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.

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

Course begins with an applied regression module that emphasizes analysis and interpretation of real data, and statistical computing. Second part of course focuses on principles and implementation of experimental design for scientific research purposes. Standard designs presented along with the proper kinds of analysis for each. Continued emphasis on real data and statistical computing using R and/or SAS.

After a brief review of population genetics theory, the course is divided into two sections which cover methods of estimating genetic variances and selection methods in population improvement. The course will focus on handling and interpretation of actual data sets through data analysis and discussion of current literature.

Survey of multivariate statistical techniques important in applied research. Focus on multivariate structure-seeking methods, but attention given to important hypothesis testing applications in ANOVA and MANOVA. Emphasis on implementation using modern statistical software and interpretation of results in context.

Basic principles of statistical consulting including how to manage a consulting session, how to formulate and solve problems and how to express results both orally and in writing. Students will be expected to analyze data from a current consulting project. Lecture, two hours; laboratory, two hours per week.

This course will involve students in small consulting projects intended to illustrate practical biostatistical problems.

Topics to be selected by STA faculty. May be repeated to a maximum of nine credits.

The Real Numbers (Natural Numbers and Induction, Ordered Fields, Ordered Fields, Topology of the Real Numbers, Compact Sets).

Basic concepts of decision theory, sufficiency and completeness; completeness of multiparametric exponential family; unbiasedness and invariance of decision rules; Bayes, minimax and invariant estimators; testing of hypotheses and optimality properties.