is an interesting and important open statistical question how to combine data from different “modalities” (like transcriptome with clinical data or imaging data) in a statistically valid way, to compare different datasets and make justifiable statistical inferences. This PhD project will be jointly supervised with Dr. Thomas Otto and Prof. Stefan Siebert from the Institute of Infection, Immunity & Inflammation ), you will explore how to combine different datasets using Bayesian latent variable modelling, focusing on clinical datasets from Rheumatoid Arthritis.

Funding Notes

The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate for four years.

Scalable Bayesian models for inferring evolutionary traits of plants (PhD)

Supervisors: Vinny Davies , Richard Reeve Relevant research groups: Bayesian Modelling and Inference , Computational Statistics , Environmental, Ecological Sciences and Sustainability , Statistical Modelling for Biology, Genetics and *omics

The functional traits and environmental preferences of plant species determine how they will react to changes resulting from global warming. The main global biodiversity repositories, such as the Global Biodiversity Information Facility ( GBIF ), contain hundreds of millions of records from hundreds of thousands of species in the plant kingdom alone, and the spatiotemporal data in these records can be associated with soil, climate or other environmental data from other databases. Combining these records allow us to identify environmental preferences, especially for common species where many records exist. Furthermore, in a previous PhD studentship we showed that these traits are highly evolutionarily conserved ( Harris et al., 2022 ), so it is possible to impute the preferences for rare species where little data exists using phylogenetic inference techniques.

The aim of this PhD project is to investigate the application of Bayesian variable selection methods to identify these evolutionarily conserved traits more effectively, and to quantify these traits and their associated uncertainty for all plant species for use in a plant ecosystem digital twin that we are developing separately to forecast the impact of climate change on biodiversity. In another PhD studentship, we previously developed similar methods for trait inference in viral evolution ( Davies et al., 2017 ; Davies et al., 2019 ), but due to the scale of the data here, these methods will need to be significantly enhanced. We therefore propose a project to investigate extensions to methods for phylogenetic trait inference to handle datasets involving hundreds of millions of records in phylogenies with hundreds of thousands of tips, potentially through either sub-sampling ( Quiroz et al, 2018 ) or modelling splitting and recombination ( Nemeth & Sherlock, 2018 ).

Computational Statistics - Example Research Projects

Supervisors: Jethro Browell Relevant research groups: Modelling in Space and Time , Computational Statistics , Applied Probability and Stochastic Processes

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (PhD)

Bayesian variable selection for genetic and genomic studies (phd), bayesian statistical data integration of single-cell and bulk “omics” datasets with clinical parameters for accurate prediction of treatment outcomes in rheumatoid arthritis (phd), scalable bayesian models for inferring evolutionary traits of plants (phd).

Multi objective Bayesian optimisation for in silico to real metabolomics experiments (PhD/MSc)

Supervisors: Vinny Davies , Craig Alexander Relevant research groups: Computational Statistics , Machine Learning and AI , Emulation and Uncertainty Quantification , Statistical Modelling for Biology, Genetics and *omics , Statistics in Chemistry/Physics

Untargeted metabolomics experiments aim to identify the small molecules that make up a particular sample (e.g. , blood), allowing us to identify biomarkers, discover new chemicals, or understand the metabolism ( Smith et al., 2014 ) . Data Dependent Acquisition (DDA) methods are used to collect the information needed to identify the metabolites , and various more advanced DDA methods have recently been designed to improve this process ( Davies et al. (2021) ; McBride et al. (2023) ) . Each of these methods , however, ha s parameters that must be chosen in order to maximise the amount of relevant data (metabolite spectra) that is collected . Our recent work led to the design of a Virtual Metabolomics Mass Spectrometer ( ViMMS ) in which we can run computer simulations of experiments and test different parameter settings ( Wandy et al., 2019 , 2022 ). Previously this has involve d running a pre-determined set of parameters as part of a grid search in ViMMS , and then choosing the best parameter settings based on a single measure of performance. The proposed M . Res . (or Ph . D . ) will extend this appro ach by using multi objective Bayesian Optimisation to adapt simulations and optimise over multiple different measurements of quality . By optimising parameters in this manner, we can help improve real experiments currently underway at the University of Glasgow and beyond.

Analysis of spatially correlated functional data objects (PhD)

Nonparametric and semi-parametric statistics - example research projects, modality of mixtures of distributions (phd).

Supervisors: Surajit Ray Relevant research groups: Nonparametric and Semi-parametric Statistics , Applied Probability and Stochastic Processes , Statistical Modelling for Biology, Genetics and *omics , Biostatistics, Epidemiology and Health Applications

Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems.

Applied Probability and Stochastic Processes - Example Research Projects

Modality of mixtures of distributions (phd).

Machine Learning and AI - Example Research Projects

Estimating false discovery rates in metabolite identification using generative ai (phd).

Supervisors: Vinny Davies , Andrew Elliott , Justin J.J. van der Hooft (Wageningen University) Relevant research groups: Machine Learning and AI , Emulation and Uncertainty Quantification , Statistical Modelling for Biology, Genetics and *omics , Statistics in Chemistry/Physics

Metabolomics is the study field that aims to map all molecules that are part of an organism, which can help us understand its metabolism and how it can be affected by disease, stress, age, or other factors. During metabolomics experiments, mass spectra of the metabolites are collected and then annotated by comparison against spectral databases such as METLIN ( Smith et al., 2005 ) or GNPS ( Wang et al., 2016 ). Generally, however, these spectral databases do not contain the mass spectra of a large proportion of metabolites, so the best matching spectrum from the database is not always the correct identification. Matches can be scored using cosine similarity, or more advanced methods such as Spec2Vec ( Huber et al., 2021 ), but these scores do not provide any statement about the statistical accuracy of the match. Creating decoy spectral libraries, specifically a large database of fake spectra, is one potential way of estimating False Discovery Rates (FDRs), allowing us to quantify the probability of a spectrum match being correct ( Scheubert et al., 2017 ). However, these methods are not widely used, suggesting there is significant scope to improve their performance and ease of use. In this project, we will use the code framework from our recently developed Virtual Metabolomics Mass Spectrometer (ViMMS) ( Wandy et al., 2019 , 2022 ) to systematically evaluate existing methods and identify possible improvements. We will then explore how we can use generative AI, e.g., Generative Adversarial Networks or Variational Autoencoders, to train a deep neural network that can create more realistic decoy spectra, and thus improve our estimation of FDRs.

Medical image segmentation and uncertainty quantification (PhD)

Supervisors: Surajit Ray Relevant research groups: Machine Learning and AI , Imaging, Image Processing and Image Analysis

This project focuses on the application of medical imaging and uncertainty quantification for the detection of tumours. The project aims to provide clinicians with accurate, non-invasive methods for detecting and classifying the presence of malignant and benign tumours. It seeks to combine advanced medical imaging technologies such as ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) with the latest artificial intelligence algorithms. These methods will automate the detection process and may be used for determining malignancy with a high degree of accuracy. Uncertainty quantification (UQ) techniques will help generate a more precise prediction for tumour malignancy by providing a characterisation of the degree of uncertainty associated with the diagnosis. The combination of medical imaging and UQ will significantly decrease the requirement for performing invasive medical procedures such as biopsies. This will improve the accuracy of the tumour detection process and reduce the duration of diagnosis. The project will also benefit from the development of novel image processing algorithms (e.g. deep learning) and machine learning models. These algorithms and models will help improve the accuracy of the tumour detection process and assist clinicians in making the best treatment decisions.

Generating deep fake left ventricles: a step towards personalised heart treatments (PhD)

Supervisors: Andrew Elliott , Vinny Davies , Hao Gao Relevant research groups: Machine Learning and AI , Emulation and Uncertainty Quantification , Biostatistics, Epidemiology and Health Applications , Imaging, Image Processing and Image Analysis

Personalised medicine is an exciting avenue in the field of cardiac healthcare where an understanding of patient-specific mechanisms can lead to improved treatments ( Gao et al., 2017 ). The use of mathematical models to link the underlying properties of the heart with cardiac imaging offers the possibility of obtaining important parameters of heart function non-invasively ( Gao et al., 2015 ). Unfortunately, current estimation methods rely on complex mathematical forward simulations, resulting in a solution taking hours, a time frame not suitable for real-time treatment decisions. To increase the applicability of these methods, statistical emulation methods have been proposed as an efficient way of estimating the parameters ( Davies et al., 2019 ; Noè et al., 2019 ). In this approach, simulations of the mathematical model are run in advance and then machine learning based methods are used to estimate the relationship between the cardiac imaging and the parameters of interest. These methods are, however, limited by our ability to understand the how cardiac geometry varies across patients which is in term limited by the amount of data available ( Romaszko et al., 2019 ). In this project we will look at AI based methods for generating fake cardiac geometries which can be used to increase the amount of data ( Qiao et al., 2023 ). We will explore different types of AI generation, including Generative Adversarial Networks or Variational Autoencoders, to understand how we can generate better 3D and 4D models of the fake left ventricles and create an improved emulation strategy that can make use of them.

Emulation and Uncertainty Quantification - Example Research Projects

Environmental, Ecological Sciences and Sustainability - Example Research Projects

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (phd), statistical modelling for biology, genetics and *omics - example research projects, modelling genetic variation (msc/phd).

Supervisors: Vincent Macaulay Relevant research groups: Bayesian Modelling and Inference , Modelling in Space and Time , Statistical Modelling for Biology, Genetics and *omics

Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis (PhD)

Supervisors: Vinny Davies , Richard Reeve , Claire Harris (BIOSS) Relevant research groups: Bayesian Modelling and Inference , Computational Statistics , Environmental, Ecological Sciences and Sustainability , Statistical Modelling for Biology, Genetics and *omics

Multi objective Bayesian optimisation for in silico to real metabolomics experiments (PhD/MSc)

Implementing a biology-empowered statistical framework to detect rare varient risk factors for complex diseases in whole genome sequence cohorts (PhD)

Supervisors: Vincent Macaulay , Luísa Pereira (Geneticist, i3s ) Relevant research groups: Statistical Modelling for Biology, Genetics and *omics , Biostatistics, Epidemiology and Health Applications

The traditional genome-wide association studies to detect candidate genetic risk factors for complex diseases/phenotypes (GWAS) recur largely to the microarray technology, genotyping at once thousands or millions of variants regularly spaced across the genome. These microarrays include mostly common variants (minor allele frequency, MAF>5%), missing candidate rare variants which are the more likely to be deleterious [ 1 ]. Currently, the best strategy to genotype low-frequency (1%<MAF<5%) and rare (MAF<1%) variants is through next generation sequencing, and the increasingly availability of whole genome sequences (WGS) places us in the brink of detecting rare variants associated with complex diseases [ 2 ]. Statistically, this detection constitutes a challenge, as the massive number of rare variants in genomes (for example, 64.7M in 150 Iberian WGSs) would imply genotyping millions/billions of individuals to attain statistical power. In the last couple years, several statistical methods have being tested in the context of association of rare variants with complex traits [ 2 , 3 , 4 ], largely testing strategies to aggregate the rare variants. These works have not yet tested the statistical empowerment that can be gained by incorporating reliable biological evidence on the aggregation of rare variants in the most probable functional regions, such as non-coding regulatory regions that control the expression of genes [ 4 ]. In fact, it has been demonstrated that even for common candidate variants, most of these variants (around 88%; [ 5 ]) are located in non-coding regions. If this is true for the common variants detected by the traditional GWAS, it is highly probable to be also true for rare variants.

In this work, we will implement a biology-empowered statistical framework to detect rare variant risk factors for complex diseases in WGS cohorts. We will recur to the 200,000 WGSs from UK Biobank database [ 6 ], that will be available to scientists before the end of 2023. Access to clinical information of these >40 years old UK residents is also provided. We will build our framework around type-2 diabetes (T2D), a common complex disease for which thousands of common variant candidates have been found [ 7 ]. Also, the mapping of regulatory elements is well known for the pancreatic beta cells that play a leading role in T2D [ 8 ]. We will use this mapping in guiding the rare variants’ aggregation and test it against a random aggregation across the genome. Of course, the framework rationale will be appliable to any other complex disease. We will browse literature for aggregation methods available at the beginning of this work, but we already selected the method SKAT (sequence kernel association test; [ 3 ]) to be tested. SKAT fits a random-effects model to the set of variants within a genomic interval or biologically-meaningful region (such as a coding or regulatory region) and computes variant-set level p-values, while permitting correction for covariates (such as the principal components mentioned above that can account for population stratification between cases and controls).

