Applied Statistics Minor

Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions.

Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics.

This course represents a basic concepts and methodology course in regression analysis using application of general linear regression models to real-life situations. Case studies are used to give practice in diagnosing practical problems, deciding on appropriate models, and knowing which inferential technique will answer the researcher's questions for the purposes of description and prediction. Regression models and model building typical of problems used in the social and behavioral sciences, the natural and health sciences, and many other disciplines are covered.

An introduction to data mining, management and statistical programming techniques using comprehensive and widely available tools like SAGE, SPSS, SAS and R. Students learn exploratory data analysis, coding and manipulation of variables, database management applying statistical concepts. Modeling and simulation experiments on a variety of applied data sets.

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.

econd 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.

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.

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.

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.

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.

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.