Statistics Department


Courses of Study

[SC110]    Statistical Thinking Statistics is the science of learning from data; it provides tools for understanding data and arguments based on data in many diverse fields. Students will learn to describe data in basic terms and to verbalize interpretations of it. Topics include graphical and numerical methods for summarizing data, methods of data collection, basic study design, introductory probability, confidence intervals, and statistical inference. Does not count toward any major or minor. Four credit hours. Q.
SC212fs    Introduction to Statistics and Data Science An exploration of statistical methods relevant to a broad array of scientific disciplines. Students will learn to properly collect data through sound experimental design and to present and interpret data in a meaningful way, making use of statistical computing packages. Topics include descriptive statistics, design of experiments, randomization, contingency tables, measures of association for categorical variables, confidence intervals, one- and two-sample tests of hypotheses for means and proportions, analysis of variance, correlation/regression, and nonparametrics. Prerequisite: Sophomore standing or above. Four credit hours. Q, W2. Bontea, Wieczorek, Zeldow
SC306f    Topics in Epidemiology The purposes of epidemiological research are to discover the causes of disease, to advance and evaluate methods of disease prevention, and to aid in planning and evaluating the effectiveness of public health programs. Students will learn about the historical development of epidemiology, a cornerstone of public health practice. Through the use of statistical methods and software, they will explore the analytic methods commonly used to investigate the occurrence of disease. Topics include descriptive and analytic epidemiology; measures of disease occurrence and association; observational and experimental study designs; and interaction, confounding, and bias. Prerequisite: Statistics 212. Four credit hours. Scott
[SC308]    Topics in Psychometrics and Multivariate Statistics Psychometrics is concerned with the development and evaluation of psychological instruments such as tests and questionnaires. Students will learn about the fundamental concepts central to measurements derived from these tools. The establishment and assessment of the validity and reliability of research instruments, as well as the construction of scales and indices, will be discussed. Data reduction techniques and an introduction to testing theory will also be covered. Statistical software will be used throughout. Prerequisite: Statistics 212 and Mathematics 253 (may be taken concurrently). Four credit hours.
[SC310]    Applied Longitudinal Analysis Longitudinal data occur when the same response is measured repeatedly through time. Students in this course will learn the fundamental properties of the structure of longitudinal data, as well as standard regression and mixed modeling strategies to analyze them. The types of estimation, and implications for using them, will also be discussed. Statistical software will be used throughout the course. Prerequisite: Statistics 212 and Mathematics 253 (may be taken concurrently). Four credit hours.
SC321fs    Statistical Modeling Students will expand on their inferential statistical background and explore methods of modeling data through linear and nonlinear regression analysis. Through the use of statistical software, they will learn how to identify possible models based on data visualization techniques, to validate assumptions required by such models, and to describe their limitations. Topics include multiple linear regression, multicollinearity, logistic regression, models for analyzing temporal data, model-building strategies, transformations, model validation. Prerequisite: Statistics 212. Four credit hours. Scott, Wieczorek
SC323s    Statistical Surveys, Censuses, and Society Revolves around the role of sampling and surveys in the context of U.S. society. We will examine the evolution of census- and survey-taking in the U.S. in the context of its economic, social, and political uses, eventually leading to discussions about the accuracy and relevance of survey responses, especially in light of various kinds of sampling and nonsampling errors. We will also explore links to sampling methods useful for studying wildlife, forests, and other non-human populations. Students will be required to design, implement, and analyze a survey using rigorous, well-motivated methods. Previously offered as Statistics 397 (Fall 2019). Prerequisite: Statistics 212. Four credit hours. Wieczorek
SC324s    Statistical Learning in Data Science Statistical methods used in data science allow computers to make inferences and predictions about target variables. This course will provide students exposure to the common statistical methods and models used in this setting. Although the emphasis is on applications, the statistical and mathematical foundations for these data science techniques will also be covered. Topics will include linear modeling and classification techniques, cross validation, bootstrapping, non-linear modeling, tree-based methods, and data reduction strategies. Unsupervised learning techniques will also be covered as time allows. Prerequisite: Statistics 212 and Mathematics 253 (may be taken concurrently). Four credit hours. Scott
SC326f    Statistical Graphics and Principles of Visualization An effective statistical graphic is a powerful tool for analyzing data and communicating insights. From tabular to geospatial and network datasets, students will learn to create and interpret visualizations that show the raw data, statistical models of that data, and the statistical precision of those summaries. Students will also apply principles of human visual processing and data science workflows to ensure their statistical graphics are effective and reproducible. With the help of the tidyverse, ggplot2, rmarkdown, and shiny R packages, students will create static and interactive graphics, culminating in an interactive data dashboard. Previously offered as Statistics 398 (Spring 2021). Prerequisite: Statistics 212. Four credit hours. Wieczorek
SC327f    Bayesian Statistics An introduction to Bayesian statistics. We will cover topics such as Bayes Theorem, prior and posterior distributions, linear regression, hierarchical models, and statistical inference using Bayesian methods. We will also make extensive use of R to implement these methods. Prerequisite: Mathematics 381. Four credit hours. Zeldow
SC381fs    Probability Listed as Mathematics 381. Four credit hours. Randles
SC482s    Topics in Statistical Inference Building on their background in probability theory, students explore inferential methods in statistics and learn how to evaluate different estimation techniques and hypothesis-testing methods. Students learn techniques for modeling the response of a continuous random variable using information from several variables using regression modeling. Topics include maximum likelihood and other methods estimation, sample properties of estimators, including sufficiency, consistency, and relative efficiency, Rao-Blackwell theorem, tests of hypotheses, confidence, and resampling techniques. Prerequisite: Mathematics 381. Four credit hours. Zeldow
SC491f, 492s    Independent Study One to four credit hours.