Applied Linear Modeling and Regression Analysis

Catalog description

Applications of the general linear model (GLM) typical in the social and behavioral sciences. Review of the foundations of statistical inference, including null hypothesis significance testing, confidence intervals, effect size, and statistical power. Theory and application of model comparisons, simple, multiple, and hierarchical multiple regression, incorporation of categorical predictors, interaction effects, diagnostics, and mediation analysis. Training in the use of the R statistical programming language. Mandatory weekly lab sessions. Assumes successful completion of an undergraduate course in psychological statistics (or equivalent).