Statistical software will be utilized for analyzing data and for simulating probability and sampling distributions. An introduction to probability modeling. Topics include a study of sample spaces, counting rules, conditional probability and independence, random variables and their properties, and applications. The course begins with a review of sampling, sampling distributions, and simple comparative experiments. Single factor experiments with both fixed and random effects are considered.
Designs illustrated include randomized blocks, latin squares and factorial experiments.
Mixed models and rules for expected mean square are presented. Model adequacy, sample size considerations, power determinations and restrictions on randomization procedures are discussed. The use of statistical software packages is integrated throughout the course. An introduction to least squares estimation in simple and multiple regression models. The matrix approach is used in the more general multiple regression model.