Stats Courses at Tufts

Tufts University offers a number of courses on statistics and qualitative analysis. These courses are taught throughout all three campuses and hosted in a number of different schools and departments. Below is an overview of available courses:

The School of Arts & Sciences and School of Engineering

Advanced Statistics I – PSY – 0207 – Fall
Advanced Statistics II – PSY – 0208 – Spring
Applied Regression Analysis – CSHD – 0249 – Spring
Multilevel Modeling in Developmental Science – CSHD – 243 – Fall
Structural Equation Modeling – CSHD – 252 – Fall
Biostatistics – BIO – 0132 – Fall
Fundamentals of Biostatistics – CH – 0036 – Fall
Introductory Statistics – MATH – 0021 – Fall / Spring
Introduction to Programming for Math – MATH – 0022 – Fall
Statistics – MATH – 0166 – Fall / Spring
Political Science Research Methods – PS – 0103 – Fall / Spring
Data Analysis and Statistical Methods – CEE – 0202 – Fall
Public Health – CEE – 0057 – Fall
Fundamentals of Biostatistics – CEE – 0006 – Fall
Quantitative Research Methods – SOC – 0101 – Fall
Qualitative Research Methods – SOC – 0102 – Fall
Quantitative Reasoning – UEP – 0254 – Fall / Spring
Urban Analytics & Visualization – UEP – 0237 – Fall
Statistics – EC – 0013 – Fall / Spring
Statistics for Quantitative Economics – EC – 0014 – Fall
Econometric Analysis – EC – 0107 – Spring
Statistics for Econometrics – EC – 0201 – Fall
Introduction to Data Analytics – EM – 0202 – Fall / Spring
Advanced Topics Data Analytics – EM – 0213 – Fall / Spring
Applied Data Science – EM – 0212 – Spring
Data Visualization & Communication – EM – 0251 – Fall

The Graduate School of Biomedical Sciences & The Friedman School of Nutrition Science and Policy

Biostatistics I – CTS – 0527– Fall
Introduction to Biostatistics II – CTS – 0507 – Fall
Predictive Models – CTS – 0510 – Fall
Advanced Topics in Biostatistics – CTS – 0533 – Fall
Advanced Epidemiology & Regression – CTS – 0575 – Spring
Biomedical Data Science – CTS – 0599 – Spring
Biostatistics I – NUTR – 0206 – Fall
Biostatistics II – NUTR – 0309 – Spring
Data Management Using SAS – NUTR – 0237 – Fall
Advanced Data Analysis – NUTR – 0394 – Fall
Statistical Methods in Nutrition Science and Policy – NUTR – 0207 – Fall
Regression Analysis for Nutritional Policy – NUTR – 0307 – Spring
Statistical Methods I – NUTB – 0250 – Fall

The Fletcher School of Law and Diplomacy

Analytic Frameworks in Public Policy – DHP – P203 – Fall
Quantitative Methods – EIB – E210M – Fall
Data Analysis & Statistical Methods – EIB – B205 – Fall
Statistical Methods in Business – EIB – B206 – Spring
Marketing Research & Analysis – EIB – B262 – Spring
Econometrics – EIB – E213 – Spring

The Cummings School of Veterinary Medicine

Statistics I – APP – 516 – Fall
Statistics II: Intermediate – APP – 517 – Spring


The School of Arts & Sciences and The School of Engineering

Advanced Statistics I – PSY – 0207
Offered: Fall
Instructor: Daniel Barch

Development of statistical concepts for the design and analysis of research. Consideration of the logic of statistical inference, analysis of variance, and nonparametric analysis.


Advanced Statistics II – PSY – 0208
Offered: Spring
Instructor: Daniel Barch

Consideration of certain multivariate designs, regression, and the analysis of covariance.


Applied Regression Analysis – CSHD – 0249
Offered Spring
Instructor: Sara Johnson

(Cross-listed w/DEIJ 276). This course covers the specification, conduct, and evaluation of various regression-based (general linear model) techniques used in developmental science and associated disciplines. Topics include models with continuous predictors, categorical predictors (including dummy codes), and interactions, as well as extensions of the general linear model such as logistic regression. Students must have a solid background in intermediate statistics, including analyses of variance. Prerequisite: CSHD 146 or equivalent intermediate statistics course with a focus on social and behavioral sciences.


