Statistical Writing
36-707 Regression Analysis
Preface
1
Introduction
Preliminaries
2
Causality
3
Matrix and Vector Algebra
Linear Regression
4
Linear Regression Basics
5
Geometric Multiple Regression
6
Linear Models in R
7
Interpreting Regressors
8
The Regressinator
9
Regression Assumptions and Diagnostics
10
Nonlinear Regressors
11
Conducting Inference
Generalized Linear Models
12
Logistic Regression
13
Other Response Distributions
14
Generalized Additive Models
15
The Bootstrap
Prediction
16
Prediction Goals and Prediction Errors
17
Estimating Error
18
Penalized Models
19
Kernel Regression
Special Topics
20
Survival Analysis
21
Missing Data
22
Multilevel and Hierarchical Models
23
Experimental Design
Statistical Writing
24
Genre Conventions
25
Reporting Results in APA Format
References
Statistical Writing
23
Experimental Design
24
Genre Conventions