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 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 25: Logistic regression
Lecture 26: Writing Statistical Reports