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9780471416647

SAS System for Regression

by ;
  • ISBN13:

    9780471416647

  • ISBN10:

    0471416649

  • Edition: 3rd
  • Format: Paperback
  • Copyright: 2000-12-29
  • Publisher: Wiley-SAS
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Supplemental Materials

What is included with this book?

Summary

This book describes how to use the SAS System to perform a wide variety of different regression analyses, such as using various models as well as diagnosing data problems. Topics include performing linear regression analyses using PROC REG; diagnosing and providing remedies for data problems including outliers and multicollinearity; using regression to fit a variety of different models, including nonlinear models; using SAS/INSIGHT software for performing regression analysis. Examples feature many SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others.

Author Biography

Rudolf Freund is Professor Emeritus, Texas A&M University.

Ramon Littell is at the University of Florida.

Table of Contents

Acknowledgments vii
Regression Concepts
1(14)
Statistical Background
1(8)
Performing a Regression with the IML Procedure
9(3)
Regression with the SAS System
12(3)
Using the REG Procedure
15(48)
Introduction
15(2)
A Model with One Independent Variable
17(3)
A Model with Several Independent Variables
20(4)
Various Model Statement Options
24(12)
Further Examination of Model Parameters
36(7)
Plotting Results
43(9)
Creating Data I: the Output Statement
52(3)
Creating Data II: Other Data Sets
55(2)
Creating Data III: ODS Output
57(1)
Predicting to a Different Set of Data
57(3)
Exact Collinearity: Linear Dependency
60(2)
Summary
62(1)
Observations
63(32)
Introduction
63(1)
Outlier Detection
64(13)
Specification Errors
77(4)
Heterogeneous Variances
81(5)
Correlated Errors
86(8)
Summary
94(1)
Multicollinearity: Detection and Remedial Measures
95(32)
Introduction
95(2)
Detecting Multicollinearity
97(4)
Model Restructuring
101(7)
Variable Selection
108(12)
Biased Estimation
120(5)
Summary
125(2)
Curve Fitting
127(30)
Introduction
127(1)
Polynomial Models with One Independent Variable
128(4)
Polynomial Plots
132(2)
Polynomial Models with Several Variables
134(4)
Response Surface Plots
138(4)
A Three-Factor Response Surface Experiment
142(6)
Curve Fitting without a Model
148(7)
Summary
155(2)
Special Applications of Linear Models
157(28)
Introduction
157(1)
Multiplicative Models
158(6)
Spline Models
164(5)
Indicator Variables
169(4)
Binary Response Variable: Logistic Regression
173(11)
Summary
184(1)
Nonlinear Models
185(22)
Introduction
185(1)
Estimating the Exponential Decay Model
186(6)
Fitting a Growth Curve with the NLIN Procedure
192(6)
Fitting Splines with Unknown Knots
198(5)
Additional Comments on the NLIN Procedure
203(1)
Summary
204(3)
Using SAS/Insight Software for Regression
207(20)
Introduction
207(1)
Multiple Linear Regression: the BOQ Data
207(8)
A Polynomial Response Surface: the FISH Data
215(3)
Logistic Regression: the DYSTRO Data
218(3)
Nonparametric Smoothing: the Barbados Data
221(4)
Summary
225(2)
References 227(2)
Index 229

Supplemental Materials

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