Simple random sampling, statistics and their sampling distributions, sampling distributions for normal populations; concepts of loss and risk functions; Bayes and minimax inference procedures; point and interval estimation; hypothesis testing; introduction to nonparametric tests; regression and correlation.

Introduction to methods of analyzing data from experiments and surveys; the role of statistics in research, statistical concepts and models; probability and distribution functions; estimation; hypothesis testing; regression and correlation; analysis of single and multiple classification models; analysis of categorical data.

Multivariate normal distribution, linear models in matrix notation, multiple linear regression (distribution results, categorical predictors, interactions, connection to ANOVA, sums of squares, diagnostics, ridge and nonparametric regression), Generalized linear models (binomial, poisson, and gamma regression), overdispersion, mixed models, diagnostics, professional presentation of results.

Convergence concepts (Central Limit Theorem), Sampling from a Normal Distribution, Order Statistics, Methods for finding point and interval estimates, methods for finding hypothesis tests, sufficiency principle, methods for evaluating point estimators (mean square error, unbiasedness, Carmer-Rao lower bound), Asymptotic of point estimates, interval estimates, and hypothesis testing procedures.

Definition and classification of stochastic processes, renewal theory and applications, Markov chains, continuous time Markov chains, queueing theory, epidemic processes, Gaussian processes.

Likelihood principles, sufficiency, natural conjugate and hierarchical priors, empirical Baysian analysis for estimation and testing.

Statistics (STA) 648 is an applied regression course that emphasizes data analysis and interpretation. Generally, regression is a collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables). This course aims to teach students about different regression models, their corresponding assumptions, and how to interpret the estimated models.

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.