Biostatistics, Epidemiology and Health Applications - Example Research Projects

Bayesian statistical data integration of single-cell and bulk “omics” datasets with clinical parameters for accurate prediction of treatment outcomes in rheumatoid arthritis (phd).

Supervisors: Mayetri Gupta Relevant research groups: Bayesian Modelling and Inference , Computational Statistics , Vincent Macaulay , Biostatistics, Epidemiology and Health Applications

Supervisors: Craig Anderson Relevant research groups: Modelling in Space and Time , Bayesian Modelling and Inference , Biostatistics, Epidemiology and Health Applications

Implementing a biology-empowered statistical framework to detect rare varient risk factors for complex diseases in whole genome sequence cohorts (PhD)

Social and Urban Studies - Example Research Projects

Our group has an active PhD student community, and every year we admit new PhD students. We welcome applications from across the world. Further information can be found here .

Imaging, Image Processing and Image Analysis - Example Research Projects

Statistics in Chemistry/Physics - Example Research Projects

Statistics and data analytics education - example research projects.

Our group has an active PhD student community, and every year we admit new PhD students. We welcome applications from across the world. Further information can be found here .

Department of Statistics – Academic Commons Link to Recent Ph.D. Dissertations (2011 – present)

2022 Ph.D. Dissertations

Andrew Davison

Statistical Perspectives on Modern Network Embedding Methods

Sponsor: Tian Zheng

Nabarun Deb

Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing

Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee

Elliot Gordon Rodriguez

Advances in Machine Learning for Compositional Data

Sponsor: John Cunningham

Charles Christopher Margossian

Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling

Sponsor: Andrew Gelman

Alejandra Quintos Lima

Dissertation TBA

Sponsor: Philip Protter

Bridgette Lynn Ratcliffe

Statistical approach to tagging stellar birth groups in the Milky Way

Sponsor: Bodhisattva Sen

Chengliang Tang

Latent Variable Models for Events on Social Networks

On Recovering the Best Rank-? Approximation from Few Entries

Sponsor: Ming Yuan

Sponsor: Sumit Mukherjee

2021 Ph.D. Dissertations

On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods

Sponsor: Liam Paninski

Advances in Statistical Machine Learning Methods for Neural Data Science

Milad Bakhshizadeh

Phase retrieval in the high-dimensional regime

Chi Wing Chu

Semiparametric Inference of Censored Data with Time-dependent Covariates

Miguel Angel Garrido Garcia

Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles

Sponsor: Ioannis Karatzas

Rishabh Dudeja

High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices

Sponsor: Arian Maleki

Statistical Learning for Process Data

Sponsor: Jingchen Liu

Toward a scalable Bayesian workflow

2020 Ph.D. Dissertations

Jonathan Auerbach

Some Statistical Models for Prediction

Sponsor: Shaw-Hwa Lo

Adji Bousso Dieng

Deep Probabilistic Graphical Modeling

Sponsor: David Blei

Guanhua Fang

Latent Variable Models in Measurement: Theory and Application

Sponsor: Zhiliang Ying

Promit Ghosal

Time Evolution of the Kardar-Parisi-Zhang Equation

Sponsor: Ivan Corwin

Partition-based Model Representation Learning

Sihan Huang

Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates

Peter JinHyung Lee

Spike Sorting for Large-scale Multi-electrode Array Recordings in Primate Retina

Statistical Analysis of Complex Data in Survival and Event History Analysis

Multiple Causal Inference with Bayesian Factor Models

New perspectives in cross-validation

Upcoming Events

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Megamenu featured, megamenu social, math/stats thesis and colloquium topics.

Updated: April 2024

Math/Stats Thesis and Colloquium Topics 2024- 2025

The degree with honors in Mathematics or Statistics is awarded to the student who has demonstrated outstanding intellectual achievement in a program of study which extends beyond the requirements of the major. The principal considerations for recommending a student for the degree with honors will be: Mastery of core material and skills, breadth and, particularly, depth of knowledge beyond the core material, ability to pursue independent study of mathematics or statistics, originality in methods of investigation, and, where appropriate, creativity in research.

An honors program normally consists of two semesters (MATH/STAT 493 and 494) and a winter study (WSP 031) of independent research, culminating in a thesis and a presentation. Under certain circumstances, the honors work can consist of coordinated study involving a one semester (MATH/STAT 493 or 494) and a winter study (WSP 030) of independent research, culminating in a “minithesis” and a presentation. At least one semester should be in addition to the major requirements, and thesis courses do not count as 400-level senior seminars.

An honors program in actuarial studies requires significant achievement on four appropriate examinations of the Society of Actuaries.

Highest honors will be reserved for the rare student who has displayed exceptional ability, achievement or originality. Such a student usually will have written a thesis, or pursued actuarial honors and written a mini-thesis. An outstanding student who writes a mini-thesis, or pursues actuarial honors and writes a paper, might also be considered. In all cases, the award of honors and highest honors is the decision of the Department.

Here is a list of possible colloquium topics that different faculty are willing and eager to advise. You can talk to several faculty about any colloquium topic, the sooner the better, at least a month or two before your talk. For various reasons faculty may or may not be willing or able to advise your colloquium, which is another reason to start early.

RESEARCH INTERESTS OF MATHEMATICS AND STATISTICS FACULTY

Here is a list of faculty interests and possible thesis topics. You may use this list to select a thesis topic or you can use the list below to get a general idea of the mathematical interests of our faculty.

Colin Adams (On Leave 2024 – 2025)

Research interests: Topology and tiling theory. I work in low-dimensional topology. Specifically, I work in the two fields of knot theory and hyperbolic 3-manifold theory and develop the connections between the two. Knot theory is the study of knotted circles in 3-space, and it has applications to chemistry, biology and physics. I am also interested in tiling theory and have been working with students in this area as well.

Hyperbolic 3-manifold theory utilizes hyperbolic geometry to understand 3-manifolds, which can be thought of as possible models of the spatial universe.

Possible thesis topics:

Investigate various aspects of virtual knots, a generalization of knots.
Consider hyperbolicity of virtual knots, building on previous SMALL work. For which virtual knots can you prove hyperbolicity?
Investigate why certain virtual knots have the same hyperbolic volume.
Consider the minimal Turaev volume of virtual knots, building on previous SMALL work.
Investigate which knots have totally geodesic Seifert surfaces. In particular, figure out how to interpret this question for virtual knots.
Investigate n-crossing number of knots. An n-crossing is a crossing with n strands of the knot passing through it. Every knot can be drawn in a picture with only n-crossings in it. The least number of n-crossings is called the n-crossing number. Determine the n-crossing number for various n and various families of knots.
An übercrossing projection of a knot is a projection with just one n-crossing. The übercrossing number of a knot is the least n for which there is such an übercrossing projection. Determine the übercrossing number for various knots, and see how it relates to other traditional knot invariants.
A petal projection of a knot is a projection with just one n-crossing such that none of the loops coming out of the crossing are nested. In other words, the projection looks like a daisy. The petal number of a knot is the least n for such a projection. Determine petal number for various knots, and see how it relates to other traditional knot invariants.
In a recent paper, we extended petal number to virtual knots. Show that the virtual petal number of a classical knot is equal to the classical petal number of the knot (This is a GOOD question!)
Similarly, show that the virtual n-crossing number of a classical knot is equal to the classical n-crossing number. (This is known for n = 2.)
Find tilings of the branched sphere by regular polygons. This would extend work of previous research students. There are lots of interesting open problems about something as simple as tilings of the sphere.
Other related topics.

Possible colloquium topics : Particularly interested in topology, knot theory, graph theory, tiling theory and geometry but will consider other topics.

Christina Athanasouli

Research Interests: Differential equations, dynamical systems (both smooth and non-smooth), mathematical modeling with applications in biological and mechanical systems

My research focuses on analyzing mathematical models that describe various phenomena in Mathematical Neuroscience and Engineering. In particular, I work on understanding 1) the underlying mechanisms of human sleep (e.g. how sleep patterns change with development or due to perturbations), and 2) potential design or physical factors that may influence the dynamics in vibro-impact mechanical systems for the purpose of harvesting energy. Mathematically, I use various techniques from dynamical systems and incorporate both numerical and analytical tools in my work.

Possible colloquium topics: Topics in applied mathematics, such as:

Mathematical modeling of sleep-wake regulation
Mathematical modeling vibro-impact systems
Bifurcations/dynamics of mathematical models in Mathematical Neuroscience and Engineering
Bifurcations in piecewise-smooth dynamical systems

Julie Blackwood

Research Interests: Mathematical modeling, theoretical ecology, population biology, differential equations, dynamical systems.

My research uses mathematical models to uncover the complex mechanisms generating ecological dynamics, and when applicable emphasis is placed on evaluating intervention programs. My research is in various ecological areas including ( I ) invasive species management by using mathematical and economic models to evaluate the costs and benefits of control strategies, and ( II ) disease ecology by evaluating competing mathematical models of the transmission dynamics for both human and wildlife diseases.

Mathematical modeling of invasive species
Mathematical modeling of vector-borne or directly transmitted diseases
Developing mathematical models to manage vector-borne diseases through vector control
Other relevant topics of interest in mathematical biology

Each topic (1-3) can focus on a case study of a particular invasive species or disease, and/or can investigate the effects of ecological properties (spatial structure, resource availability, contact structure, etc.) of the system.

Possible colloquium topics: Any topics in applied mathematics, such as:

Research Interest : Statistical methodology and applications. One of my research topics is variable selection for high-dimensional data. I am interested in traditional and modern approaches for selecting variables from a large candidate set in different settings and studying the corresponding theoretical properties. The settings include linear model, partial linear model, survival analysis, dynamic networks, etc. Another part of my research studies the mediation model, which examines the underlying mechanism of how variables relate to each other. My research also involves applying existing methods and developing new procedures to model the correlated observations and capture the time-varying effect. I am also interested in applications of data mining and statistical learning methods, e.g., their applications in analyzing the rhetorical styles in English text data.

Variable selection uses modern techniques such as penalization and screening methods for several different parametric and semi-parametric models.
Extension of the classic mediation models to settings with correlated, longitudinal, or high-dimensional mediators. We could also explore ways to reduce the dimensionality and simplify the structure of mediators to have a stable model that is also easier to interpret.
We shall analyze the English text dataset processed by the Docuscope environment with tools for corpus-based rhetorical analysis. The data have a hierarchical structure and contain rich information about the rhetorical styles used. We could apply statistical models and statistical learning algorithms to reduce dimensions and gain a more insightful understanding of the text.

Possible colloquium topics: I am open to any problems in statistical methodology and applications, not limited to my research interests and the possible thesis topics above.

Richard De Veaux

Research interests: Statistics.

My research interests are in both statistical methodology and in statistical applications. For the first, I look at different methods and try to understand why some methods work well in particular settings, or more creatively, to try to come up with new methods. For the second, I work in collaboration with an investigator (e.g. scientist, doctor, marketing analyst) on a particular statistical application. I have been especially interested in problems dealing with large data sets and the associated modeling tools that work for these problems.

Human Performance and Aging.I have been working on models for assessing the effect of age on performance in running and swimming events. There is still much work to do. So far I’ve looked at masters’ freestyle swimming and running data and a handicapped race in California, but there are world records for each age group and other events in running and swimming that I’ve not incorporated. There are also many other types of events.
Variable Selection. How do we choose variables when we have dozens, hundreds or even thousands of potential predictors? Various model selection strategies exist, but there is still a lot of work to be done to find out which ones work under what assumptions and conditions.
Problems at the interface.In this era of Big Data, not all methods of classical statistics can be applied in practice. What methods scale up well, and what advances in computer science give insights into the statistical methods that are best suited to large data sets?
Applying statistical methods to problems in science or social science.In collaboration with a scientist or social scientist, find a problem for which statistical analysis plays a key role.

Possible colloquium topics:

Almost any topic in statistics that extends things you’ve learned in courses — specifically topics in Experimental design, regression techniques or machine learning
Model selection problems

Thomas Garrity (On Leave 2024 – 2025)

Research interest: Number Theory and Dynamics.