Multilevel Modeling in Developmental Science – CSHD – 243
Offered: Fall
Instructor: Sara Johnson

Guided individual study of an approved topic. Please contact the department for detailed information.Please see departmental website for specific details.


Structural Equation Modeling – CSHD – 0252
Offered Fall
Instructor: Sara Johnson

(Cross-listed w/DEIJ 277) This course will provide an introduction to the theory and application of structural equation modeling (SEM) as it applies to developmental science. The course assumes that you have already completed courses in regression and multivariate statistics. The goal of this course is to have students be able to construct, analyze, modify, and test the adequacy of a variety of structural equation models and report the results of their analyses in a manner acceptable in refereed journals.


Biostatistics – Bio – 0132
Offered Fall
Instructor: Helen McCreery

An examination of statistical methods for designing, analyzing, and interpreting biological experiments and observations. Topics include probability, parameter estimation, inference, correlation, regression, analysis of variance, and nonparametric methods. (Group Q.) Required: BIO 13 and 14, or equivalent, plus one additional biology course above BIO 14.


Fundamentals of Biostatistics – CH – 0034
Offered Spring
Instructor Mark Woodin

(Cross listed as CEE 6) Examination of statistical methods used in biomedical and public health studies. Descriptive statistics, probability, basic hypothesis testing, ANOVA, linear regression, logistic regression, and an introduction to survival analysis. Instruction in the use of statistical software will be provided throughout the course. CEE 6 and CEE 156 cannot both be taken for credit


Introductory Statistics – MATH – 0021
Offered Fall / Spring
Instructor: Gail Florence Kaufmann, Wesley Patrick Clawson

Descriptive data analysis, sampling and experimentation, basic probability rules, binomial and normal distributions, estimation, regression analysis, one and two sample hypothesis tests for means and proportions. The course may also include contingency table analysis, and nonparametric estimation. Applications from a wide range of disciplines. Recommendations: High school algebra and geometry.


Introduction to Programming – MATH – 0022
Offered Fall
Instructor: Marshall Mueller

For students without programming backgrounds. Basics of organizing and implementing algorithms and programs in programming languages relevant to computational mathematics and scientific computing, such as Python and Matlab. Emphasis on key concepts needed for programming in any language.


Statistics – MATH – 0166
Offered Fall
Instructor Merek Johnson

Statistical estimation, sampling distributions of estimators, hypothesis testing, regression, analysis of variance, and nonparametric methods. Recommendations: MATH 165 or permission of instructor.


Political Science Research Methods – PS – 0103
Offered Fall / Spring
Instructor: Deborah Schildkraut

(Cross-listed w/ CVS 148). The study of quantitative methods for investigating political issues and policy controversies. Focuses on collecting, analyzing, and presenting data. Emphasizes hands-on training that provides useful skills for academic and professional settings. Topics covered include: measurement, hypothesis development, survey design, experiments, content analysis, significance tests, correlation, and regression. No prior statistics background necessary. Prerequisites: PS 11, 21, 41, 42, or 61. A methodologically focused course.


Data Analysis & Statistical Methods – CEE – 0202
Offered Fall
Instructor: Shafiqul Islam

Exploratory data analysis, descriptive statistics, representativeness of samples, inferential statistics, statistical estimation, sampling distributions of estimators, residual analysis, and confidence intervals. Recommendation: CEE201


Public Health – CEE – 0057
Offered Fall
Instructor: David Gute

An introduction to the public health approach is provided. The epidemiological model of the disease process is used to study a variety of infectious and noninfectious diseases. The wide variety of nonmedical approaches to disease control is emphasized. The public health aspects of vital statistics, evaluation, and administrative decision making are introduced and applied to current problems in public health. Recommendations: Consent of instructor.


Fundamentals of Biostatistics – CEE – 0006
Offered Fall / Spring
Instructor: Mark Woodin

(Cross listed as CH 36) Examination of statistical methods used in biomedical and public health studies. Descriptive statistics, probability, basic hypothesis testing, ANOVA, linear regression, logistic regression, and an introduction to survival analysis. Instruction in the use of statistical software will be provided throughout the course. CEE 6 and CEE 156 cannot both be taken for credit


Quantitative Research Methods – SOC – 0101
Offered Fall
Instructor: Felipe Dias

Data analysis and statistics for the social sciences. Sampling, describing data, and logic of inference, especially with surveys. Introduction to microcomputer tools for analysis and graphic display. Answering research questions through individual or group projects. Recommendations: One introductory social science course.


