Sas System For Regression, Third Edition / Edition 3

Sas System For Regression, Third Edition / Edition 3

by Ph.D. Rudolf J. Freund, Ramon C. Littell
     
 

Learn to perform a wide variety of regression analyses using SAS software with this example-driven revised favorite from SAS Publishing. With this third edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression analyses using PROC REG and diagnosing… See more details below

Overview

Learn to perform a wide variety of regression analyses using SAS software with this example-driven revised favorite from SAS Publishing. With this third edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression analyses using PROC REG and diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.

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Product Details

ISBN-13:
9781580257251
Publisher:
SAS Institute Inc.
Publication date:
10/17/2001
Series:
Wiley Series in Probability and Statistics
Edition description:
3RD
Pages:
264
Product dimensions:
8.25(w) x 11.00(h) x 0.55(d)

Table of Contents

Acknowledgmentsvii
Chapter 1Regression Concepts1
1.1Statistical Background1
1.2Performing a Regression with the IML Procedure9
1.3Regression with the SAS System12
Chapter 2Using the REG Procedure15
2.1Introduction15
2.2A Model with One Independent Variable17
2.3A Model with Several Independent Variables20
2.4Various MODEL Statement Options24
2.5Further Examination of Model Parameters36
2.6Plotting Results43
2.7Creating Data I: the OUTPUT Statement52
2.8Creating Data II: Other Data Sets55
2.9Creating Data III: ODS Output57
2.10Predicting to a Different Set of Data57
2.11Exact Collinearity: Linear Dependency60
2.12Summary62
Chapter 3Observations63
3.1Introduction63
3.2Outlier Detection64
3.3Specification Errors77
3.4Heterogeneous Variances81
3.5Correlated Errors86
3.6Summary94
Chapter 4Multicollinearity: Detection and Remedial Measures95
4.1Introduction95
4.2Detecting Multicollinearity97
4.3Model Restructuring101
4.4Variable Selection108
4.5Biased Estimation120
4.6Summary125
Chapter 5Curve Fitting127
5.1Introduction127
5.2Polynomial Models with One Independent Variable128
5.3Polynomial Plots132
5.4Polynomial Models with Several Variables134
5.5Response Surface Plots138
5.6A Three-Factor Response Surface Experiment142
5.7Curve Fitting without a Model148
5.8Summary155
Chapter 6Special Applications of Linear Models157
6.1Introduction157
6.2Multiplicative Models158
6.3Spline Models164
6.4Indicator Variables169
6.5Binary Response Variable: Logistic Regression173
6.6Summary184
Chapter 7Nonlinear Models185
7.1Introduction185
7.2Estimating the Exponential Decay Model186
7.3Fitting a Growth Curve with the NLIN Procedure192
7.4Fitting Splines with Unknown Knots198
7.5Additional Comments on the NLIN Procedure203
7.6Summary204
Chapter 8Using SAS/INSIGHT Software for Regression207
8.1Introduction207
8.2Multiple Linear Regression: the BOQ Data207
8.3A Polynomial Response Surface: the FISH Data215
8.4Logistic Regression: the DYSTRO Data218
8.5Nonparametric Smoothing: the Barbados Data221
8.6Summary225
References227
Index229

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