My area of research is officially called “multi-dimensional continued fraction algorithms,” an area that touches many different branches of mathematics (which is one reason it is both interesting and rich). In recent years, students writing theses with me have used serious tools from geometry, dynamics, ergodic theory, functional analysis, linear algebra, differentiability conditions, and combinatorics. (No single person has used all of these tools.) It is an area to see how mathematics is truly interrelated, forming one coherent whole.

While my original interest in this area stemmed from trying to find interesting methods for expressing real numbers as sequences of integers (the Hermite problem), over the years this has led to me interacting with many different mathematicians, and to me learning a whole lot of math. My theses students have had much the same experiences, including the emotional rush of discovery and the occasional despair of frustration. The whole experience of writing a thesis should be intense, and ultimately rewarding. Also, since this area of math has so many facets and has so many entrance points, I have had thesis students from wildly different mathematical backgrounds do wonderful work; hence all welcome.

Generalizations of continued fractions.
Using algebraic geometry to study real submanifolds of complex spaces.

Possible colloquium topics: Any interesting topic in mathematics.

Leo Goldmakher

Research interests: Number theory and arithmetic combinatorics.

I’m interested in quantifying structure and randomness within naturally occurring sets or sequences, such as the prime numbers, or the sequence of coefficients of a continued fraction, or a subset of a vector space. Doing so typically involves using ideas from analysis, probability, algebra, and combinatorics.

Possible thesis topics:

Anything in number theory or arithmetic combinatorics.

Possible colloquium topics: I’m happy to advise a colloquium in any area of math.

Susan Loepp

Research interests: Commutative Algebra. I study algebraic structures called commutative rings. Specifically, I have been investigating the relationship between local rings and their completion. One defines the completion of a ring by first defining a metric on the ring and then completing the ring with respect to that metric. I am interested in what kinds of algebraic properties a ring and its completion share. This relationship has proven to be intricate and quite surprising. I am also interested in the theory of tight closure, and Homological Algebra.

Topics in Commutative Algebra including:

Using completions to construct Noetherian rings with unusual prime ideal structures.
What prime ideals of C[[ x 1 ,…, x n ]] can be maximal in the generic formal fiber of a ring? More generally, characterize what sets of prime ideals of a complete local ring can occur in the generic formal fiber.
Characterize what sets of prime ideals of a complete local ring can occur in formal fibers of ideals with height n where n ≥1.
Characterize which complete local rings are the completion of an excellent unique factorization domain.
Explore the relationship between the formal fibers of R and S where S is a flat extension of R .
Determine which complete local rings are the completion of a catenary integral domain.
Determine which complete local rings are the completion of a catenary unique factorization domain.

Possible colloquium topics: Any topics in mathematics and especially commutative algebra/ring theory.

Steven Miller

For more information and references, see http://www.williams.edu/Mathematics/sjmiller/public_html/index.htm

Research interests : Analytic number theory, random matrix theory, probability and statistics, graph theory.

My main research interest is in the distribution of zeros of L-functions. The most studied of these is the Riemann zeta function, Sum_{n=1 to oo} 1/n^s. The importance of this function becomes apparent when we notice that it can also be written as Prod_{p prime} 1 / (1 – 1/p^s); this function relates properties of the primes to those of the integers (and we know where the integers are!). It turns out that the properties of zeros of L-functions are extremely useful in attacking questions in number theory. Interestingly, a terrific model for these zeros is given by random matrix theory: choose a large matrix at random and study its eigenvalues. This model also does a terrific job describing behavior ranging from heavy nuclei like Uranium to bus routes in Mexico! I’m studying several problems in random matrix theory, which also have applications to graph theory (building efficient networks). I am also working on several problems in probability and statistics, especially (but not limited to) sabermetrics (applying mathematical statistics to baseball) and Benford’s law of digit bias (which is often connected to fascinating questions about equidistribution). Many data sets have a preponderance of first digits equal to 1 (look at the first million Fibonacci numbers, and you’ll see a leading digit of 1 about 30% of the time). In addition to being of theoretical interest, applications range from the IRS (which uses it to detect tax fraud) to computer science (building more efficient computers). I’m exploring the subject with several colleagues in fields ranging from accounting to engineering to the social sciences.

Possible thesis topics:

Theoretical models for zeros of elliptic curve L-functions (in the number field and function field cases).
Studying lower order term behavior in zeros of L-functions.
Studying the distribution of eigenvalues of sets of random matrices.
Exploring Benford’s law of digit bias (both its theory and applications, such as image, voter and tax fraud).
Propagation of viruses in networks (a graph theory / dynamical systems problem). Sabermetrics.
Additive number theory (questions on sum and difference sets).

Possible colloquium topics:

Plus anything you find interesting. I’m also interested in applications, and have worked on subjects ranging from accounting to computer science to geology to marketing….

Ralph Morrison

Research interests: I work in algebraic geometry, tropical geometry, graph theory (especially chip-firing games on graphs), and discrete geometry, as well as computer implementations that study these topics. Algebraic geometry is the study of solution sets to polynomial equations. Such a solution set is called a variety. Tropical geometry is a “skeletonized” version of algebraic geometry. We can take a classical variety and “tropicalize” it, giving us a tropical variety, which is a piecewise-linear subset of Euclidean space. Tropical geometry combines combinatorics, discrete geometry, and graph theory with classical algebraic geometry, and allows for developing theory and computations that tell us about the classical varieties. One flavor of this area of math is to study chip-firing games on graphs, which are motivated by (and applied to) questions about algebraic curves.

Possible thesis topics : Anything related to tropical geometry, algebraic geometry, chip-firing games (or other graph theory topics), and discrete geometry. Here are a few specific topics/questions:

Study the geometry of tropical plane curves, perhaps motivated by results from algebraic geometry. For instance: given 5 (algebraic) conics, there are 3264 conics that are tangent to all 5 of them. What if we look at tropical conics–is there still a fixed number of tropical conics tangent to all of them? If so, what is that number? How does this tropical count relate to the algebraic count?
What can tropical plane curves “look like”? There are a few ways to make this question precise. One common way is to look at the “skeleton” of a tropical curve, a graph that lives inside of the curve and contains most of the interesting data. Which graphs can appear, and what can the lengths of its edges be? I’ve done lots of work with students on these sorts of questions, but there are many open questions!
What can tropical surfaces in three-dimensional space look like? What is the version of a skeleton here? (For instance, a tropical surface of degree 4 contains a distinguished polyhedron with at most 63 facets. Which polyhedra are possible?)
Study the geometry of tropical curves obtained by intersecting two tropical surfaces. For instance, if we intersect a tropical plane with a tropical surface of degree 4, we obtain a tropical curve whose skeleton has three loops. How can those loops be arranged? Or we could intersect degree 2 and degree 3 tropical surfaces, to get a tropical curve with 4 loops; which skeletons are possible there?
One way to study tropical geometry is to replace the usual rules of arithmetic (plus and times) with new rules (min and plus). How do topics like linear algebra work in these fields? (It turns out they’re related to optimization, scheduling, and job assignment problems.)
Chip-firing games on graphs model questions from algebraic geometry. One of the most important comes in the “gonality” of a graph, which is the smallest number of chips on a graph that could eliminate (via a series of “chip-firing moves”) an added debt of -1 anywhere on the graph. There are lots of open questions for studying the gonality of graphs; this include general questions, like “What are good lower bounds on gonality?” and specific ones, like “What’s the gonality of the n-dimensional hypercube graph?”
We can also study versions of gonality where we place -r chips instead of just -1; this gives us the r^th gonality of a graph. Together, the first, second, third, etc. gonalities form the “gonality sequence” of a graph. What sequences of integers can be the gonality sequence of some graph? Is there a graph whose gonality sequence starts 3, 5, 8?
There are many computational and algorithmic questions to ask about chip-firing games. It’s known that computing the gonality of a general graph is NP-hard; what if we restrict to planar graphs? Or graphs that are 3-regular? And can we implement relatively efficient ways of computing these numbers, at least for small graphs?
What if we changed our rules for chip-firing games, for instance by working with chips modulo N? How can we “win” a chip-firing game in that context, since there’s no more notion of debt?
Study a “graph throttling” version of gonality. For instance, instead of minimizing the number of chips we place on the graph, maybe we can also try to decrease the number of chip-firing moves we need to eliminate debt.
Chip-firing games lead to interesting questions on other topics in graph theory. For instance, there’s a conjectured upper bound of (|E|-|V|+4)/2 on the gonality of a graph; and any graph is known to have gonality at least its tree-width. Can we prove the (weaker) result that (|E|-|V|+4)/2 is an upper bound on tree-width? (Such a result would be of interest to graph theorists, even the idea behind it comes from algebraic geometry!)
Topics coming from discrete geometry. For example: suppose you want to make “string art”, where you have one shape inside of another with string weaving between the inside and the outside shapes. For which pairs of shapes is this possible?

Possible Colloquium topics: I’m happy to advise a talk in any area of math, but would be especially excited about talks related to algebra, geometry, graph theory, or discrete mathematics.

Shaoyang Ning (On Leave 2024 – 2025)

Research Interest : Statistical methodologies and applications. My research focuses on the study and design of statistical methods for integrative data analysis, in particular, to address the challenges of increasing complexity and connectivity arising from “Big Data”. I’m interested in innovating statistical methods that efficiently integrate multi-source, multi-resolution information to solve real-life problems. Instances include tracking localized influenza with Google search data and predicting cancer-targeting drugs with high-throughput genetic profiling data. Other interests include Bayesian methods, copula modeling, and nonparametric methods.

Digital (disease) tracking: Using Internet search data to track and predict influenza activities at different resolutions (nation, region, state, city); Integrating other sources of digital data (e.g. Twitter, Facebook) and/or extending to track other epidemics and social/economic events, such as dengue, presidential approval rates, employment rates, and etc.
Predicting cancer drugs with multi-source profiling data: Developing new methods to aggregate genetic profiling data of different sources (e.g., mutations, expression levels, CRISPR knockouts, drug experiments) in cancer cell lines to identify potential cancer-targeting drugs, their modes of actions and genetic targets.
Social media text mining: Developing new methods to analyze and extract information from social media data (e.g. Reddit, Twitter). What are the challenges in analyzing the large-volume but short-length social media data? Can classic methods still apply? How should we innovate to address these difficulties?
Copula modeling: How do we model and estimate associations between different variables when they are beyond multivariate Normal? What if the data are heavily dependent in the tails of their distributions (commonly observed in stock prices)? What if dependence between data are non-symmetric and complex? When the size of data is limited but the dimension is large, can we still recover their correlation structures? Copula model enables to “link” the marginals of a multivariate random variable to its joint distribution with great flexibility and can just be the key to the questions above.
Other cross-disciplinary, data-driven projects: Applying/developing statistical methodology to answer an interesting scientific question in collaboration with a scientist or social scientist.

Possible colloquium topics: Any topics in statistical methodology and application, including but not limited to: topics in applied statistics, Bayesian methods, computational biology, statistical learning, “Big Data” mining, and other cross-disciplinary projects.

Anna Neufeld

Research interests: My research is motivated by the gap between classical statistical tools and practical data analysis. Classic statistical tools are designed for testing a single hypothesis about a single, pre-specified model. However, modern data analysis is an adaptive process that involves exploring the data, fitting several models, evaluating these models, and then testing a potentially large number of hypotheses about one or more selected models. With this in mind, I am interested in topics such as (1) methods for model validation and selection, (2) methods for testing data-driven hypotheses (post-selection inference), and (3) methods for testing a large number of hypotheses. I am also interested in any applied project where I can help a scientist rigorously answer an important question using data.