Qualitative Research Methods – SOC – 0102
Offered Fall
Instructor: Helen Marrow

Advanced course specializing in qualitative methods (e.g., ethnographic observation and in-depth interviewing). Epistemological foundations of qualitative research design, sampling, and methods, including related ethical issues. Development of an original research project, including formulation of a researchable sociological question, review of the sociological literature, identification of field sites, development of consent forms and interview guides, completion of systematic observations and qualitative interviews, coding and analysis of data, and development of a clear sociological argument and final paper simulating a published academic journal article. Strongly recommended for students interested in conducting independent qualitative research (e.g., senior honors theses).


Quantitative Reasoning – UEP – 0254
Offered Fall
Instructor: Shomon Shamsuddin

Required core course for M.A. and M.P.P. students. Introduction to the use of quantitative thinking. Designed to develop basic statistical skills as indispensable tools for policy research, planning and decision making. Students learn how to choose and apply statistical tools to data sources, when and how statistical tools can be used to analyze data, and how to interpret and understand others’ quantitative research. Statistical software is used to facilitate learning through active application of statistical tools. Although prior coursework in statistics is not required, students are required to have a solid understanding of college-level algebra. Waiver permitted for students with an undergraduate major or substantial work-related experience in statistics subject to faculty approval. Recommendations: College-level algebra


Urban Analytics & Visualization – UEP – 0237
Offered Fall
Instructor: Shan Jiang

With rapid urbanization, the development of data science, machine learning, and the emergence of ubiquitous sensing technologies, cities have become the foci of multidisciplinary investigations. This course is designed to equip future planners, data scientists, and policymakers with computational methods and tools to acquire new urban data from social media, crowdsourcing, and sensor networks, and use them to represent, understand, and visualize complex urban environments in comprehensive and scientific ways, to make informed decisions to design, plan and manage smart, sustainable and resilient cities. Prerequisites: (1) Intro to GIS; (2) UEP Coding Bootcamp (offered in Su/Fall/Spring) or an equivalent Data Lab Workshop.


Statistics – EC – 0013
Offered Fall / Spring
Instructor: Thomas Downes

An introduction to basic statistical techniques that are used in economic analysis. Major topics include probability, discrete random variables, continuous random variables, sampling distributions, estimation, and hypothesis testing. The course will conclude with some theory and applications of the linear regression model. Required of all economics majors. Prerequisites: Economics 5 or Economics 8; and Mathematics 32 or above.


Statistics for Quantitative Economics – EC – 0014
Offered Fall
Instructor: Marcelo Bianconi

Mathematical statistical techniques used in econometric analysis. Topics include probability theory, discrete and continuous random variables and their joint distributions, sampling and estimation, hypothesis testing, rudiments of statistical programming, and brief introduction to the theory of linear regression. Foundation course for students pursuing the quantitative economics major track. Economics 13 and 14 may not both be taken for credit. Prerequisites: one of EC 5 or EC 8; and MATH 34 or MATH 39 or above.


Econometric Analysis – EC – 0107
Offered Fall
Instructor: Silke Forbes

The study of multiple regression models and their applications. Focus on the properties of estimation techniques when the classical regression assumptions hold and when they do not hold. Topics include least squares estimation, instrumental variable estimation, panel data techniques, and time-series techniques. EC 15 and 107 may not both be taken for credit.


Statistics for Econometrics – EC – 0210
Offered Fall
Instructor: Adam Storeygard

Topics from probability and mathematical statistics that provide a foundation for econometrics. Includes elements of probability theory, estimation, hypothesis testing, and statistical programming. Emphasizes conditional expectations, and population vs. sample interpretations of bivariate regression. Offered in the Fall only. Prerequisites: Enrollment in the economics graduate program or instructor permission


Introduction to Data Analytics – EM – 0202
Offered Fall / Spring
Instructor: Kishore Pochampally

Data identification, analysis and interpretation to drive strategic and operational success in technology-based companies. Data-based decisions with uncertain or ambiguous conditions, and development of models for decision-making in a business setting.


Advanced Topics Data Analytics – EM – 0213
Offered Fall / Spring
Instructor: Kishore Pochampally

Use of established software frameworks to support applied projects in data science. Topics include: PyMC3, PySpark, NLP tools, PyTorch, TensorFlow, Keras, Flask and Docker. Pre-requisite EM206 or instructor consent.


