Regression

Synopsis

This website contains the teaching materials for STAT 415/615 Regression, taught at American University by David Gerard.

Administrative

• Syllabus

Lectures

• 00 Motivation
  • Lecture 00-1: Linear Regression Overview.
• 01 Crash Course in R
  • Lecture 01-1: R introduction.
  • Lecture 01-2: R markdown introduction.
  • Lecture 01-3: R graphics with {ggplot2}.
  • Lab 01-1: R Lab.
• 02 Review
  • Lecture 02-1: Mathematical prerequisites.
  • Lecture 02-2: Probability and simulation.
• 03 Simple linear regression
  • Lecture 03-1: The Model.
    • Chapter 1 of KNNL.
  • Lecture 03-2: Inference.
    • Chapter 2 of KNNL.
  • Lecture 03-3: Diagnostics and Remedial Measures.
    • Chapter 3 of KNNL.
  • Lecture 03-4: SLR Case Study.
  • Lecture 03-5: Other Topics in SLR.
    • Chapter 4 of KNNL.
  • Lab 03-1: Inference Lab.
  • Lab 03-2: Diagnostics Lab.
• 04 Linear algebra
  • Lecture 04-1: Linear Algebra.
    • Chapter 5 of KNNL.
  • Lab 04-1: Linear Algebra Lab.
• 05 Multiple linear regression
  • Lecture 05-1: Multiple Linear Regression.
    • Model definition, interpretation, estimation, inference, assumptions.
    • Chapter 6 of KNNL.
  • Lecture 05-2: MLR and ANOVA
    • ANOVA perspective of hypothesis testing in the muliple linear regression model.
    • Sections 6.5, 6.9, 7.1, 7.2, and 7.3 of KNNL
  • Lecture 05-3: MLR: Practical Considerations.
    • Standardizing predictors, multicollinearity.
    • Chapter 7 of KNNL.
    • Chapter 12 of ROS.
  • Lecture 05-4: MLR: Quadratic Terms, Interaction Effects, and Categorical Variables.
    • Chapter 8 of KNNL.
  • Lecture 05-5: MLR: Model Selection.
    • Chapter 12 of Statistical Sleuth.
    • Chapter 9 of KNNL.
  • Lecture 05-6: MLR: More Complicated Model Diagnostics.
    • Chapter 10 of KNNL.
• 06 Logistic Regression
  • Lecture 06-1: Logistic Regression.
    • Chapter 20 of Statistical Sleuth
    • Sections 14.1–14.10 of KNNL
• Review Sheets
  • Equations.

Final Project

• Final Project Description.