Preface
1 Paper Overview
2 The Basics of Regression Modelling
3 Simple Linear Regression
4 Linear Regression Modelling with R
5 Checking the Assumptions
6 Outliers and Influential Points
7 What to do When Assumptions Fail
8 Prediction in Simple Linear Regression
9 Introduction to Multiple Linear Regression
10 Testing in Multiple Regression Models (1)
11 Testing in Multiple Regression Models (2)
12 Basic Matrix Algebra
13 Matrices and Linear Regression Models
14 Comparison of Multiple Linear Regression Models
15 Models with binary predictors
16 Polynomial Regression Models
17 Models using orthogonal polynomials
18 Piecewise Regression Models
19 Linear Models with One Factor
20 Diagnostics and Multiple Comparisons for One-way ANOVA
21 Factor or Numeric Covariate?
22 Introduction to the Two Factor Model
- 22.1 A Main Effects Model for Two Factors
- 22.2 Example: Foster Feeding Rats
23 Orthogonal Factorial Models
24 Factors with Interactions
25 The General Linear Model
26 Comparison of General Linear Models
27 Variable Selection
28 Stepwise and Penalised Regression
29 Multicollinearity
30 Weighted regression
31 Splines and LOESS: Flexible Regression Curves
32 Time-Indexed Regression: Trend and Seasonality
33 Material Included in Lectures 32/33
- 33.1 Lecture 33: Continue to Lectures 32/33
34 Diagnosing Autocorrelated Errors
35 Regression with Autoregressive Errors
36 Placeholder
37 Appendix: Introduction to R and RStudio
38 Appendix: Regression through the origin
39 Appendix: Transformations in regression models
40 Appendix: Models with Many Factors
41 Appendix: Nonlinear least squares

Compiled Lectures for Regression Modelling

Lecture 36 Placeholder

In class version

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