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Logistic Regression Using the SAS System: Theory and Application 4th Revised edition, AND Regression Analysis by Example, 4r.ed [Paperback / softback]

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  • Pub. Date: 29-Feb-2008
  • Publisher: Wiley-Blackwell
  • ISBN-10: 0470388072
  • ISBN-13: 9780470388075
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Logistic Regression Using the SAS System: Theory and Application 4th Revised edition, AND Regression Analysis by Example, 4r.edLarger Image
  • Format: Paperback / softback, height x width x depth: 286x219x44 mm, weight: 1526 g, 2 paperbacks
  • Pub. Date: 29-Feb-2008
  • Publisher: Wiley-Blackwell
  • ISBN-10: 0470388072
  • ISBN-13: 9780470388075
Other books in subject:
Permanent link: https://www.kriso.ee/db/9780470388075.html
This set contains: 9780471221753 Logistic Regression Using the SAS System: Theory and Application by Paul D. Allison and 9780471746966 Regression Analysis by Example, Fourth Edition by Samprit Chatterjee, Ali S. Hadi.
Preface xiii
Introduction
1(20)
What Is Regression Analysis?
1(1)
Publicly Available Data Sets
2(1)
Selected Applications of Regression Analysis
3(4)
Agricultural Sciences
3(1)
Industrial and Labor Relations
3(1)
History
4(2)
Government
6(1)
Environmental Sciences
6(1)
Steps in Regression Analysis
7(10)
Statement of the Problem
11(1)
Selection of Potentially Relevant Variables
11(1)
Data Collection
11(1)
Model Specification
12(2)
Method of Fitting
14(1)
Model Fitting
14(2)
Model Criticism and Selection
16(1)
Objectives of Regression Analysis
16(1)
Scope and Organization of the Book
17(4)
Exercises
18(3)
Simple Linear Regression
21(32)
Introduction
21(1)
Covariance and Correlation Coefficient
21(5)
Example: Computer Repair Data
26(2)
The Simple Linear Regression Model
28(1)
Parameter Estimation
29(3)
Tests of Hypotheses
32(5)
Confidence Intervals
37(1)
Predictions
37(2)
Measuring the Quality of Fit
39(3)
Regression Line Through the Origin
42(2)
Trivial Regression Models
44(1)
Bibliographic Notes
45(8)
Exercises
45(8)
Multiple Linear Regression
53(32)
Introduction
53(1)
Description of the Data and Model
53(1)
Example: Supervisor Performance Data
54(3)
Parameter Estimation
57(1)
Interpretations of Regression Coefficients
58(2)
Properties of the Least Squares Estimators
60(1)
Multiple Correlation Coefficient
61(1)
Inference for Individual Regression Coefficients
62(2)
Tests of Hypotheses in a Linear Model
64(10)
Testing All Regression Coefficients Equal to Zero
66(3)
Testing a Subset of Regression Coefficients Equal to Zero
69(2)
Testing the Equality of Regression Coefficients
71(2)
Estimating and Testing of Regression Parameters Under Constraints
73(1)
Predictions
74(1)
Summary
75(10)
Exercises
75(7)
Appendix: Multiple Regression in Matrix Notation
82(3)
Regression Diagnostics: Detection of Model Violations
85(36)
Introduction
85(1)
The Standard Regression Assumptions
86(2)
Various Types of Residuals
88(2)
Graphical Methods
90(3)
Graphs Before Fitting a Model
93(4)
One-Dimensional Graphs
93(1)
Two-Dimensional Graphs
93(3)
Rotating Plots
96(1)
Dynamic Graphs
96(1)
Graphs After Fitting a Model
97(1)
Checking Linearity and Normality Assumptions
97(1)
Leverage, Influence, and Outliers
98(5)
Outliers in the Response Variable
100(1)
Outliers in the Predictors
100(1)
Masking and Swamping Problems
100(3)
Measures of Influence
103(4)
Cook's Distance
103(1)
Welsch and Kuh Measure
104(1)
Hadi's Influence Measure
105(2)
The Potential-Residual Plot
107(1)
What to Do with the Outliers?