Applied Data Science – EM – 0212
Offered Spring
Instructor: Kyle Monahan

Foundational skills for data science practice in business. Using data and systematic methods to generate business value. Shaping data practices within organizations.


Data Visualization & Communication – EM – 0251
Offered Fall
Instructor: Kyle Monahan

Concepts of visual analytics such as visual reports and dashboards with a hands-on tutorial using leading self-service business intelligence and data visualization tools. Hands-on exercises to identify datasets, explore, analyze, filter and structure data to communicate via visualizations.


The Graduate School of Biomedical Sciences & The Friedman School of Nutrition Science and Policy

Biostatistics I – CTS – 0527
Offered Fall
Instructor: Angie Rodday, Paola Sebastiani

This course introduces basic principles and applications of statistics to problems in clinical research. Topics covered include descriptive statistics, probability and random variation, sampling, hypothesis testing, proportions, measures of frequency, t-tests, chi-square tests, one-way analysis of variance, correlation, linear regression and nonparametric statistics.


Introduction to Biostatistics II – CTS – 0507
Offered Fall
Instructor: Sarah Pagni

This course is the second half of a two-part course which presents the practical application of biostatistical methods for exploring and analyzing health data. Methods for working with data and exploring basic associations are presented through case examples and clinical research projects. CTS 0506 and 0507 are considered equivalent to 0527.


Predictive Models – CTS – 0510
Offered Fall 
Instructor: David Kent, Jason Nelson

This course explores the use of statistical models to predict clinical outcomes for retrospective review and as prospective decision aids. Emphasis is placed on integrating statistical and clinical thinking to construct models that are both statistically and clinically sound and that give accurate predictions when generalized to other populations.


Advanced Topics in Biostatistics – CTS – 0533
Offered Fall 
Instructor: Angie Rodday, Farzad Noubary

This course provides background in advanced applied statistical methods in clinical research. Topics in the course include Poisson, multinomial, and ordinal regression, competing risk survival models, longitudinal data analysis, and hierarchical mixed models. The course provides students with the statistical foundations of these methods and their applications in clinical research.


Advanced Epidemiology & Regression – CTS – 0575
Offered Spring 
Instructor: Angie Rodday, Paola Sebastiani, Robert Goldberg

This course serves as an introduction to more advanced topics in epidemiologic study design and biostatistical modeling with a focus on multivariate regression methods. It begins with the randomized clinical trial as a paradigm, and proceed to examine observational designs in depth, including prospective and retrospective cohorts, and those sampling from an underlying cohort (i.e. case-control). Design, sampling and analysis strategies and the biases that are specific to each study design will be discussed.


Biomedical Data Science – CTS – 0599
Offered Spring 
Instructor: Paola Sebastiani

This course provides an overview of the analysis methods of genetic and genomic data, as well as integration of diverse ‘omics data. Topics include an introduction to high-throughput technologies for the generation of genetics and genomic data, including DNA variants and gene expression data that can be measured though next generation sequencing technologies, proteomics and metabolomic data, and the microbiome. Main stream methods of analysis for each data type, as well as their integration, will be described. A final project with will involve analysis of a publicly available dataset.


Biostatistics I – NUTR – 0206
Offered Fall
Instructor: Angie Rodday, Paola Sebastiani

This course introduces basic principles and applications of statistics to problems in clinical research. Topics covered include descriptive statistics, probability and random variation, sampling, hypothesis testing, proportions, measures of frequency, t-tests, chi-square tests, one-way analysis of variance, correlation, linear regression and nonparametric statistics. This course has a required Laboratory (NUTR 0206.1L) linked to the NUTR 0206.01 course. NOTE: Students cannot receive semester hour units for both NUTR 0206: Biostatistics I and its counterpart NUTR 0207: Statistical Methods of Nutrition Science and Policy (or PH 0205: Principles of Biostatistics). Prerequisites: Graduate standing or instructor consent. Please bring laptop to Lecture class sessions and Lab class sessions.


