The course introduces the student to statistical situations not encountered previously in courses of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include open-ended analysis of data, review of statistical literature on current techniques and practice of statistics, development of statistical communication skills, and the use of statistical software tools in data analysis. Students may work individually or in a group. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Student teams are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set.

This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include single-factor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs and topics as time permits.

A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.