**SAS Web Books**

**Regression with SAS: Short Outline**

by Xiao Chen, Phil 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 SAS. This web book does not teach regression, per se, but focuses on how to perform regression analyses using SAS. 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 SAS, for example that you have taken the Introduction to SAS workshop or have equivalent knowledge of SAS. If you are a member of the UCLA community and have questions about these materials, we welcome you to send questions via email to ATSstat@ucla.edu or to visit our consulting services .

You can find this book at https://stats.idre.ucla.edu/stat/sas/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 - 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 - 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 -
Chapter 4 – Beyond OLS

4.1 Robust Regression Methods

4.1.1 Regression with Robust Standard Errors

4.1.2 Using the Proc Genmod for Clustered Data

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

- Chapter 1 – Simple and Multiple Regression
- 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)