17
Describing, Resampling, Validating, and Simplifying the Model
Statistical Notes AK Book
Preface
1
Introduction
2
Learning R
3
Best Books on Statistics
4
Best Books on Data Visualization
5
Best Books on Reproducible Research and Git
6
Best Websites on Statistics
7
Best Podcast on Statistics
8
Best Papers in Statistics
9
Parametric vs non-parametric vs semiparametric
10
Nonparametric Statistical Tests
11
Conditional (covariate-adjusted) vs marginal effects
12
General Aspects of Fitting Regression Models
13
Multivariable Modeling Strategies
14
Regression Models
15
Linear regression
16
Prediction Models
17
Describing, Resampling, Validating, and Simplifying the Model
18
Binary Logistic Regression
19
Ordinal Logistic Regression
20
Modeling Longitudinal Responses using Generalized Least Squares
21
Smoothing Splines in R: A Tutorial
22
Cox Proportional Hazards Regression Model
23
Ordinal Semiparametric Regression for Survival Analysis
24
Game-theoretic statistics
25
Bayesian Statistics
26
Case Studies for Critique
27
Summary
References
Table of contents
17.1
Describing the Fitted Model
17.1.1
Relative Explained Variation
17.2
The Boostrap
17.3
Model Validation
17.4
Bootstrapping Ranks of Predictors
17.5
Simplifying the Final Model by Approximating It
17
Describing, Resampling, Validating, and Simplifying the Model
17.1
Describing the Fitted Model
17.1.1
Relative Explained Variation
SSR
SSE
SST
SST= SSR+SSE
17.2
The Boostrap
17.3
Model Validation
17.4
Bootstrapping Ranks of Predictors
17.5
Simplifying the Final Model by Approximating It
16
Prediction Models
18
Binary Logistic Regression