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SAS Web Books 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 ATS SAS 1 & SAS 2 classes
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 http://www.ats.ucla.edu/stat/sas/webbooks/reg/ Book Chapters Book Chapters and Outline Chapter 4 - Beyond OLS
Regression with SAS: Short Outline
by Xiao Chen, Phil Ender, Michael Mitchell & Christine Wells
(in alphabetical order)
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
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
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
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