[S=Slides | A=Annotated Slides | R=R Code]

Source files for all notes are available from the master branch of the GitHub repository for this course.

• Class 1: Course Introduction
• Class 2: Introduction to Regression [ S | A ]
• Class 3: Geometry of regression and least squares [ S | A ]
• Class 4: Hands-on SLR practice
• Class 5: R^2, ANOVA [ S | A ]
• Class 6: Version Control [ S ]
• Class 7: Introduction to Multiple Linear Regression [ S | A | R ]
• Class 8: [cancelled, snow day]
• Class 9: MLR: Notation and Estimation [ S ]
• Class 10: MLR: Hat Matrix, Identifiability, Collinearity [ S | A ]
• Class 11: MLR: Categorical variables [ S | A ]
• Class 12: MLR: Inference [ S | A ]
• Class 13: MLR: Inference [ S | A | R | lab ]
• Class 14: MLR: Simulation and Resampling Inference [ S | A | R | lab ]
• Class 15: MLR: Regression Diagnostics and Residual Plots [ R ]
• [spring break]
• Class 16: MLR: More on diagnostics, confidence intervals [ S | A ]
• Class 17: MLR: Model selection [ S | A | R ]
• Class 18: Interaction models and variable transformation [ S | A ]
• Class 19: Spline models [ S | A ]
• Class 20: Generalized Linear Models and Logistic Regression [ S | A ]
• Class 21: Logistic Regression competition [ lab ]
• Class 22: Longitudinal Data Analysis [ S | A ]
• Class 23: Longitudinal Data Analysis (continued)
• Class 24: Project prep
• Classes 25-26: Project presentations