Stata Web Books
Regression with Stata: Short Outline
by Xiao Chen, Philip B. Ender, Michael Mitchell & Christine Wells
(in alphabetical order)
The aim of these materials is to help you increase your skills in using regression analysis with Stata. This web book does not teach regression, per se, but focuses on how to perform regression analyses using Stata. It is assumed that you have had at least a one quarter/semester course in regression (linear models) or a general statistical methods course that covers simple and multiple regression and have access to a regression textbook that explains the theoretical background of the materials covered in these chapters. These materials also assume you are familiar with using Stata, for example that you have taken the Introduction to Stata workshop or have equivalent knowledge of Stata.
You can find this book at http://www.ats.ucla.edu/stat/stata/webbooks/reg/
Book Chapters
 Section 1: Regression Concepts

Section 2: Categorical
Coding and Interactions in Depth
 Chapter 5 – Additional coding systems for categorical variables in regression analysis
 Chapter 6 – More on interactions of categorical variables in regression analysis (under development)
 Chapter 7 – More on interactions of continuous and categorical variables in regression analysis (under development)
 Chapter 8 – Interactions of continuous variables in regression analysis (under development)
Book Chapters and Outline
 Section 1: Regression Concepts
 Chapter 1 – Simple and Multiple Regression
1.0 Introduction
1.1 A First Regression Analysis
1.2 Examining Data
1.3 Simple linear regression
1.4 Multiple regression
1.5 Transforming variables
1.6 Summary
Self Assessment
Self Assessment Answers  Chapter 2 – Regression Diagnostics
2.0 Regression Diagnostics
2.1 Unusual and Influential data
2.2 Tests on Normality of Residuals
2.3 Tests on Nonconstant Error of Variance
2.4 Tests on Multicollinearity
2.5 Tests on Nonlinearity
2.6 Model Specification
2.7 Issues of Independence
2.8 Summary
Self Assessment
Self Assessment Answers  Chapter 3 – Regression with Categorical Predictors
3.0 Regression with Categorical Predictors
3.1 Regression with a 0/1 variable
3.2 Regression with a 1/2 variable
3.3 Regression with a 1/2/3 variable
3.4 Regression with multiple categorical predictors
3.5 Categorical predictor with interactions
3.6 Continuous and Categorical variables
3.7 Interactions of Continuous by 0/1 Categorical variables
3.8 Continuous and Categorical variables, interaction with 1/2/3 variable
3.9 Summary
Self Assessment
Self Assessment Answers  Chapter 4 – Beyond OLS
4.1 Robust Regression Methods
4.1.1 Regression with Robust Standard Errors
4.1.2 Using the Cluster Option
4.1.3 Robust Regression
4.1.4 Quantile Regression
4.2 Constrained Linear Regression
4.3 Regression with Censored or Truncated Data
4.3.1 Regression with Censored Data
4.3.2 Regression with Truncated Data
4.4 Regression with Measurement Error
4.5 Multiple Equation Regression Models
4.5.1 Seemingly Unrelated Regression
4.5.2 Multivariate Regression
4.6 Summary
Self Assessment
Self Assessment Answers
 Chapter 1 – Simple and Multiple Regression
1.0 Introduction

Coding and Interactions in Depth
 Chapter 5 – Additional coding systems
for categorical variables in regression analysis
5.1 Simple Coding
5.2 Forward Difference Coding
5.3 Backward Difference Coding
5.4 Helmert Coding
5.5 Reverse Helmert Coding
5.6 Deviation Coding
5.7 Orthogonal Polynomial Coding
5.8 UserDefined Coding
5.9 Summary  Chapter 6 – More on interactions of categorical variables in regression analysis (under development)
 Chapter 7 – More on interactions of continuous and categorical variables in regression analysis (under development)
 Chapter 8 – Interactions of continuous variables in regression analysis (under development)
 Chapter 5 – Additional coding systems
for categorical variables in regression analysis