MATH 239 Statistical Learning with R (1)
The general linear model is a fundamental tool frequently implemented by statisticians to describe the relationship between a quantitative response variable and one or more qualitative and/or quantitative explanatory variables. In this course, we will explore the implementation of the general linear model which will ultimately lead us to common model fitting techniques, including one-sample t-tests, two-sample t-tests, simple and multiple linear regressing, ANOVA, and ANCOVA. While theoretical results will occasionally be covered to provide necessary justification, the primary focus of the class will be on applying the aforementioned model fitting techniques to real data sets. The statistical software R will be used throughout the course to perform data analysis. Students enrolled in this course are presumed to have strong quantitative backgrounds and/or previous statistics experience.