Biostatistics II – NUTR – 0309
Offered Spring
Instructor: Mei-Chun Chung

This course is part two of a one-year, two-semester course on statistical methods for nutrition research. The focus of this course is on simple and multiple regression methods for continuous, binary, and survival data. Emphasis is on developing a conceptual understanding of the application of these techniques to solving problems rather than on numerical details. In the computer lab sessions, students will use concepts learned during lecture to analyze data using statistical software R and RStudio, an integrated development environment for R. NOTE: Students cannot receive semester hour units for both NUTR 309 and NUTR 307: Regression Analysis for Nutrition Policy. Prerequisites: Biostatistics I (NUTR 0206) or Statistical Methods for Nutrition Science and Policy (NUTR 0207) or equivalent, and graduate standing or instructor consent. Ability to conduct exploratory data analysis using R. Students who have not taken Principles of Epidemiology (NUTR 0204) or an equivalent course are strongly encouraged to take Principles of Epidemiology (NUTR 0204) concurrently with NUTR 0309.


Data Management Using SAS – NUTR – 0237
Offered Fall 
Instructor: Gail Rogers

This semester long course will provide participants with sufficient knowledge to manage data from collection to analysis in SAS for Windows. Emphasis will be placed on the data life cycle, database structures, cleaning, manipulation, documentation, and dynamic reporting of data. Upon completion, students should be able to be able manage a small project from start to finish, providing well documented reproducible code to generate a report, codebook and data set ready for data analysis. Macro programming, structured query language (SQL), public data sets and reporting tools commonly used in nutrition analysis will be utilized during the semester. In-class exercises and weekly homework assignments will allow students to acquire hands-on experience solving common data management tasks in SAS. Prerequisite: Graduate standing or instructor consent.


Advanced Data Analysis – NUTR – 0394
Offered Fall
Instructor: Elena Naumova, Ryan Simpson 

This project-based course capitalizes on student interests to formulate research questions with understanding of data limitations, conduct multi staged data analysis, and select proper data visualization and graphical representation tools. Students will learn advanced modern analytical tools and techniques essential for analysis in a variety of disciplines such as Climate, Environment, Nutrition and Health applications (knowledge of only one of these disciplines. This course also covers research design, the scientific method, data quality and validity, data management, and research ethics in data analysis. Students should attempt to identify data sets relevant to their specific interests prior to the course. Instructor will approve data set suitability. If students cannot identify appropriate datasets, the instructor will provide a dataset. Designated time outside of the classroom is required for each student to work with the team partner to provide and receive feedback on homework assignments. Prerequisites: Students should have basic working knowledge of statistical methods in environmental and/or nutrition research and epidemiology. Recommended courses that cover those topics include: Biostatistics I and II (NUTR 0206/0309) or Statistical Methods in Nutrition Research and Regression Analysis for Nutrition Policy (NUTR 0207/NUTR 0307) or equivalent. Ability to analyze data by use of R is preferable, but students may utilize other statistical programs as long as those programs are sufficient for the analysis that is proposed.


Statistical Methods in Nutrition Science and Policy – NUTR – 0207
Offered Fall
Instructor: Anastasia Marshak

In this class we will explore statistical techniques for analyzing social science data, with specific applications to nutrition, food policy, agriculture and the environment. Although it is necessary to teach some theory, this course is meant to be practical and user oriented. The primary goal here is to learn how to analyze data in ways that will be useful in your academic and professional careers, both in conducting your own work and critically assessing the work and claims of others.For most students, this course is the first part of a year-long sequence. This is a first semester graduate course in statistics that is required for students in the AFE, FANPP, and NICBC programs. This one-semester course will provide students with an introductory level understanding of social science statistics concepts and methodologies, and how and why they are applied. Topics will include data gathering, experimental design, probability, descriptive statistics, graphical displays, hypothesis testing, nonparametric tests, analysis of variance, correlation, and simple linear regression. A distinctive feature of this course is its focus on methods that can be used with observational data, which frequently arise in the social sciences. Prerequisite: Graduate standing or instructor consent.


Regression Analysis for Nutritional Policy – NUTR – 0307
Offered Spring 
Instructor: Parker Wilde

Part two of a one-year, two-semester course sequence in statistics. This course is intended for students whose main focus is non-experimental or survey-based research. The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non-linear functional forms, heteroskedasticity, complex survey designs, and real-world statistical applications in nutrition science and policy. Students will make extensive use of Stata for Windows. NOTE: Students cannot receive credit for both NUTR 0307 and NUTR 0309. Pre-requisites: NUTR 0207 or NUTR 0206 and graduate standing or instructor consent.


