Lecture 1: Intro to regression and coding

Lecture 2: Data manipulation in R

Lecture 3: ggplot and R Markdown

Lecture 4: Simple linear regression and least squares properties

Lecture 5: Simple linear regression; least squares properties; MLR notation

Lecture 6: Multiple linear regression; interactions; categorical predictors

Lecture 7: Polynomial and spline models; least squares and properties

Lecture 8: Identifiability and collinearity; example analysis

Lecture 9: Inference for MLR, Part 1

Lecture 10: Inference for MLR, Part 2

Lecture 11: Resampling methods

Lecture 12: Gauss-Markov, MLE, regression diagnostics

Lecture 13: Model Checking

Lecture 14: Review

Lecture 15: Model Selection

Lecture 16: Penalized Regression

Lecture 17: Splines and Penalized Splines

Lecture 18: Additive Models; Case study

Lecture 19: Weighted and Generalized Least Squares

Lecture 20: Longitudinal Data Analysis

Lecture 21: LDA Interpretation, Estimation

Lecture 22: Random slope models, case studies

Lecture 23: Multilevel models

Lecture 24: Measurement error; mediation; confounding

Lecture 25: Logistic regression

Lecture 26: Writing Statistical Reports