Week 1

Lecture 1: Paper Overview Lecture 2: The Basics of Regression Modelling Lecture 3: Simple Linear Regression

Week 2

Lecture 4: Linear Regression Modelling with R Lecture 5: Checking the Assumptions Lecture 6: Outliers and Influential Points

Week 3

Lecture 7: What to do When Assumptions Fail Lecture 8: Prediction in Simple Linear Regression Lecture 9: Introduction to Multiple Linear Regression

Week 4

Lecture 10: Testing in Multiple Regression Models (1) Lecture 11: Testing in Multiple Regression Models (2) Lecture 12: Comparison of Multiple Linear Regression Models

Week 5

Lecture 13: Variable Selection Lecture 14: Model Choice Using Information Criteria Lecture 15: Polynomial Regression Models

Week 6

Lecture 16: A Brief Look at Matrix Algebra Lecture 17: Matrices and Linear Regression Models Lecture 18: Orthogonal Polynomials

Week 7

Lecture 19: Linear Models with Factors Lecture 20: Interpretation of One-Way Models Lecture 21: Post Hoc Testing

Week 8

Lecture 22: Factor or Numerical Covariate? Lecture 23: Introduction to the Two Factor Model

Week 9

Lecture 24: Orthogonal Factorial Models Lecture 25: Interactions

Week 10

Lecture 26: The General Linear Model Lecture 27: Comparison of general linear models Lecture 28: Models with Many Factors

Week 11

Lecture 29: Nested Factors Lecture 30: Introduction to Linear Modelling for Time Series Lecture 31: Models with Autoregressive Errors