RDA1_logo

Regression Analysis

This class is all about applying regression analysis and linear models, including generalized linear models, mediation and moderation, with a little bit of machine learning techniques thrown in. The book we’ll use throughout the class, and that drives the structure of the lecture slides, is Regression Analysis and Linear Models by Richard Darlington and Andrew Hayes. This course uses R and RStudio for all data analyses.

We will use several different data sets during the course:

We may also pull from FiveThirtyEight’s open data on GitHub occassionally throughout the class (many of these data sets can be used for your class project as well if they have both continuous and categorical predictors).

Syllabus

Class Materials

Unit 1

In-Class Material RMD

Lecture Slides and Materials Recorded Lecture
L0: Intro to the class PPTX  
L1: Intro to R and RStudio HTML Intro to R
L2: Causation PPTX Causation
L3: Simple Regression PPTX Simple Regression
L4: Multiple Regression PPTX Multiple Regression
L5: Categorical Predictors PPTX Categorical Predictors

Unit 2

In-Class Material RMD

Lecture Slides and Materials Recorded Lecture
L6: Statistical Inference PPTX Inference Part 1 & Inference Part 2
L7: Model Diagnostics PPTX Diagnostics Part 1 & Diagnostics Part 2
L8: Missing Data & Such PPTX Missing Data
L9: Threats to Validity PPTX Threats to Validity

Unit 3

In-Class Material RMD

Lecture Slides and Materials Recorded Lecture
L10: Effect Sizes PPTX Effect Size
L11: Linear Interactions PPTX Interactions Part 1 & Interactions Part 2
L12: Nonlinear Relationships PPTX Nonlinear
L13: Intro to GLMs PPTX Intro to GLMs

Unit 4

In-Class Material RMD

Lecture Slides and Materials Recorded Lecture
L14: Logistic Regression PPTX Logistic Regression
L15: Other GLMs PPTX Other GLMs
L16: Mediation Analysis HTML Mediation Analysis
L17: Miscellaneous PPTX Recorded Lecture

Final Project