Statistical Methods I – NUTB – 0250
Offered Fall 
Instructor: Anastasia Marshak

Students will critically evaluate, compare, interpret, judge, summarize and explain statistical results published in research articles in health and nutrition journals from the United States and around the world that are influencing the practice of nutrition science, policy and research. Students learn and use Stata® statistical software for their homework.



The Fletcher School of Law and Diplomacy

Analytic Frameworks in Public Policy – DHP – P203
Offered Fall
Instructor: Carolyn Gideon

Introduction to the basic tools of policy analysis and decision making, providing students with analytic skills to make policy decisions in many types of organizations. The course includes an introduction to public policy objectives, decision making, and the role of analysis. Students then learn powerful analytic decision-making techniques, including decision trees, Bayes theorem, utility theory, prospect theory, game theory, benefit-cost analysis, and tipping models. Case studies are used to learn the policy analysis tools while applying them to real world policy problems. Cases come from developed and developing countries, and cover many different policy fields. No background in economics or statistics is required.


Quantitative Methods – EIB – E210M
Offered Fall
Instructor: Michael Klein

This module presents the mathematical methods that are used widely in economics, including logarithms, exponential functions, differentiation, optimization, constrained optimization, and an introduction to dynamic analysis. The mathematical material is presented in the context of economic applications and examples that illustrate the bridge between mathematics and economics. One-half credit.


Data Analysis & Statistical Methods – EIB – B205
Offered Fall
Instructor: Robert Nakosteen

This course provides an overview of classical statistical analysis and inference. The language and methods of statistics are used throughout the Fletcher curriculum, both in the classroom and in assigned readings. In addition, the language and methods of statistical analysis have permeated much of academic and professional writing, as well as media reporting. The goal is to present a broad introduction to statistical thinking, concepts, methods, and vocabulary.


Statistical Methods in Business – EIB – B206
Offered Spring
Instructor: Robert Nakosteen 

This course provides an overview of classical statistical analysis and inference. The goal is to provide you with an introduction to statistical thinking, concepts, methods, and vocabulary. This will give you some tools for dealing with statistical methods you may encounter in your coursework or research while at The Fletcher School, especially “regression analysis,” which is covered at the end of the course. In addition, this section of the course has a particular emphasis on business applications. Students who plan to or have completed B205 are not permitted to take this course.


Marketing Research & Analysis – EIB – B262
Offered Spring 
Instructor: Bernard Simonin

This course adopts a comprehensive hands-on approach to designing and conducting research. From classic opinion research to social media analytics, a wide range of contexts, problem areas, and methods are covered that are relevant across disciplines and fields of study. Students will be exposed to the various stages of the research process from recognizing the need for research and defining the problem to analyzing data and interpreting results. Proper design of research methods, fieldwork, questionnaires, and surveys (e.g., online surveys) is covered. Both qualitative (e.g., focus groups, projective techniques) and quantitative approaches (e.g., cluster, discriminant, and factor analysis) are presented. Various analytical techniques are introduced “hands on” via a series of computer exercises and cases (using SPSS and Excel).


Econometrics – EIB – E213
Offered Spring
Instructor: Julie Schaffner

This course introThis course introduces students to the primary tools of quantitative data analysis employed in the study of economic and social relationships. It equips students for independent econometric research and for critical reading of empirical research papers. The course covers ordinary least squares, probit, fixed effects, two-stage least squares and weighted least squares regression methods, and the problems of omitted variables, measurement error, multicollinearity, heteroskedasticity, and autocorrelation. Prerequisites include familiarity with (1) basic probability and statistics (B205), and (2) basic concepts of functions and derivatives (E210m or an introductory calculus course).



The Cummings School of Veterinary Medicine

Statistics I – APP 516
Offered Fall
Instructor: Adam South, Jada Ford

This course introduces students to the basics of statistical methods and research design. Students learn to state hypotheses, evaluate sampling procedures, create and manage data sets, and carry out basic statistical testing. Examples are drawn from research in veterinary medicine, animal science, human-animal relationships, and animal ecology.


Statistics II: Intermediate – APP 517
Offered Spring
Instructor: Adam South

Intended for advanced research track students and tailored to their interests, this course will focus on experimental design and analysis of survey data, exploring the use of analysis of variance (ANOVA) and regression models, factor analysis, and other advanced techniques using SPSS or an equivalent statistical package.