Cross-validation for unsupervised learning. Cross-validation is one of the most widely-used tools for model validation, but, in its typical form, it cannot be used for unsupervised learning problems. Numerous ad-hoc proposals exist for validating unsupervised learning models, but there is a need to compare and contrast these proposals and work towards a unified approach.
Identifying the number of cell types in single-cell genomics datasets. This is an application of the topic above, since the cell types are typically estimated via unsupervised learning.
There is growing interest in “post-prediction inference”, which is the task of doing valid statistical inference when some inputs to your statistical model are the outputs of other statistical models (i.e. predictions). Frameworks have recently been proposed for post-prediction inference in the setting where you have access to a gold-standard dataset where the true inputs, rather than the predicted inputs, have been observed. A thesis could explore the possibility of post-prediction inference in the absence of this gold-standard dataset.
Any other topic of student interest related to selective inference, multiple testing, or post-prediction inference.
Any collaborative project in which we work with a scientist to identify an interesting question in need of non-standard statistics.
I am open to advising colloquia in almost any area of statistical methodology or applications, including but not limited to: multiple testing, post-selection inference, post-prediction inference, model selection, model validation, statistical machine learning, unsupervised learning, or genomics.

Allison Pacelli

Research interests: Math Education, Math & Politics, and Algebraic Number Theory.

Math Education. Math education is the study of the practice of teaching and learning mathematics, at all levels. For example, do high school calculus students learn best from lecture or inquiry-based learning? What mathematical content knowledge is critical for elementary school math teachers? Is a flipped classroom more effective than a traditional learning format? Many fascinating questions remain, at all levels of education. We can talk further to narrow down project ideas.

Math & Politics. The mathematics of voting and the mathematics of fair division are two fascinating topics in the field of mathematics and politics. Research questions look at types of voting systems, and the properties that we would want a voting system to satisfy, as well as the idea of fairness when splitting up a single object, like cake, or a collection of objects, such as after a divorce or a death.

Algebraic Number Theory. The Fundamental Theorem of Arithmetic states that the ring of integers is a unique factorization domain, that is, every integer can be uniquely factored into a product of primes. In other rings, there are analogues of prime numbers, but factorization into primes is not necessarily unique!

In order to determine whether factorization into primes is unique in the ring of integers of a number field or function field, it is useful to study the associated class group – the group of equivalence classes of ideals. The class group is trivial if and only if the ring is a unique factorization domain. Although the study of class groups dates back to Gauss and played a key role in the history of Fermat’s Last Theorem, many basic questions remain open.

Possible thesis topics:

Topics in math education, including projects at the elementary school level all the way through college level.
Topics in voting and fair division.
Investigating the divisibility of class numbers or the structure of the class group of quadratic fields and higher degree extensions.
Exploring polynomial analogues of theorems from number theory concerning sums of powers, primes, divisibility, and arithmetic functions.

Possible colloquium topics: Anything in number theory, algebra, or math & politics.

Anna Plantinga

Research interests: I am interested in both applied and methodological statistics. My research primarily involves problems related to statistical analysis within genetics, genomics, and in particular the human microbiome (the set of bacteria that live in and on a person). Current areas of interest include longitudinal data, distance-based analysis methods such as kernel machine regression, high-dimensional data, and structured data.

Impacts of microbiome volatility. Sometimes the variability of a microbial community is more indicative of an unhealthy community than the actual bacteria present. We have developed an approach to quantifying microbiome variability (“volatility”). This project will use extensive simulations to explore the impact of between-group differences in volatility on a variety of standard tests for association between the microbiome and a health outcome.
Accounting for excess zeros (sparse feature matrices). Often in a data matrix with many zeros, some of the zeros are “true” or “structural” zeros, whereas others are simply there because we have fewer observations for some subjects. How we account for these zeros affects analysis results. Which methods to account for excess zeros perform best for different analyses?
Longitudinal methods for compositional data. When we have longitudinal data, we assume the same variables are measured at every time point. For high-dimensional compositions, this may not be the case. We would generally assume that the missing component was absent at any time points for which it was not measured. This project will explore alternatives to making that assumption.
Applied statistics research. In collaboration with a scientist or social scientist, use appropriate statistical methodology (or variations on existing methods) to answer an interesting scientific question.

Any topics in statistical application, education, or methodology, including but not restricted to:

Topics in applied statistics.
Methods for microbiome data analysis.
Statistical genetics.
Electronic health records.
Variable selection and statistical learning.
Longitudinal methods.

Cesar Silva

Research interests : Ergodic theory and measurable dynamics; in particular mixing properties and rank one examples, and infinite measure-preserving and nonsingular transformations and group actions. Measurable dynamics of transformations defined on the p-adic field. Measurable sensitivity. Fractals. Fractal Geometry.

Possible thesis topics: Ergodic Theory. Ergodic theory studies the probabilistic behavior of abstract dynamical systems. Dynamical systems are systems that change with time, such as the motion of the planets or of a pendulum. Abstract dynamical systems represent the state of a dynamical system by a point in a mathematical space (phase space). In many cases this space is assumed to be the unit interval [0,1) with Lebesgue measure. One usually assumes that time is measured at discrete intervals and so the law of motion of the system is represented by a single map (or transformation) of the phase space [0,1). In this case one studies various dynamical behaviors of these maps, such as ergodicity, weak mixing, and mixing. I am also interested in studying the measurable dynamics of systems defined on the p-adics numbers. The prerequisite is a first course in real analysis. Topological Dynamics. Dynamics on compact or locally compact spaces.

Topics in mathematics and in particular:

Any topic in measure theory. See for example any of the first few chapters in “Measure and Category” by J. Oxtoby. Possible topics include the Banach-Tarski paradox, the Banach-Mazur game, Liouville numbers and s-Hausdorff measure zero.
Topics in applied linear algebra and functional analysis.
Fractal sets, fractal generation, image compression, and fractal dimension.
Dynamics on the p-adic numbers.
Banach-Tarski paradox, space filling curves.

Mihai Stoiciu

Research interests: Mathematical Physics and Functional Analysis. I am interested in the study of the spectral properties of various operators arising from mathematical physics – especially the Schrodinger operator. In particular, I am investigating the distribution of the eigenvalues for special classes of self-adjoint and unitary random matrices.

Topics in mathematical physics, functional analysis and probability including:

Investigate the spectrum of the Schrodinger operator. Possible research topics: Find good estimates for the number of bound states; Analyze the asymptotic growth of the number of bound states of the discrete Schrodinger operator at large coupling constants.
Study particular classes of orthogonal polynomials on the unit circle.
Investigate numerically the statistical distribution of the eigenvalues for various classes of random CMV matrices.
Study the general theory of point processes and its applications to problems in mathematical physics.

Possible colloquium topics:

Any topics in mathematics, mathematical physics, functional analysis, or probability, such as:

The Schrodinger operator.
Orthogonal polynomials on the unit circle.
Statistical distribution of the eigenvalues of random matrices.
The general theory of point processes and its applications to problems in mathematical physics.

Elizabeth Upton

Research Interests: My research interests center around network science, with a focus on regression methods for network-indexed data. Networks are used to capture the relationships between elements within a system. Examples include social networks, transportation networks, and biological networks. I also enjoy tackling problems with pragmatic applications and am therefore interested in applied interdisciplinary research.

Regression models for network data: how can we incorporate network structure (and dependence) in our regression framework when modeling a vertex-indexed response?
Identify effects shaping network structure. For example, in social networks, the phrase “birds of a feather flock together” is often used to describe homophily. That is, those who have similar interests are more likely to become friends. How can we capture or test this effect, and others, in a regression framework when modeling edge-indexed responses?
Extending models for multilayer networks. Current methodologies combine edges from multiple networks in some sort of weighted averaging scheme. Could a penalized multivariate approach yield a more informative model?
Developing algorithms to make inference on large networks more efficient.
Any topic in linear or generalized linear modeling (including mixed-effects regression models, zero-inflated regressions, etc.).
Applied statistics research. In collaboration with a scientist or social scientist, use appropriate statistical methodology to answer an interesting scientific question.
Any applied statistics research project/paper
Topics in linear or generalized linear modeling
Network visualizations and statistics

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Dissertations & Theses

The following is a list of recent statistics and biostatistics PhD Dissertations and Masters Theses.

Jeffrey Gory (2017) PhD Dissertation (Statistics): Marginally Interpretable Generalized Linear Mixed Models Advisors: Peter Craigmile & Steven MacEachern

Yi Lu (2017) PhD Dissertation (Statistics): Function Registration from a Bayesian Perspective Advisors: Radu Herbei & Sebastian Kurtek

Michael Matthews (2017) PhD Dissertation (Statistics): Extending Ranked Sampling in Inferential Procedures Advisor: Douglas Wolfe

Anna Smith (2017) PhD Dissertation (Statistics): Statistical Methodology for Multiple Networks Advisor: Catherine Calder

Weiyi Xie (2017) PhD Dissertation (Statistics): A Geometric Approach to Visualization of Variability in Univariate and Multivariate Functional Data Advisor: Sebastian Kurtek

Jingying Zeng (2017) Masters Thesis (Statistics): Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering Advisors: Matthew Pratola & Laura Kubatko

Han Zhang (2017) PhD Dissertation (Statistics): Detecting Rare Haplotype-Environmental Interaction and Nonlinear Effects of Rare Haplotypes using Bayesian LASSO on Quantitative Traits Advisor: Shili Lin

Mark Burch (2016) PhD Dissertation (Biostatistics): Statistical Methods for Network Epidemic Models Advisor: Grzegorz Rempala

Po-hsu Chen (2016) PhD Dissertation (Statistics): Modeling Multivariate Simulator Outputs with Applications to Prediction and Sequential Pareto Minimization Advisors: Thomas Santner & Angela Dean

Yanan Jia (2016) PhD Dissertation (Statistics): Generalized Bilinear Mixed-Effects Models for Multi-Indexed Multivariate Data Advisor: Catherine Calder

Rong Lu (2016) PhD Dissertation (Biostatistics): Statistical Methods for Functional Genomics Studies Using Observational Data Advisor: Grzegorz Rempala (Public Health)

Junyan Wang (2016) PhD Dissertation (Statistics): Empirical Bayes Model Averaging in the Presence of Model Misfit Advisors: Mario Peruggia & Christopher Hans

Ran Wei (2016) PhD Dissertation (Statistics): On Estimation Problems in Network Sampling Advisors: David Sivakoff & Elizabeth Stasny

Hui Yang (2016) PhD Dissertation (Statistics): Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation Advisors: Elizabeth Stasny & Asuman Turkmen

Matthew Brems (2015) Masters Thesis (Statistis): The Rare Disease Assumption: The Good, The Bad, and The Ugly Advisor: Shili Lin

Linchao Chen (2015) PhD Dissertation (Statistics): Predictive Modeling of Spatio-Temporal Datasets in High Dimensions Advisors: Mark Berliner & Christopher Hans

Casey Davis (2015) PhD Dissertation (Statistics): A Bayesian Approach to Prediction and Variable Selection Using Nonstationary Gaussian Processes Advisors: Christopher Hans & Thomas Santner

Victor Gendre (2015) Masters Thesis (Statistics): Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency Advisor: Radu Herbei

Zhengyu Hu (2015) PhD Dissertation (Statistics): Initializing the EM Algorithm for Data Clustering and Sub-population Detection Advisors: Steven MacEachern & Joseph Verducci

David Kline (2015) PhD Dissertation (Biostatistics): Systematically Missing Subject-Level Data in Longitudinal Research Synthesis Advisors: Eloise Kaizar, Rebecca Andridge (Public Health)

Andrew Landgraf (2015) PhD Dissertation (Statistics): Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters Advisor: Yoonkyung Lee

Andrew Olsen (2015) PhD Dissertation (Statistics): When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods Advisor: Radu Herbei

Elizabeth Petraglia (2015) PhD Dissertation (Statistics): Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS) Advisor: Elizabeth Stasny

Mark Risser (2015) PhD Dissertation (Statistics): Spatially-Varying Covariance Functions for Nonstationary Spatial Process Modeling Advisor: Catherine Calder

John Stettler (2015) PhD Dissertation (Statistics): The Discrete Threshold Regression Model Advisor: Mario Peruggia

Zachary Thomas (2015) PhD Dissertation (Statistics): Bayesian Hierarchical Space-Time Clustering Methods Advisor: Mark Berliner

Sivaranjani Vaidyanathan (2015) PhD Dissertation (Statistics): Bayesian Models for Computer Model Calibration and Prediction Advisor: Mark Berliner

Xiaomu Wang (2015) PhD Dissertation (Statistics): Robust Bayes in Hierarchical Modeling and Empirical Bayes Analysis in Multivariate Estimation Advisor: Mark Berliner

Staci White (2015) PhD Dissertation (Statistics): Quantifying Model Error in Bayesian Parameter Estimation Advisor: Radu Herbei

Jiaqi Zaetz (2015) PhD Dissertation (Statistics): A Riemannian Framework for Shape Analysis of Annotated 3D Objects Advisor: Sebastian Kurtek

Fangyuan Zhang (2015) PhD Dissertation (Biostatistics): Detecting genomic imprinting and maternal effects in family-based association studies Advisor: Shili Lin

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International Students blog

Thesis life: 7 ways to tackle statistics in your thesis.

