目錄
Preface
1 Linear Models
1.1 A simple linear model
Simple least squares estimation
1.1.1 Sampling properties of B
1.1.2 So how old is the universe?
1.1.3 Adding a distributional assumption
Testing hypotheses about B
Confidence intervals
1.2 Linear models in general
1.3 The theory of linear models
1.3.1 Least squares estimation of B
1.3.2 The distribution of B
1.3.3 (Bi-Bi)/oBi~tn-p
1.3.4 F-ratio results I
1.3.5 F-ratio results Ⅱ
1.3.6 The influence matrix
1.3.7 The residuals, E, and fitted values, u
1.3.8 Results in terms of X
1.3.9 The Gauss Markov Theorem: What's special about least squares?
1.4 The geometry of linear modelling
1.4.1 Least squares
1.4.2 Fitting by orthogonal decompositions
1.4.3 Comparison of nested models
1.5 Practical linear modelling
1.5.1 Model fitting and model checking
1.5.2 Model summary
1.5.3 Model selection
1.5.4 Another model selection example
A follow-up
1.5.5 Confidence intervals
1.5.6 Prediction
1.5.7 Co-linearity, confounding and causation
……
2 Linear Mixed Models
3 Generalized Linear Models
4 Introducing GAMs
5 Smoothers
6 GAM theory
7 GAMs in Practice: mg CV