MSER 524 - Regression Analysis and Predictive Modeling (3)

In this course we cover linear models in matrix notations and multiple linear regression models. Distribution results, categorical predictors, interactions, connection to ANOVA, sums of squares, diagnostics, remedial methods of common violation of model assumptions, and ridge and nonparametric regression will be discussed. Students will learn to understand the structure of linear regression models in theory, distinguish between regression and ANOVA models, identify the appropriate model for real-data analysis, and implement and correctly interpret real-data analysis with software. See Director.