1 Introduction to Linear Models
2 Simple Linear Regression
3 Simple Linear Regression: Properties of Least Squares Estimators
4 Simple Linear Regression: Estimating the Residual Variance
5 Simple Linear regression: Matrix Notation
6 Simple Linear Regression: Maximum Likelihood Estimation
7 Simple Linear Regression: Partitioning Total Variability
8 Simple Linear Regression: Matrix Notation for Sum of Squares
9 Simple Linear Regression: ANOVA Table
10 Simple Linear Regression: Testing the Model is Useful
11 Simple Linear Regression: LSEs are Normally Distributed
12 Simple Linear Regression: Confidence intervals for Beta Parameters
13 Simple Linear Regression: Coefficient of Determination
14 Simple Linear Regression:Confidence and Prediction Intervals on the Mean and Individual Response
15 Simple Linear Regression: Simultaneous Inference on B0 and B1
16 Simple Linear Regression: Bonferroni and Working-Hotelling Adjustments
17 Simple Linear Regression: Residuals and their Properties
18 Simple Linear Regression: X and Y Random
19 Simple Linear Regression: Test for the Correlation Coefficient
20 Simple Linear Regression: Fixed Zero Intercept Model
21 Multiple Linear Regression: Introduction
22 Multiple Linear Regression: Least Squares Estimates
23 Multiple Linear Regression: The Hat Matrix
24 Multiple Linear Regression: Estimating the Error Variance
25 Multiple Linear Regression: Projection and Idempotent Matrices
26 Multiple Linear Regression: Gauss Markov Theorem
27 Multiple Linear Regression: Partitioning Total Variability
28 Multiple Linear Regression: Type I Sum of Squares
29 Multiple Linear Regression: Type II Sum of Squares
30 Multiple Linear Regression: Global F Test
31 Multiple Linear Regression: Partial F Tests
32 Multiple Linear Regression: t Tests for a Single Beta Parameter
33 Multiple Linear Regression: General Linear Hypotheses
34 Using R: Simple Linear Regression from Scratch
35 Multiple Linear Regression: CI/PI on the Mean and Individual Response
36 Multiple Linear Regression: Simultaneous Inference of B'=(B0,B1, ... ,Bk)
37 Multiple Linear Regression: Partitioning the Residual Sum of Squares
38 Multiple Linear Regression: Repeated Observations and Lack of Fit Test
39 Multiple Linear Regression: Centering and Scaling the Design Matrix
40 Multiple Linear Regression: Condition Number / Multicollinearity
41 Multiple Linear Regression: Variance Inflation Factor (VIF) / Multicollinearity
42 Multiple Linear Regression: Variance Proportions / Multicollinearity
43 Multiple Linear Regression: Indicator / Dummy Variables
44 Multiple Linear Regression: AIC (Akaike Information Criterion)
45 Multiple Linear Regression: Choosing a model with R2, Adjusted R2, and MSE
46 Multiple Linear Regression: Mallow's Cp
47 Multiple Linear Regression: Impact of Under or Over Fitting a Model
48 Multiple Linear Regression: The PRESS Prediction SS Statistic
49 Multiple Linear Regression: Residual Properties
50 Weighted Least Squares Regression: Mahalanobis Distance
51 Weighted Least Squares Regression: Hat Matrix
52 Weighted Least Squares Regression: Estimability / BLUE
53 Weighted Least Squares Regression: Estimating the Error Variance
54 Weighted Least Squares Regression: Testing for Estimable Functions
55 Weighted Least Squares Regression: Partial F Tests
56 Multiple Linear Regression: Canonical Form
57 Multiple Linear Regression: Canonical Form and Multicollinearity
58 Multiple Linear Regression: Principal Components Model
59 Ridge Regression (part 1 of 4): Variance Reduction
60 Ridge Regression (part 2 of 4): Deriving the Bias
61 Ridge Regression (part 3 of 4): Deriving from 1st principles.
62 Ridge Regression (part 4 of 4): Canonical Form
63 Multiple Linear Regression: Box-Cox Transformation
64 Multiple Linear Regression: Box - Tidwell Transformation
65 Multiple Linear Regression: Studentized Residuals (Part 1 of 2)
66 Multiple Linear Regression: Studentized Residuals (Part 2 of 2)
67 Multiple Linear Regression: Partial Regression Plots (Added Variable Plots)
68 Multiple Linear Regression: Influence Measures (Part 1 of 2)
69 Multiple Linear Regression: Influence Measures (Part 2 of 2)
70 Best quadratic unbiased estimator of variance in a MLR model using Lagrange Multipliers