108(1)
Role of Variables in a Regression Equation
109(5)
Added-Variable Plot
109(1)
Residual Plus Component Plot
110(4)
Effects of an Additional Predictor
114(1)
Robust Regression
115(6)
Exercises
115(6)
Qualitative Variables as Predictors
121(30)
Introduction
121(1)
Salary Survey Data
122(3)
Interaction Variables
125(3)
Systems of Regression Equations
128(11)
Models with Different Slopes and Different Intercepts
130(7)
Models with Same Slope and Different Intercepts
137(1)
Models with Same Intercept and Different Slopes
138(1)
Other Applications of Indicator Variables
139(1)
Seasonality
140(1)
Stability of Regression Parameters Over Time
141(10)
Exercises
143(8)
Transformation of Variables
151(28)
Introduction
151(2)
Transformations to Achieve Linearity
153(2)
Bacteria Deaths Due to X-Ray Radiation
155(4)
Inadequacy of a Linear Model
156(2)
Logarithmic Transformation for Achieving Linearity
158(1)
Transformations to Stabilize Variance
159(5)
Detection of Heteroscedastic Errors
164(2)
Removal of Heteroscedasticity
166(1)
Weighted Least Squares
167(1)
Logarithmic Transformation of Data
168(1)
Power Transformation
169(4)
Summary
173(6)
Exercises
174(5)
Weighted Least Squares
179(18)
Introduction
179(1)
Heteroscedastic Models
180(3)
Supervisors Data
180(2)
College Expense Data
182(1)
Two-Stage Estimation
183(2)
Education Expenditure Data
185(9)
Fitting a Dose-Response Relationship Curve
194(3)
Exercises
196(1)
The Problem of Correlated Errors
197(24)
Introduction: Autocorrelation
197(1)
Consumer Expenditure and Money Stock
198(2)
Durbin-Watson Statistic
200(2)
Removal of Autocorrelation by Transformation
202(2)
Iterative Estimation With Autocorrelated Errors
204(1)
Autocorrelation and Missing Variables
205(1)
Analysis of Housing Starts
206(4)
Limitations of Durbin-Watson Statistic
210(1)
Indicator Variables to Remove Seasonality
211(3)
Regressing Two Time Series
214(7)
Exercises
216(5)
Analysis of Collinear Data
221(38)
Introduction
221(1)
Effects on Inference
222(6)
Effects on Forecasting
228(5)
Detection of Multicollinearity
233(6)
Centering and Scaling
239(4)
Centering and Scaling in Intercept Models
240(1)
Scaling in No-Intercept Models
241(2)
Principal Components Approach
243(3)
Imposing Constraints
246(2)
Searching for Linear Functions of the β's
248(4)
Computations Using Principal Components
252(2)
Bibliographic Notes
254(5)
Exercises
254(1)
Appendix: Principal Components
255(4)
Biased Estimation of Regression Coefficients
259(22)
Introduction
259(1)
Principal Components Regression
260(2)
Removing Dependence Among the Predictors
262(2)
Constraints on the Regression Coefficients
264(1)
Principal Components Regression: A Caution
265(3)
Ridge Regression
268(1)
Estimation by the Ridge Method
269(3)
Ridge Regression: Some Remarks
272(3)
Summary
275(6)
Exercises
275(2)
Appendix: Ridge Regression
277(4)
Variables Selection Procedures
281(36)
Introduction
281(1)
Formulation of the Problem
282(1)
Consequences of Variables Deletion
282(2)
Uses of Regression Equations
284(1)
Description and Model Building
284(1)
Estimation and Prediction
284(1)
Control
284(1)
Criteria for Evaluating Equations
285(3)
Residual Mean Square
285(1)
Mallows Cp
286(1)
Information Criteria: Akaike and Other Modified Forms
287(1)
Multicollinearity and Variable Selection
288(1)
Evaluating All Possible Equations
288(1)
Variable Selection Procedures
289(2)
Forward Selection Procedure
289(1)
Backward Elimination Procedure
290(1)
Stepwise Method
290(1)
General Remarks on Variable Selection Methods
291(1)
A Study of Supervisor Performance
292(4)
Variable Selection With Collinear Data
296(1)
The Homicide Data
296(3)
Variable Selection Using Ridge Regression
299(1)
Selection of Variables in an Air Pollution Study
300(7)
A Possible Strategy for Fitting Regression Models
307(1)
Bibliographic Notes
308(9)
Exercises
308(5)
Appendix: Effects of Incorrect Model Specifications
313(4)
Logistic Regression