By Pranav Kulkarni

Thesis is an integral part of your Masters’ study in Wageningen University and Research. It is the most exciting, independent and technical part of the study. More often than not, most departments in WU expect students to complete a short term independent project or a part of big on-going project for their thesis assignment.

Source : www.coursera.org

This assignment involves proposing a research question, tackling it with help of some observations or experiments, analyzing these observations or results and then stating them by drawing some conclusions.

Since it is an immitigable part of your thesis, you can neither run from statistics nor cry for help.

The penultimate part of this process involves analysis of results which is very crucial for coherence of your thesis assignment.This analysis usually involve use of statistical tools to help draw inferences. Most students who don’t pursue statistics in their curriculum are scared by this prospect. Since it is an immitigable part of your thesis, you can neither run from statistics nor cry for help. But in order to not get intimidated by statistics and its “greco-latin” language, there are a few ways in which you can make your journey through thesis life a pleasant experience.

Make statistics your friend

The best way to end your fear of statistics and all its paraphernalia is to befriend it. Try to learn all that you can about the techniques that you will be using, why they were invented, how they were invented and who did this deed. Personifying the story of statistical techniques makes them digestible and easy to use. Each new method in statistics comes with a unique story and loads of nerdy anecdotes.

If you cannot make friends with statistics, at least make a truce

If you cannot still bring yourself about to be interested in the life and times of statistics, the best way to not hate statistics is to make an agreement with yourself. You must realise that although important, this is only part of your thesis. The better part of your thesis is something you trained for and learned. So, don’t bother to fuss about statistics and make you all nervous. Do your job, enjoy thesis to the fullest and complete the statistical section as soon as possible. At the end, you would have forgotten all about your worries and fears of statistics.

Visualize your data

The best way to understand the results and observations from your study/ experiments, is to visualize your data. See different trends, patterns, or lack thereof to understand what you are supposed to do. Moreover, graphics and illustrations can be used directly in your report. These techniques will also help you decide on which statistical analyses you must perform to answer your research question. Blind decisions about statistics can often influence your study and make it very confusing or worse, make it completely wrong!

Simplify with flowcharts and planning

Similar to graphical visualizations, making flowcharts and planning various steps of your study can prove beneficial to make statistical decisions. Human brain can analyse pictorial information faster than literal information. So, it is always easier to understand your exact goal when you can make decisions based on flowchart or any logical flow-plans.

https://www.imindq.com/blog/how-to-simplify-decision-making-with-flowcharts

Source: www.imindq.com

Find examples on internet

Although statistics is a giant maze of complicated terminologies, the internet holds the key to this particular maze. You can find tons of examples on the web. These may be similar to what you intend to do or be different applications of the similar tools that you wish to engage. Especially, in case of Statistical programming languages like R, SAS, Python, PERL, VBA, etc. there is a vast database of example codes, clarifications and direct training examples available on the internet. Various forums are also available for specialized statistical methodologies where different experts and students discuss the issues regarding their own projects.

Comparative studies

Much unlike blindly searching the internet for examples and taking word of advice from online faceless people, you can systematically learn which quantitative tests to perform by rigorously studying literature of relevant research. Since you came up with a certain problem to tackle in your field of study, chances are, someone else also came up with this issue or something quite similar. You can find solutions to many such problems by scouring the internet for research papers which address the issue. Nevertheless, you should be cautious. It is easy to get lost and disheartened when you find many heavy statistical studies with lots of maths and derivations with huge cryptic symbolical text.

When all else fails, talk to an expert

All the steps above are meant to help you independently tackle whatever hurdles you encounter over the course of your thesis. But, when you cannot tackle them yourself it is always prudent and most efficient to ask for help. Talking to students from your thesis ring who have done something similar is one way of help. Another is to make an appointment with your supervisor and take specific questions to him/ her. If that is not possible, you can contact some other teaching staff or researchers from your research group. Try not to waste their as well as you time by making a list of specific problems that you will like to discuss. I think most are happy to help in any way possible.

Talking to students from your thesis ring who have done something similar is one way of help.

Sometimes, with the help of your supervisor, you can make an appointment with someone from the “Biometris” which is the WU’s statistics department. These people are the real deal; chances are, these people can solve all your problems without any difficulty. Always remember, you are in the process of learning, nobody expects you to be an expert in everything. Ask for help when there seems to be no hope.

Apart from these seven ways to make your statistical journey pleasant, you should always engage in reading, watching, listening to stuff relevant to your thesis topic and talking about it to those who are interested. Most questions have solutions in the ether realm of communication. So, best of luck and break a leg!!!

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There are 4 comments.

A perfect approach in a very crisp and clear manner! The sequence suggested is absolutely perfect and will help the students very much. I particularly liked the idea of visualisation!

You are write! I get totally stuck with learning and understanding statistics for my Dissertation!

Statistics is a technical subject that requires extra effort. With the highlighted tips you already highlighted i expect it will offer the much needed help with statistics analysis in my course.

this is so much relevant to me! Don’t forget one more point: try to enrol specific online statistics course (in my case, I’m too late to join any statistic course). The hardest part for me actually to choose what type of statistical test to choose among many options

Statistics and Actuarial Sciences Theses and Dissertations

This collection contains theses and dissertations from the Department of Statistics and Actuarial Sciences, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

Studies of compound risk models with dependence and parameter uncertainty , Dechen Gao

Theses/Dissertations from 2023 2023

Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm , Zahra Aghahosseinalishirazi

Statistical modelling and applications for sustainable-development goals , Yiyang Chen

Multivariate Regression Analysis for Data with Measurement Error, Missing Values, and/or Sparsity Structures , Jingyu Cui

Addressing the Impact of Time-Dependent Social Groupings on Animal Survival and Recapture Rates in Mark-Recapture Studies , Alexandru M. Draghici

Generalized Poisson random variables: Their distributional properties and actuarial applications , Pouya Faroughi

Optimizing Dynamic Treatment Regimes with Q-Learning: Complications due to Error-Prone Data and Applications to COVID-19 Data , Yasin Khadem Charvadeh

Estimating the spatial correlation structure of measurement error in functional magnetic resonance imaging (fMRI) to improve multivariate inference , Lingling Lin

Cyber risk valuation via a hidden Markov-modulated modelling approach , Yuying Li

Advances in Copula Estimation and Distribution Theory , Yishan Zang

Modelling long-term security returns , XINGHAN ZHU

Theses/Dissertations from 2022 2022

Efficiency Improvements in the Least-Squares Monte Carlo Algorithm , François-Michel Boire

Portfolio Optimization Analysis in the Family of 4/2 Stochastic Volatility Models , Yuyang Cheng

Early-Warning Alert Systems for Financial-Instability Detection: An HMM-Driven Approach , Xing Gu

The Analysis of Mark-recapture Data with Individual Heterogeneity via the H-likelihood , Han-na Kim

Statistical Applications to the Management of Intensive Care and Step-down Units , Yawo Mamoua Kobara

Regression-based Methods for Dynamic Treatment Regimes with Mismeasured Covariates or Misclassified Response , Dan Liu

Statistical Roles of the G-expectation Framework in Model Uncertainty: the Semi-G-structure as a Stepping Stone , Yifan Li

Risk theory: data-driven models , Yang Miao

New Developments on the Estimability and the Estimation of Phase-Type Actuarial Models , Cong Nie

Copulas, maximal dependence, and anomaly detection in bi-variate time series , Ning Sun

Interdisciplinary Knowledge Exchange in Statistics with Applications in Fire Science and Statistical Education , Chelsea Uggenti

On the Geometry of Multi-Affine Polynomials , Junquan Xiao

Understanding Deep Learning with Noisy Labels , Li Yi

An Analysis of Weighted Least Squares Monte Carlo , Xiaotian Zhu

Application Of A Polynomial Affine Method In Dynamic Portfolio Choice , Yichen Zhu

Theses/Dissertations from 2021 2021

A class of phase-type ageing models and their lifetime distributions , Boquan Cheng

Application of Stochastic Control to Portfolio Optimization and Energy Finance , Junhe Chen

Making Sense of Noisy Data: Theory and Applications , Lingzhi Chen

The Mean-Reverting 4/2 Stochastic Volatility Model: Properties And Financial Applications , Zhenxian Gong

Compound Sums, Their Distributions, and Actuarial Pricing , Ang Li

On the Estimation of Heston-Nandi GARCH Using Returns and/or Options: A Simulation-based Approach , Xize Ye

Theses/Dissertations from 2020 2020

A Treatise of PD-LGD Correlation Modelling , Wisdom S. Avusuglo WSA

Visualization and Joint Analysis of Monitored Multivariate Spatio-Temporal Data with Applications to Forest Fire Modelling and Sports Analytics , Devan Becker

Generalized 4/2 Factor Model , Yuyang Cheng

Renewable-energy resources, economic growth and their causal link , Yiyang Chen

Some Insurance Options on Stochastic Drawdowns , Filip Dikic

Extensions of Classification Method Based on Quantiles , Yuanhao Lai

Point Process Modelling of Objects in the Star Formation Complexes of the M33 Galaxy , Dayi Li

Classification-based method for estimating dynamic treatment regimes , Junwei Shen

Statistical Methods with a Focus on Joint Outcome Modeling and on Methods for Fire Science , Da Zhong Xi

Ranking comments: An Entropy-based Method with Word Embedding Clustering , Yuyang Zhang

Theses/Dissertations from 2019 2019

A computationally efficient methodology in pricing a guaranteed minimum accumulation benefit , Yiming Huang

Some Recent Developments on Pareto-optimal Reinsurance , Wenjun Jiang

Classification with Measurement Error in Covariates Or Response, with Application to Prostate Cancer Imaging Study , Kexin Luo

Exploring the Estimability of Mark-Recapture Models with Individual, Time-Varying Covariates using the Scaled Logit Link Function , Jiaqi Mu

Split credibility: A two-dimensional semi-linear credibility model , Jingbing Qiu

Advances in Moment-Based Distributional Methodologies , Yishan Zang

How to Rank Answers in Text Mining , Guandong Zhang

On the Sparre-Andersen Risk Models , Ruixi Zhang

Valuation and Risk Management of Some Longevity and P&C Insurance Products , Yixing Zhao

Theses/Dissertations from 2018 2018

Modelling the Common Risk among Equities Using a New Time Series Model , Jingjia Chu

Stochastic modelling of implied correlation index and herd behavior index. Evidence, properties and pricing. , Lin Fang

Optimal Trading of a Storable Commodity via Forward Markets , Behzad Ghafouri

Statistical Modeling of CO2 Flux Data , Fang He

Advances in the Modeling of Heavy-tailed Distributions , Sang Jin Kang

The Statistical Exploration in the $G$-expectation Framework: The Pseudo Simulation and Estimation of Variance Uncertainty , Yifan Li

Statistical tools for assessment of spatial properties of mutations observed under the microarray platform , Bin Luo

Valuation of Multiple Exercise Option Using a Modified Longstaff and Schwartz Approach , Rahim Mohammadhasani Khorasany

Statistical Applications in Healthcare Systems , Maryam Mojalal

Exact Box-Cox Analysis , Samira Soleymani

Anisotropic kernel smoothing for change-point data with an analysis of fire spread rate variability , John Ronald James Thompson

Some applications of higher-order hidden Markov models in the exotic commodity markets , Heng Xiong

Advances in Semi-Nonparametric Density Estimation and Shrinkage Regression , Hossein Zareamoghaddam

Analysis Challenges for High Dimensional Data , Bangxin Zhao

Theses/Dissertations from 2017 2017

Properties of k-isotropic functions , Tianpei Jiang

Data-Adaptive Kernel Support Vector Machine , Xin Liu

Annuity Product Valuation and Risk Measurement under Correlated Financial and Longevity Risks , Soohong Park

Statistical Modelling, Optimal Strategies and Decisions in Two-Period Economies , Jiang Wu

