The following course notes were from my Spring 2017 survey course in Regression for first-year graduate students. The materials were created in R markdown to supplement the verbal lectures. Most of the lectures simulate datasets to allow students to connect the data with how the analysis can be interpreted. Most of the simulation code is embedded into the lectures, but some were too complicated and are not shown (feel free to request them).

Warnings: These notes are likely full of errors (both statistical and grammatical) as this is the first time I taught this course and I am dyslexic. Further, this is the first time I taught a course exclusively through the R markdown system, so please excuse any roughness. Further, I had not planned to share these online, but the students requested I do so.

Corrections & Comments are appreciated!

Course Overview

Correlations and Linear Regression

Partial and Semipartial (part) Correlation

Multiple Regression

Stepwise and Hierarchical

Non-Linear Models

Interactions and Simple Slopes

Categorical Variables

Missing Data

Generalized Linear Model


Linear Mixed Models

Multi-Level Models

Exploratory Factor Analysis