Applied Statistics Minor

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations. Prerequisites: MAT 245, and one of the following: CIS 268, EGR 121, or STA 144. (3 units; Fall, even years)

Second semester course in a systematic development of the theories of probability and statistics. Topics include analysis of categorical data, multivariate distributions, nonparametric inference, linear models and analysis of variance. As time permits, the theory underlying Markov chain, Monte Carlo, quasi-likelihood, empirical likelihood, statistical functionals, generalized estimating equations, the jackknife, and the bootstrap are addressed. Prerequisite: STA 310. (3 units; Spring, odd years)

This course studies experimental designs with corresponding models and analyses critical for students in the empirical sciences. Course topics include estimation, test of hypothesis, analysis of variance and a variety of topics in experimental design. Decisions and practical considerations which minimize experimental error and avoid confounding results are dealt with in real life contexts. Prerequisite: STA 144. (3 units; Fall, odd years)

Sampling theory and practice are presented in this course through a study of simple random samples, stratified random samples, cluster sampling, estimating sample size, ratio estimates, subsampling, two-state sampling and analysis of sampling error. This is a critical course for students in education and the social, medical, biological and management sciences where sampling is a fundamental step in virtually every statistical procedure and critical to meaningful survey research. Prerequisite: STA 144. (3 units; Fall, even years)

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered. Prerequisite: STA 144. (3 units; Spring, even years)

A course to introduce statistical models of advanced least squares regression and standard ANOVA techniques and extensions to categorical data. Students will conceptually understand linear mixed effects models, log linear and generalized linear models for count data; and survival models for the analysis of lifetime data. In addition, students will apply these models to real data, to discern patterns and conclusions, and present their results. Prerequisites: STA 205 and STA 210. (3 units; Fall, even years)

A course in application of parametric and nonparametric single and multivariable data analytic techniques to sports. Topics will include: linear regression, correlation, confounding and interactions, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, discriminant analysis, and regression techniques with longitudinal data. Prerequisite: STA 210. (3 units; Spring, odd years)