Theses/Dissertations from 2016 2016

Joint Models for Spatial and Spatio-Temporal Point Processes , Alisha Albert-Green

Applications of Credit Scoring Models , Mimi Mei Ling Chong

Joint Analysis of Zero-heavy Longitudinal Outcomes: Models and Comparison of Study Designs , Erin R. Lundy

Data Smoothing Techniques: Historical and Modern , Lori L. Murray

Joint Modelling in Liver Transplantation , Elizabeth M. Renouf

Probability Models for Health Care Operations with Application to Emergency Medicine , Azaz Bin Sharif

Advances in Portmanteau Diagnostic Tests , Jinkun Xiao

Actuarial Modelling with Mixtures of Markov Chains , Yuzhou Zhang

Theses/Dissertations from 2015 2015

Healthy And Unhealthy Statistics: Examining The Impact Of Erroneous Statistical Analyses In Health-Related Research , Britney Allen

Recent Advances in Accumulating Priority Queues , Na Li

Quantitative Techniques for Spread Trading in Commodity Markets , Mir Hashem Moosavi Avonleghi

A Novel Method for Assessing Co-monotonicity: an Interplay between Mathematics and Statistics with Applications , Danang T. Qoyyimi

Completely monotone and Bernstein functions with convexity properties on their measures , Shen Shan

Online Nonparametric Estimation of Stochastic Differential Equations , Xin Wang

On the Dual Risk Models , Chen Yang

Theses/Dissertations from 2014 2014

Statistical methods for the analysis of RNA sequencing data , Man-Kee Maggie Chu

Valuation and Risk Measurement of Guaranteed Annuity Options under Stochastic Environment , Huan Gao

Statistical Applications in Wildfire Management and Prediction , Lengyi Han

Computing and Approximation Methods for the Distribution of Multivariate Aggregate Claims , Tao Jin

The Doubly Adaptive LASSO Methods for Time Series Analysis , Zi Zhen Liu

Risk models with dependence and perturbation , Zhong Li

Censored Time Series Analysis , Nagham Muslim Mohammad

A Spatial Analysis of Forest Fire Survival and a Marked Cluster Process for Simulating Fire Load , Amy A. Morin

Estimation of Hidden Markov Models and Their Applications in Finance , Anton Tenyakov

Perfect and Nearly Perfect Sampling of Work-conserving Queues , Yaofei Xiong

Decision Theory Based Models in Insurance and Beyond , Raymond Ye Zhang

Theses/Dissertations from 2013 2013

Seasonal Decomposition for Geographical Time Series using Nonparametric Regression , Hyukjun Gweon

Stochastic simulation and spatial statistics of large datasets using parallel computing , Jonathan SW Lee

Flexible Partially Linear Single Index Regression Models for Multivariate Survival Data , Na Lei

Joint outcome modeling using shared frailties with application to temporal streamflow data , Lihua Li

Asymptotic Theory for GARCH-in-mean Models , Weiwei Liu

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A Grand Journey of Statistical Hierarchical Modelling

Advances in empirical bayes modeling and bayesian computation , advances in statistical network modeling and nonlinear time series modeling , advances in the normal-normal hierarchical model , analysis, modeling, and optimal experimental design under uncertainty: from carbon nano-structures to 3d printing , bayesian biclustering on discrete data: variable selection methods , bayesian learning of relationships , a bayesian perspective on factorial experiments using potential outcomes , building interpretable models: from bayesian networks to neural networks , causal inference under network interference: a framework for experiments on social networks , complications in causal inference: incorporating information observed after treatment is assigned , diagnostic tools in missing data and causal inference on time series , dilemmas in design: from neyman and fisher to 3d printing , distributed and multiphase inference in theory and practice: principles, modeling, and computation for high-throughput science , essays in causal inference and public policy , expediting scientific discoveries with bayesian statistical methods , exploring objective causal inference in case-noncase studies under the rubin causal model , exploring the role of randomization in causal inference , extensions of randomization-based methods for causal inference , g-squared statistic for detecting dependence, additive modeling, and calibration concordance for astrophysical data .

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Qualitative & Quantitative data analysis

Best Statistics Research Topics & Ideas For 2021-22

Date published October 7 2021 by Jacob Miller

Statistics is a demanding subject that deals with the collection, analysis, interpretation, evaluation, and management of numeric data. The topic selection of the statistics dissertation can involve the subfields of statistics, i.e. Probability Theory, Mathematical Statistics, Design of Experiments, Sampling, Classification, and Time Series.

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Complications in statistics researches:

This subject is much complicated, further, the implication of the proportions in large quantities under complex theories contribute to the difficulties concerning the subject. That’s why it is hard to find considerable statistics dissertation topics. Moreover, the multiple dimensions of the subject make it more problematic to come up with a focused and comprehensive topic.

Why Choosing a Statistics Dissertation Topic is Hard for Students?

While selecting a topic for a statistics dissertation, you must consider the fundamental idea of statistics, i.e. variation and uncertainty. Certain statistical frameworks and methods are applied to get the results.

The topic of the statistics dissertation should be so close to the subject that you will be able the statistical method in the dissertation and presentation of findings.

There are several reasons which together make it a difficult task for the students to select a worthwhile topic for their statistics dissertation.

Shortage of Ideas

Students usually lack in generating potential ideas concerning different areas and aspects of the subject. That’s why they face difficulty in listing out the suitable statistics topics for the dissertation.

Wider Scope

Statistics has a wide scope. It holds a relation with scientific, industrial, and social problems. So, a dissertation topic for this subject can never stand out alone. Due to this reason, students find it difficult to determine their direction and fail to select a potential topic.

Irrelevant or diversified knowledge

Somehow, if students manage to come up with some understandable topics for their dissertation, the uncertainty of the context or the background leads them towards the confusion. They are unable to find a purpose and the background on which they can base their research.

While this all seems a pretty tough task, so then you may take inspiration from our free dissertation topics, and even better you can get the professional on those each topic.

How Do We Help You Select a Statistics Dissertation Topic?

We have skilled and professional subject experts, who bring the best ideas for your statistics dissertation selection. They are well aware of how to meet your subject requirements and professors’ expectations. Through their expertise, they help you select the most significant topics for your dissertation.

By selecting one of the strong statistics research topics we propose, you may contribute to the subject through your intellectual capabilities and unique ideas. While preparing a list of topic suggestions for you, we focus on the following points.

Your level of Education
Subject Domain
Area of Interest
Prerequisite Guidelines by the University (if any)

What do our experts say about the Statistics Topic Selection?

Our statistics dissertation experts are well-equipped with dense knowledge in the subject. They know which topic is worthy to be chosen for your dissertation. According to our experts, your topic must involve data collection, data analysis, and data synthesis.

You also must have to go through with several previous dissertations and research papers regarding the subject so that you can come up with a topic having fine scope, context, relevancy, and accuracy. Further, it should be concise and manageable so that you can complete a dissertation on it within the deadline.

You can avoid all these complexities by hiring our statistics dissertation topic selection services. Our experts have produced hundreds of successful works for the satisfaction of the customers. With vast experience in the world of academics and command of statistics dissertations, they have prepared the list of most suitable statistics dissertation topics.

Bayesian Methods for Functional and Time Series

Kernel regression using the four fourier transform, assessing and accounting for correlation in rna-seq data analysis., a guide to doing statistics in second language research using spss, prediction interval methods for reliability data, relevance of tests of significances uses and limitations., interaction forward selection in ultra-high-dimension functional linear models..

To know the details of the above-mentioned topics and have an idea about their aims and objectives, you can consult with our team. You are welcomed 24/7 to get our consultancy. Further, you can have more potential topics for your statistics dissertation topics by hiring our services.

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Academic Level Undergraduate Masters PhD Others

List of Best Statistics Research Topics with Objectives

Objectives:

To explore all new bayesian methods which are used in statistical analysis.
To introduce new methodology of bayesian which are suitable for functional and time series data.
To exhibit the functional challenges provided by the methodology.

To explore the methods of kernel regression

To demonstrate the method of speeding up the computation of kernel.

To analyse the FFT to improve the computation of kernel.

Difficulties in Learning Basic Concepts in Probability and Statistics: Implications of Research.

To explore the importance of statistics and probability.

To examine the different methods of statistics and probability used in education system.

To provide the need for collaborative and cross-disciplinary in researches.

To explore the concepts behind the usage of statistics in different domains.

To examine the concept of statistics in Second Language.

To study and implement the SPSS software in statistics.

To study the importance of Prediction in statistics.

To analyse the statistical Prediction methods in statistics theory.

To examine the different methods of Prediction interval under the parametric framework.

To study the importance of statistical tools and significance test both in parametric and nonparametric test.

To examine the statistical tools significance in decision making.

To evaluate the statistical significance test in information retrieval.

To study the statistical methods for the variable selection in ultra-high dimensional functional linear models.

To propose two forward selection procedures on the basis of coefficients approximation.

To demonstrate the application of the proposed methodologies.

Bayes Methods for Biclustering and Vector Data with Binary Coordinates.

To explore the different method of Bayes and its applications.

To examine the Bayes method for the purpose of biclustering and inference for mixture models.

To represent the performance of model through the simulation and applications to real datasets.

To study the concept behind the RNA- sequence data analysis and its procedure.

To examine the papers on the analysis of RNA- sequence data analysis.

To perform a simulation and validate the proposed methods on the basis of results.

An Exploration of Techniques Used in Data Analytics to Produce Analysed Data in Graphical Format.

To explore the techniques used in data analytics used for various purposes in order to produce visual charts.

To demonstrate the use of python language as a main feature in Data analytics.

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

1.1 AP Statistics Topics for Project
1.2 Statistics Project Topics for High School Students
1.3 Statistical Survey Topics
1.4 Statistical Experiment Ideas
1.5 Easy Stats Project Ideas
1.6 Business Ideas for Statistics Project
1.7 Socio-Economic Easy Statistics Project Ideas
1.8 Experiment Ideas for Statistics and Analysis
2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

Challenging concepts suitable for advanced placement courses.
Accessible ideas that are engaging and educational for younger students.
Ideas for conducting surveys and analyzing the results.
Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

Need help with statistics project? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

AP Statistics Topics for Project

Analyzing Variance in Climate Data Over Decades.
The Correlation Between Economic Indicators and Standard of Living.
Statistical Analysis of Voter Behavior Patterns.
Probability Models in Sports: Predicting Outcomes.
The Effectiveness of Different Teaching Methods: A Statistical Study.
Analysis of Demographic Data in Public Health.
Time Series Analysis of Stock Market Trends.
Investigating the Impact of Social Media on Academic Performance.
Survival Analysis in Clinical Trial Data.
Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

The Mathematics of Personal Finance: Budgeting and Spending Habits.
Analysis of Class Performance: Test Scores and Study Habits.
A Statistical Comparison of Local Public Transportation Options.
Survey on Dietary Habits and Physical Health Among Teenagers.
Analyzing the Popularity of Various Music Genres in School.
The Impact of Sleep on Academic Performance: A Statistical Approach.
Statistical Study on the Use of Technology in Education.
Comparing Athletic Performance Across Different Sports.
Trends in Social Media Usage Among High School Students.
The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

Public Opinion on Environmental Conservation Efforts.
Consumer Preferences in the Fast Food Industry.
Attitudes Towards Online Learning vs. Traditional Classroom Learning.
Survey on Workplace Satisfaction and Productivity.
Public Health: Attitudes Towards Vaccination.
Trends in Mobile Phone Usage and Preferences.
Community Response to Local Government Policies.
Consumer Behavior in Online vs. Offline Shopping.
Perceptions of Public Safety and Law Enforcement.
Social Media Influence on Political Opinions.

