Topic 1: Experimental Design. Principles, construction, and analysis of experimental designs. Includes one-way classification, randomized blocks, Latin squares, factorial and nested designs, fixed and random effects, multiple comparisons, and analysis of covariance. Statistics and Data Sciences 385 (Topic 1) and Statistics and Scientific Computation 385 (Topic 1) may not both be counted.

Topic 2: Applied Regression. Examination of the foundation of regression analysis. Subjects include simple and multiple linear regression, residual analysis, transformations, building models with real data, testing models, logistic regression, and panel data analysis. Statistics and Data Sciences 385 (Topic 2) and Statistics and Scientific Computation 385 (Topic 2) may not both be counted. Additional prerequisite: Graduate standing, and one of the following: Public Affairs 397, Statistics and Data Sciences 380C (or Statistics and Scientific Computation 380C), 382 (or Statistics and Scientific Computation 382), or consent of instructor.

Topic 3: Applied Multivariate Methods. Introduction to the analysis of multivariate data as applied to examples from the social sciences. Includes multivariate linear models, principal components and factor analysis, discriminant analysis, clustering, and canonical correlation. Statistics and Data Sciences 385 (Topic 3) and Statistics and Scientific Computation 385 (Topic 3) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent.

Topic 4: Analysis of Categorical Data. Methods for analyzing categorical data. Includes categorical explanatory variables within the general linear model, models of association among categorical variables, and models in which the response variable is categorical or is a count. Emphasis on logical similarities across methods. Statistics and Data Sciences 385 (Topic 4) and Statistics and Scientific Computation 385 (Topic 4) may not both be counted.

Topic 5: Structural Equation Modeling. Introduction to the basic concepts, methods, and computing tools used in structural equation modeling. Designed to help students develop a working familiarity with some common statistical procedures and their application through the use of statistical software. Statistics and Data Sciences 385 (Topic 5) and Statistics and Scientific Computation 385 (Topic 5) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.

Topic 6: Hierarchical Linear Models. Introduction to multilevel data structures, model building and testing, effect size, fixed and random effects, missing data and model assumptions, hierarchical linear modeling (HLM) logistics, statistical power, and design planning. Statistics and Data Sciences 385 (Topic 6) and Statistics and Scientific Computation 385 (Topic 6) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.

Topic 7: Survey Sampling and Methodology. Survey planning, execution, and analysis. Includes the principles of survey research, including sampling and measurement; questionnaire construction and distribution; response effects; validity and reliability; scaling data sources; and data reduction and analysis. Statistics and Data Sciences 385 (Topic 7) and Statistics and Scientific Computation 385 (Topic 7) may not both be counted.

Topic 8: Introduction to Bayesian Methods. A practical introduction to Bayesian statistical interference, with an emphasis on applications in behavioral and measurement research. Examines how Bayesian statistical inference differs from classical inference in the context of simple statistical procedures and models, such as hypothesis testing, analysis of variance (ANOVA), and regression. Statistics and Data Sciences 385 (Topic 8) and Statistics and Scientific Computation 385 (Topic 8) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)) or the equivalent, or consent of instructor.

Topic 9: Longitudinal Data Analysis. Applications of models to data collected at successive points in time. Includes latent growth curve models, models for nonlinear growth, discrete-time and continuous-time event history models, multilevel models for change, random coefficient models, and applications of models to event-occurrence data. Statistics and Data Sciences 385 (Topic 9) and Statistics and Scientific Computation 385 (Topic 9) may not both be counted.

Topic 10: Modern Statistical Methods. Introduction to conducting statistical analysis using modern resampling methods, including bootstrapping and Monte Carlo simulation. Emphasis on theoretical understanding and application. Statistics and Data Sciences 385 (Topic 10) and Statistics and Scientific Computation 385 (Topic 10) may not both be counted.

Topic 11: Applied Mathematical Statistics. Introduction to the basic concepts of probability and mathematical statistics. Includes probability distributions and estimation theory and hypothesis testing techniques. Designed for doctoral students who plan to use statistical methods in their research but do not require a highly mathematical investigation of the subject. Statistics and Data Sciences 385 (Topic 11) and Statistics and Scientific Computation 385 (Topic 11) may not both be counted. Additional prerequisite: A calculus course covering integration and differentiation.

Topic 12: Meta-Analysis. Introduction to the statistics used to synthesize results from a set of studies. May include calculation of different effect sizes, calculating pooled estimates using fixed and random effects models, testing moderating variables using fixed and mixed effects models, testing heterogeneity of effect sizes, and assessing and correcting publication bias. Statistics and Data Sciences 385 (Topic 12) and Statistics and Scientific Computation 385 (Topic 12) may not both be counted.

Topic 13: Factor Analysis. Introduction to exploratory and confirmatory factor analysis. May include review of matrix algebra and vector geometry, principal components and principal axis factoring, and factor rotation methods, as well as single-factor and multiple-factor multisample models, multitrait-multimethod technique, and latent means modeling. Emphasis on critiquing current research. Statistics and Data Sciences 385 (Topic 13) and Statistics and Scientific Computation 385 (Topic 13) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.

Topic 14: Maximum-Likelihood Statistics. Introduction to the likelihood theory of statistical inference. Includes probability distributions, estimation theory, and applications of maximum-likelihood estimation (MLE) to models with categorical or limited dependent variables, even count models, event history models, models for time-series cross-section data, and models for hierarchical data. Statistics and Data Sciences 385 (Topic 14) and Statistics and Scientific Computation 385 (Topic 14) may not both be counted.

Topic 15: Survival Analysis and Duration Modeling. Focuses on the statistical methods related to the analysis of survival or of time to event data. Emphasis on practical applications in medicine, biology, economics, criminology, sociology, and engineering. May include Kaplan-Meier estimators, semiparametric and parametric regression models, model development, and model adequacy assessment. Statistics and Data Sciences 385 (Topic 15) and Statistics and Scientific Computation 385 (Topic 15) may not both be counted.

Topic 16: Statistical Models for Big Data. An introduction to approaches for modeling large data sets in the biological, social, and physical sciences. Subjects include: linear and generalized-linear models for very large data sets, graphical models, matrix factorization, latent-variable models, large-scale spatial smoothing, online learning, multiple testing, and convex optimization with applications to data analysis. Additional prerequisite: Basic knowledge of linear algebra and multivariable calculus; knowledge of basic probability and statistics at the level of Statistics and Data Sciences 321 and Mathematics 358K; some familiarity with linear regression modeling; programming experience in R, Python, Matlab, or a similar language.