317(24)
Introduction
317(1)
Modeling Qualitative Data
318(1)
The Logit Model
318(2)
Example: Estimating Probability of Bankruptcies
320(3)
Logistic Regression Diagnostics
323(1)
Determination of Variables to Retain
324(3)
Judging the Fit of a Logistic Regression
327(2)
The Multinomial Logit Model
329(7)
Multinomial Logistic Regression
329(1)
Example: Determining Chemical Diabetes
330(4)
Ordered Response Category: Ordinal Logistic Regression
334(1)
Example: Determining Chemical Diabetes Revisited
335(1)
Classification Problem: Another Approach
336(5)
Exercises
337(4)
Further Topics
341(12)
Introduction
341(1)
Generalized Linear Model
341(1)
Poisson Regression Model
342(1)
Introduction of New Drugs
343(2)
Robust Regression
345(1)
Fitting a Quadratic Model
346(2)
Distribution of PCB in U.S. Bays
348(5)
Exercises
352(1)
Appendix A: Statistical Tables 353(10)
References 363(8)
Index 371
Acknowledgements v
Introduction
1(4)
What This Book Is About
1(2)
What This Book Is Not About
3(1)
What You Need to Know
3(1)
Computing
4(1)
References
4(1)
Binary Logit Analysis: Basics
5(26)
Introduction
5(1)
Dichotomous Dependent Variables: Example
6(1)
Problems with Ordinary Linear Regression
7(4)
Odds and Odds Ratios
11(2)
The Logit Model
13(2)
Estimation of the Logit Model: General Principles
15(3)
Maximum Likelihood Estimation with PROC LOGISTIC
18(3)
Maximum Likelihood Estimation with PROC GENMOD
21(7)
Interpreting Coefficients
28(3)
Binary Logit Analysis: Details and Options
31(50)
Introduction
31(1)
Confidence Intervals
31(5)
Details of Maximum Likelihood Estimation
36(3)
Convergence Problems
39(9)
Multicollinearity
48(3)
Goodness-of-Fit Statistics
51(5)
Statistics Measuring Predictive Power
56(2)
Predicted Values, Residuals, and Influence Statistics
58(8)
Latent Variables and Standardized Coefficients
66(3)
Probit and Complementary Log-Log Models
69(7)
Unobserved Heterogeneity
76(2)
Sampling on the Dependent Variable
78(3)
Logit Analysis of Contingency Tables
81(30)
Introduction
81(1)
A Logit Model for a 2 × 2 Table
82(5)
A Three-Way Table
87(4)
A Four-Way Table
91(6)
A Four-Way Table with Ordinal Explanatory Variables
97(6)
Overdispersion
103(8)
Multinomial Logit Analysis
111(22)
Introduction
111(1)
Example
112(1)
A Model for Three Categories
113(1)
Estimation with CATMOD
114(8)
Estimation with a Binary Logit Procedure
122(1)
General Form of the Model
123(1)
Contingency Table Analysis
124(4)
CATMOD Coding of Categorical Variables
128(2)
Problems, of Interpretation
130(3)
Logit Analysis for Ordered Categories
133(28)
Introduction
133(1)
Cumulative Logit Model: Example
134(2)
Cumulative Logit Model: Explanation
136(4)
Cumulative Logit Model: Practical Considerations
140(3)
Cumulative Logit Model: Contingency Tables
143(5)
Adjacent Categories Model
148(3)
Continuation Ratio Model
151(10)
Discrete Choice Analysis
161(18)
Introduction
161(1)
Chocolate Example
162(3)
Model and Estimation
165(3)
Travel Example
168(6)
Other Applications
174(1)
Ranked Data
175(4)
Logit Analysis of Longitudinal and Other Clustered Data
179(38)
Introduction
179(1)
Longitudinal Example
180(4)
GEE Estimation
184(4)
Fixed-Effects with Conditional Logit Analysis
188(4)
Postdoctoral Training Example
192(5)
Matching
197(9)
Mixed Logit Models
206(6)
Comparison of Methods
212(1)
A Hybrid Method
213(4)
Poisson Regression
217(16)
Introduction
217(1)
The Poisson Regression Model
218(1)
Scientific Productivity Example
219(4)
Overdispersion
223(3)
Negative Binomial Regression
226(1)
Adjustment for Varying Time Spans
227(6)
Loglinear Analysis of Contingency Tables
233(34)
Introduction
233(1)
A Loglinear Model for a 2 × 2 Table
234(6)
Loglinear Models for a Four-Way Table
240(6)
Fitting the Adjacent Categories Model as a Loglinear Model
246(6)
Loglinear Models for Square, Ordered Tables
252(7)
Marginal Tables
259(2)
The Problem of Zeros
261(5)
GENMOD versus CATMOD
266(1)
Appendix 267(8)
References 275(4)
Index 279