Statistical Experiment Ideas

The Effect of Light on Plant Growth.
Memory Retention: Visual vs. Auditory Information.
Caffeine Consumption and Cognitive Performance.
The Impact of Exercise on Stress Levels.
Testing the Efficacy of Natural vs. Chemical Fertilizers.
The Influence of Color on Mood and Perception.
Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
The Effectiveness of Different Types of Water Filters.
Analyzing the Impact of Room Temperature on Concentration.
Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

Average Daily Screen Time Among Students.
Analyzing the Most Common Birth Months.
Favorite School Subjects Among Peers.
Average Time Spent on Homework Weekly.
Frequency of Public Transport Usage.
Comparison of Pet Ownership in the Community.
Favorite Types of Movies or TV Shows.
Daily Water Consumption Habits.
Common Breakfast Choices and Their Nutritional Value.
Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

Analyzing Customer Satisfaction in Retail Stores.
Market Analysis of a New Product Launch.
Employee Performance Metrics and Organizational Success.
Sales Data Analysis for E-commerce Websites.
Impact of Advertising on Consumer Buying Behavior.
Analysis of Supply Chain Efficiency.
Customer Loyalty and Retention Strategies.
Trend Analysis in Social Media Marketing.
Financial Risk Assessment in Investment Decisions.
Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

Income Inequality and Its Impact on Education.
The Correlation Between Unemployment Rates and Crime Levels.
Analyzing the Effects of Minimum Wage Changes.
The Relationship Between Public Health Expenditure and Population Health.
Demographic Analysis of Housing Affordability.
The Impact of Immigration on Local Economies.
Analysis of Gender Pay Gap in Different Industries.
Statistical Study of Homelessness Causes and Solutions.
Education Levels and Their Impact on Job Opportunities.
Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

Multivariate Analysis of Global Climate Change Data.
Time-Series Analysis in Predicting Economic Recessions.
Logistic Regression in Medical Outcome Prediction.
Machine Learning Applications in Statistical Modeling.
Network Analysis in Social Media Data.
Bayesian Analysis of Scientific Research Data.
The Use of Factor Analysis in Psychology Studies.
Spatial Data Analysis in Geographic Information Systems (GIS).
Predictive Analysis in Customer Relationship Management (CRM).
Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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Statistics PhD theses

2015 onwards.

Abdulrafiu Babatunde Odunuga
Philip Maybank
Natalie Dimier
Chintu Desai	Statistical study designs for phase III pharmacogenetic clinical trials
Frank Owusu-Ansah	Methodology for joint modelling of spatial variation and competition effects in the analysis of varietal selection trials

Supada Charoensawat	A likelihood approach based upon the proportional hazards model for SROC modelling in meta-analysis of diagnostic studies
Pianpool Kirdwichai	A nonparametric regression approach to the analysis of genomewide association studies
Reynaldo Martina	DStat thesis: Challenges in modelling pharmacogenetic data: Investigating biomarker and clinical response simultaneously for optimal dose prediction
Rungruttikarn Moungmai	Family-based genetic association studies in a likelihood framework
Michael Dunbar	Multiple hydro-ecological stressor interactions assessed using statistical models
Osama Abdulhey	Alcohol consumption and mortality from all and specific causes: the J-hypothesis. A systematic review and meta-analysis of current and historical evidence
Rattana Lerdsuwansri	Generalisation of the Lincoln-Peterson approach to non-binary source variables
Krisana Lanumteang	Estimation of the size of a target population using Capture-Recapture methods based upon multiple sources and continuous time experiments
Rainer-Georg Göldner	Investigation of new single locus and multivariate methods for the analysis of genetic association studies
Isak Neema	Survey and monitoring crimes in Namibia through the likelihood based cluster analysis
Mercedes Andrade Bejarano	Monthly average temperature modelling for Valle del Cauca (Colombia)
Robert Mastrodomenico	Statistical analysis of genetic association studies
Ruth Butler	DStat thesis: An exploration of the statistical consequences of sub-sampling for species identification
Carmen Ybarra Moncada	Multivariate methods with application to spectroscopy
Alun Bedding	The Bayesian analysis of dose titration to effect in Phase II clinical trials in order to design Phase III
Timothy Montague	Adaptive designs for bioequivalence trials
Magnus Kjaer	Clinical trials of cytostatic agents with repeated measurements: using the regression coefficients as response
Kamziah Abd Kudus	Survival analysis models for interval censored data with application to an plantation spacing trial
Isobel Barnes	Point estimation after a sequential clinical trial
Ben Carter	Statistical methodology for the analysis of microarray data
Joanna Burke	Regularised regression in QTL mapping
Alexandre M F G da Silva	Methods for the analysis of multivariate lifetime data with frailty
Harsukhjit Deo	Analysis of a Quantitative Trait Locus for twin data using univariate and multivariate linear mixed effects models
Kim Bolland	The design and analysis of neurological trials yielding repeated ordinal data
Fazil Baksh	Sequential tests of association with applications in genetic epidemiology
Martyn Byng	A statistical model for locating regulatory regions in novel DNA sequences
Rob Deardon	Representation bias in field trials for airborne plant pathogens
Marian Hamshere	Statistical aspects of objects generated by dynamic processes at sea, detected by remote sensing techniques
Mike Branson	The analysis of survival data in which patients switch treatments
Christoph Lang	Generalised estimating equation methods in statistical genetics
V R P Putcha	Random effects in survival analysis

Robin Fletcher	Statistical inversion of surface parameters from ATSR-2 satellite observations
Seth Ohemeng-Dapaah	Methods for analysis and interpretation of genotype by environment interaction
Emmanuelle Vincent	Sequential designs for clinical trials involving multiple treatments
Pi Wen Tsai	Three-level designs robust to model uncertainty
Jo Farebrother	Statistical design and analysis of factorial combination drug trials
Mark Lennon	Design and analysis of multiple site large plot field experiments
Norberto Lavorenti	Fitting models in a bivariate analysis of intercrops
Bernard North	Contributions to survival analysis
Karen Ayres	Measuring genetic correlations within and between loci, with implications for disequilibrium mapping and forensic identification
Andrew Morris	Transmission tests of linkage and association using samples of nuclear families with at least one affected child
Julian Higgins	Exploiting information in random effects meta-analysis
Mohammed Inayat Khan	Improving precision of agricultural field experiments in Pakistan
Luzia Trinca	Blocking response surface designs
Phil Bowtell	Non-linear functional relationships
Louise Burt	Statistical modelling of volcanic hazards
Helen Millns	The application of statistical methods to the analysis of diet and coronary heart disease in Scotland
Dominic Neary	Methods of analysis for ordinal repeated measures data
Graham Pursey	Shape location and classification with reference to fungal spores
Nigel Stallard	Increasing efficiency in the design and analysis of animal toxicology studies
Katarzyna Stepniewska	Some variable selection problems in medical research

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If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-. Literacy rate in a city. Abortion and pregnancy rate in the USA. Eating disorders in the citizens.
500+ Statistics Research Topics
500+ Statistics Research Topics. March 25, 2024. by Muhammad Hassan. Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more.
120 Statistical Research Topics: Latest Trends & Techniques
Here are some of the best statistical research topics worth writing on: Predictive Healthcare Modeling with Machine Learning. Analyzing Online Education During COVID-19 Epidemic. Modeling How Climate Change Affects Natural Disasters. Essential Elements Influencing Personnel Productivity. Social Media Influence on Customer Choices and Behavior.
Mathematics and Statistics Theses and Dissertations
Theses/Dissertations from 2016 PDF. A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida, Joy Marie D'andrea. PDF. Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize, Sherlene Enriquez-Savery. PDF
100 Statistics Research Topics
Statistics Research Topics in Business. Understanding the factors that influence consumer purchase decisions in the technology industry. Advertising and sales revenue: a time-series analysis. The effectiveness of customer loyalty programs in increasing customer retention and revenue.
Recent Dissertation Topics
2015. 2014. 2013. 2012. 2011. 2010. 2009. 2008. This list of recent dissertation topics shows the range of research areas that our students are working on.
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PhD Theses
PhD Theses. 2023. Title. Author. Supervisor. Statistical Methods for the Analysis and Prediction of Hierarchical Time Series Data with Applications to Demography. Daphne Liu. Adrian E Raftery. Exponential Family Models for Rich Preference Ranking Data.
What do senior theses in Statistics look like?
Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors: 1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research ...
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Dissertations & Theses
The following is a list of recent statistics and biostatistics PhD Dissertations and Masters Theses. Jeffrey Gory (2017) PhD Dissertation (Statistics): Marginally Interpretable Generalized Linear Mixed Models Advisors: Peter Craigmile & Steven MacEachern Yi Lu (2017) PhD Dissertation (Statistics): Function Registration from a Bayesian Perspective Advisors: Radu Herbei & Sebastian Kurtek
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and ﬁgures. In Section 4, some notes about the rules of conduct when writing a master's thesis are provided. 2 The Structure of a Master's Thesis A master's thesis is an independent scientiﬁc work and is meant to prepare students for future professional or academic work. Largely, the thesis is expected to be similar to papers published in
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Browsing FAS Theses and Dissertations by FAS Department "Statistics"
Statistics is the art of communicating with the silent truth-teller: data. More legitimate, accurate and powerful inference from data is the endless pursuit of all statisticians. ... We present three topics in this thesis, G-squared statistic for independence testing as well as additive modeling, and calibration concordance by multiplicative ...
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With vast experience in the world of academics and command of statistics dissertations, they have prepared the list of most suitable statistics dissertation topics. Bayesian Methods for Functional and Time Series. Kernel Regression Using the Four Fourier Transform. Assessing and Accounting for Correlation in RNA-Seq Data Analysis.
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Statistics PhD theses
DStat thesis: Challenges in modelling pharmacogenetic data: Investigating biomarker and clinical response simultaneously for optimal dose prediction. Rungruttikarn Moungmai. Family-based genetic association studies in a likelihood framework. Michael Dunbar. Multiple hydro-ecological stressor interactions assessed using statistical models.
[100+] Statistics Research Topics With Free [Thesis Pdf] 2023
Check Thesis. 1. Fertility and fertility preferences in India A statistical analysis for the programme management. Download. 2. Stochastic models in fishery economics with special reference to Kerala. Download. 3. Nonparametric Estimation of Survivor Function in Bivariate Competing Risk Model.

