Statistics (STA) 649 is an introduction to the principles of experimental design. Many statistics courses are taught from the perspective of analyzing data that has already been collected. However, problems that occur at the analysis stage (e.g., violations of assumptions, too small of sample, etc.) could have been avoided if the experimenter had consulted a statistician before the experiment was conducted and the data collected. This course will introduce common experimental designs so that when the data are collected, the aforementioned shortcomings are avoided.

The main objective of this course is to equip students with the traditional and modern multivariate statistical methods. Students will learn the motivation behind these methods, how to apply them and interpret the results obtained. Focus will be on understanding distributional results rather than the technical derivations. Students will gain competency in writing R scripts for applying the multivariate methods learned.

Statistics (STA) 651 discusses advanced programming techniques using the R language. Programming topics include how to handle various facets of data structures in R, how to produce simple and advanced graphics in R, and how to synthesize the necessary components of simulation studies.

This course aims to teach students to use advanced statistical modeling techniques and to interpret the results in context. Specifically, the statistical methods covered include general linear models and linear mixed models, semiparametric regression, nonlinear models, mixed models in ANOVA, generalized linear models, ridge regression, and repeated measures experiments.

This course provides an introduction to Bayesian inference and a summary of Bayesian methods for fitting, assessing, and selecting models. Topics include Bayes' Rule and Probability, Binomial Models for Proportions, Poisson Models for Counts, Normal Models for Continuous Data, Linear Regression, Log-linear and Contingency Tables, Hierarchical Models, Hypothesis Testing, Model Comparison, and Selected Applications.

Dimensions of quality, numerical and graphical descriptions of data, discrete and continuous distributions, basic reliability concepts, control charts for variables and attributes, process capability studies, and selected additional topics as time permits such as cusum charts, acceptance sampling.

Supervised reading, discussion, and practice of a selected statistical methodological area.

Theory and application of the bootstrap, jackknife and other resampling methods.

A continuation of STA 672. Multiplicative models in two-factor experiments. Partial factorials. Extensions and modifications of split plots and Latin squares. Confounding in factorial experiments. Response surface methods. Estimation of variance components. One restrictional and two restrictional lattice and incomplete block designs. Combining analyses of similar experiments.

Place of statistics in experimentation; topics in experimental design; response surfaces; departures from usual assumptions in analysis of variance; other selected topics. May be repeated to a maximum of nine credits.