	Using the sir epidemic model to infer the SARS outbreak in Beijing, 2003 \| M.S. \| 05/2019
	Data mining to identify gene regulatory elements \| M.S. \| 05/2019
	A comparison between logistic regression and neural networks in a constructed response item study \| M.S. \| 05/2019
	The effect of CEO political ideology on executive succession following firm misconduct \| M.S. \| 05/2019
	Analyses of USA birth-date distribution \| M.S. \| 08/2019
	Sawtimber potential proportion dynamics for loblolly pine in the Southeastern U.S. \| M.S. \| 08/2019
	Scalable individual planning in open and typed agent systems \| M.S. \| 05/2019
	Three different approaches of missing data imputation for financial data \| M.S. \| 05/2019
	A statistical approach for calibrating hydrologic models \| M.S. \| 05/2018
	Prediction of peanut butter prices in the United States by tracking concept drift \| M.S. \| 05/2018
	An application of semiparametric model estimation under shape invariance to fmri data \| M.S. \| 08/2018
	Regularized aggregation of multiple graphs with application to FMRI data \| M.S. \| 12/2018
	Model comparison with squared sharpe ratios of mimicking portfolios \| M.S. \| 05/2018
	Efficient genotyping by sampling extreme individuals in a genome wide association study in plants \| M.S. \| 05/2018
	A statistical analysis of some aspects of well-being of South Korean elderly population \| M.S. \| 05/2018
	A statistical analysis of crime in San Luis Obispo (2009-2017) \| M.S. \| 05/2018
	An expected outcome framework for evaluating batting and pitching performance in major league baseball with applications to the "juiced ball" and the "fly ball revolution" \| M.S. \| 05/2018
	Modelling precipitation volumes using a weibull mixture and the gamma generalized linear model \| M.S. \| 12/2018
	Normal and average: lexical ambiguity in an introductory statistics course \| M.S. \| 12/2018
	The periodic solution and the global asymptotic stability for northeastern Puerto Rico ecosystem \| M.S. \| 08/2018
	An examination of the transfer of errors to species tree estimation caused by model selection in gene tree estimation \| M.S. \| 05/2017
	Assessment of the performance of the lasso algorithm compared to the k-nn algorithm with high-dimensional class imbalanced data \| M.S. \| 05/2017
	A comparison of pedagogical approaches in introductory statstics \| M.S. \| 05/2017
	A mixed effect model with feature extraction for functional magnetic resonance imaging (fMRI) data \| M.S. \| 05/2017
	Assessing UGA transportation and parking services data collection using RouteMatch Software? \| M.S. \| 05/2017
	Analyzing android ad-libraries \| M.S. \| 12/2017
	Generative spatiotemporal modeling of neutrophil behavior \| M.S. \| 12/2017
	An application of graphical models to fMRI data using the lasso penalty \| M.S. \| 05/2017
	Skew and bias: the efficacy of an intervention in an introductory statistics course \| M.S. \| 05/2017
	Bootstrap based measurement of serial correlation in time series objects \| M.S. \| 12/2017
	Copula modeling analysis on multi-dimensional portfolios with backtesting \| M.S. \| 08/2016
	Data analysis of the pattern information of the collective decision-making process in subterranean termites species \| M.S. \| 08/2016
	Modeling NFL quarterback success with college data \| M.S. \| 05/2016
	Comparison of data sampling methods on IRT parameter estimation \| M.S. \| 05/2016
	Estimating precipitation volume distributions using data from the spatially dense cocorahs network \| M.S. \| 08/2016
	Predictive biomarker reproducibility modeling with censored data \| M.S. \| 12/2016
	Parallel matrix factorization in big data analytics \| M.S. \| 08/2016
	Maximum monthly rainfall behavior along the front range of Colorado \| M.S. \| 12/2016
	A study on adaptive lasso and its weight selection \| M.S. \| 12/2016
	Predictors of secondary traumatic stress among clinical social workers: a focus on the impact of the supervisory relationship \| M.S. \| 05/2016
	Estimating nutrient uptake in streams with pulse release \| M.S. \| 12/2016
	Prediction of crime categories in San Francisco area \| M.S. \| 05/2016
	Perceived importance and objective measures of built environment walkability of a university campus \| M.S. \| 05/2016
	Mergers and network effects: understanding the recent increase in percentage of non-weather-caused flight delays in the United States \| M.S. \| 05/2015
	A rolling analysis on the prediction of Value at Risk with multivariate GARCH and copula \| M.S. \| 05/2015
	Analysis of climate-crop yield relationships in Canada with distance correlation \| M.S. \| 12/2015
	False negative control for multiple acceptance-support hypotheses testing problem \| M.S. \| 05/2015
	Big data analytic tools to detect fraud in healthcare data \| M.S. \| 12/2015
	Genetic algorithms developed in R software for finding optimal experimental designs \| M.S. \| 05/2015
	Bootstrap-based test for volatility shifts in GARCH against long-range dependence \| M.S. \| 05/2015
	A rule-engine-based application for over-the-counter medication safety \| M.S. \| 12/2014
	Household whole and low-fat milk consumption in Poland: a censored system approach \| M.S. \| 12/2014
	Calibrating test item banks for an introductory statistics course \| M.S. \| 05/2014
	A guide and solution manual to The elements of statistical learning \| M.S. \| 12/2014
	Programmatic assessment for an undergraduate statistics major \| M.S. \| 05/2014
	Global temperature trends \| M.S. \| 08/2014
	Penalized regression models for Major League Baseball metrics \| M.S. \| 05/2014
	Discriminant function analysis of Major League Baseball steroid use \| M.S. \| 05/2014
	Estimation of government employment using multivariate hierarchical Bayes modeling \| M.S. \| 05/2014
	Feasibility of small voxel sizes in canine brain 1H-magnetic resonance spectroscopy at 3T \| M.S. \| 08/2014
	Improving the robustness of turbulent fluxes: an examination of the role of waves on fluxes and turbulence statistics \| M.S. \| 08/2014
	Phylogenetic analysis of cancer microarray data \| M.S. \| 12/2014
	Students? misconceptions about introductory statistics topics: assessing STAT 2000 outcomes using CAOS \| M.S. \| 05/2013
	The use of bootstrapping to measure image differences in fMRI data \| M.S. \| 05/2013
	Performance of farm level vs area level crop insurance \| M.S. \| 08/2013
	Application of multivariate geospatial statistics to soil hydraulic properties \| M.S. \| 12/2013
	Characterizing the socioeconomics of metropolitan transportation network expansion by mining a nationwide road change database \| M.S. \| 05/2013
	The rise of the Big Data: why should statisticians embrace collaborations with computer scientists \| M.S. \| 12/2013
	Undergraduate students? attitudes toward statistics in an introductory statistics class \| M.S. \| 12/2013
	Comparison of methods of analysis for Pretest and Posttest data \| M.S. \| 08/2013
	Drought, biofuel, and livestock \| M.S. \| 12/2013
	A comparison of meta-analytic approaches on the consequences of role stressors \| M.S. \| 08/2013
	Improving validity and reliability in STAT 2000 assessments \| M.S. \| 05/2013
	Classification analysis in microarray data using biological pathway and gene family information \| M.S. \| 12/2013
	Predicting equity returns using Twitter sentiment \| M.S. \| 05/2013
	Monthly trends in maxima of low temperatures in Georgia, USA \| M.S. \| 05/2013
	HIV classification using DNA sequences \| M.S. \| 08/2013
	Double eQTL mapping method to improve identification of trans eQTLs and construct intermediate gene networks \| M.S. \| 05/2013
	CacheMeter \| M.S. \| 08/2013
	A study on expectiles: measuring risk in finance \| M.S. \| 12/2012
	Design of cost-fffective cancer biomarker reproducibility studies \| M.S. \| 08/2012
	Flux measurements in the stable boundary layer and during morning transition \| M.S. \| 12/2012
	Predicting outcomes of mixed martial arts fights with novel fight variables \| M.S. \| 08/2012
	Estimation in populations with rare events \| M.S. \| 05/2012
	A Bayesian hierarchical spatial model for West Nile Virus in New York City: evaluating an approach to handle large spatial data sets \| M.S. \| 12/2012
	The influence of measurement errors in tumor markers \| M.S. \| 12/2012
	Statistical interpretation of experiments with laying hens \| M.S. \| 05/2012
	Estimation of genomic copy frequency with correlated observations \| M.S. \| 05/2012
	The appearance of Michelle Obama: an analysis of the First Lady's exposure in magazines, from January 2008 to December 2009 \| M.S. \| 05/2012
	Case studies of clear-air turbulence: evaluation and verification of new forecasting techniques \| M.S. \| 08/2012
	Assessment of nonparametric frontier models applied to socially responsible investment \| M.S. \| 08/2011
	Nonparametric GARCH models for financial volatility \| M.S. \| 08/2011
	Investigating some estimators of the fractional degree of differencing, in long memory time series \| M.S. \| 05/2011
	A bootstrap method for fitting a linear regression model to interval-valued data \| M.S. \| 05/2011
	Variable selection in longitudinal data with application to education \| M.S. \| 08/2011
	Conservation genetics of the red-cockaded woodpecker \| M.S. \| 05/2010
	Using regression based methods for time-constrained scaling of parallel processor computing applications \| M.S. \| 05/2010
	Statistical study of the decay lifetimes of the photo-excited DNA nucleobase Adenine \| M.S. \| 12/2010
	The interpretation of experiments with poultry \| M.S. \| 12/2010
	Statistical identification of the quinic acid responsive genes in Neurospora crassa \| M.S. \| 12/2010
	A content analysis of advertiser influence on editorial content in fashion magazines \| M.S. \| 05/2010
	Derivation of the complete transcriptome of Escherichia coli from microarray data \| M.S. \| 12/2009
	The coordination of design and analysis techniques for functional magnetic resonance imaging data \| M.S. \| 05/2009
	A review of ruin probability models \| M.S. \| 12/2009
	The exploration of statistical ensemble methods for market segmentation \| M.S. \| 05/2009
	Misidentification error in non-invasive genetic mark-recapture sampling: case study with the central Georgia black bear population \| M.S. \| 05/2009
	A time series analysis of mortality and air pollution in Hong Kong from 1997 to 2007 \| M.S. \| 05/2009
	Penalized principal component regression \| M.S. \| 05/2008
	Statistical methods for turtle bycatch data \| M.S. \| 12/2008
	Sexual dysfunction in young women with breast cancer \| M.S. \| 12/2008
	Investigation of statistical methods for determination of benchmark dose limits for retinoic acid-induced fetal forelimb malformation in mice \| M.S. \| 12/2008
	Competing risk models for turtle nest survival in the Bolivian Amazon \| M.S. \| 05/2008
	Exploring bidder characteristics in online auctions: an application of a bilinear mixed model to study overbidders \| M.S. \| 08/2007
	Baseball prediction using ensemble learning \| M.S. \| 05/2007
	Adoption and use of Internet among American organic farmers \| M.S. \| 12/2007
	Population structure of loggerhead sea turtles (Caretta caretta) nesting in the southeastern United States inferred from mitochondrial DNA sequences and microsatellite loci \| M.S. \| 05/2007
	Small-sample prediction of estimated loss potentials \| M.S. \| 12/2007
	Applications for NIR spectroscopy in eucalyptus genetics improvement programs and pulp mill operations \| M.S. \| 12/2007
	Lq penalized regression \| M.S. \| 05/2007
	Estimating the demand for and value of recreation access to national forest wilderness: a comparison of travel cost and onsite cost day models \| M.S. \| 05/2007
	Implementing SELC (sequential elimination of level combinations) for practitioners: new statistical softwares \| M.S. \| 12/2006
	GIS-based habitat modeling related to bearded Capuchin monkey tool use \| M.S. \| 08/2006
	Historic airboat use and change assessment using remote sensing and geographic information system techniques in Everglades National Park \| M.S. \| 08/2006
	An evaluation of airbags \| M.S. \| 05/2005
	Mixed effects models for a directional response: a case study with loblolly pine microfibril angle \| M.S. \| 08/2005
	Cross-nation examination of CCI and CPI with an emphasis on Korea \| M.S. \| 05/2005
	A new nonparametric bivariate survival function estimator under random right censoring \| M.S. \| 05/2005
	Forecasting crop water demand: structural and time series analysis \| M.S. \| 08/2004
	Extreme value methods in body-burden analysis: with application to inference from long-term data sets \| M.S. \| 05/2004
	Development of a screening method for determination of aflatoxins \| M.S. \| 12/2004
	Regression models in standardized test prediction \| M.S. \| 08/2004
	Comparison between frequentist and Bayesian implementation of mixed linear model for analysis of microarray data \| M.S. \| 05/2004
	Temporal autocorrelation in modeling soil potentially mineralizable nitrogen \| M.S. \| 05/2004
	Using extreme value models for analyzing river flow \| M.S. \| 08/2004
	Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables \| M.S. \| 08/2004
	Monitoring expense report errors: control charts under independence and dependence \| M.S. \| 05/2004
	Time series analysis of volatility in financial markets in Hong Kong from 1991 to 2004 \| M.S. \| 12/2004
	Predictive modeling of professional figure skating tournament data \| M.S. \| 08/2003
	Statistical dimension reduction methods for appearance-based face recognition \| M.S. \| 05/2003
	Statistical analysis of 16s rdna gene-based intestinal bacteria in chickens \| M.S. \| 12/2003
	Reconstruction of early 19th century vegetation to assess landscape change in southwestern Georgia \| M.S. \| 12/2003
	Statistical model for estimating the probability of using electronic cards : a statistical analysis of SCF data \| M.S. \| 08/2003
	A survey of Hill's estimator \| M.S. \| 08/2003
	Statistical analysis of mass spectrometry-assisted protein identification methods \| M.S. \| 12/2003
	Intra-individual variation in serum vitamin A measures among participants in the Third National Health and Nutrition Examination Survey, 1988-1994 \| M.S. \| 05/2002
	Application and comparison of time series models to AIDS data \| M.S. \| 05/2002
	Are wealthier elderly healthier? : a statistical analysis of AHEAD data \| M.S. \| 08/2002
	Statistical modeling and analysis of the polymerase chain reaction \| M.S. \| 05/2002
	Statistical model for the diffusion of innovation and its applications \| M.S. \| 12/2002
	Spatial pattern analysis and modeling of Heterotheca subaxillaris and Lespedeza cuneata in a South Carolina old-field \| M.S. \| 08/2002
	Prediction of residential mortgage contract rates \| M.S. \| 05/2002
	Palmist: a tool to log Palm system activity \| M.S. \| 12/2001
	The grilseification of Atlantic salmon in Iceland \| M.S. \| 08/2001
	Stochastic volatility models: a maximum likelihood approach \| M.S. \| 08/2000

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