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Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, 2nd Edition plus An Introduction to Categorical Data Analysis, 2nd Edition 2nd edition [Pehme köide]

, (Simon Fraser University at Harbour Centre), (University of Illinois at Chicago ), (University of Florida, Gainesville), (Saginaw Valley State University)
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  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470388005
  • ISBN-13: 9780470388006
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Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, 2nd Edition plus An Introduction to Categorical Data Analysis, 2nd Edition 2nd editionSuurem pilt
  • Formaat: Paperback / softback, 950 pages, kõrgus x laius x paksus: 236x163x25 mm, kaal: 680 g
  • Ilmumisaeg: 29-Feb-2008
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470388005
  • ISBN-13: 9780470388006
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470388006.html
This set contains: 9780471469445 A Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, Second Edition by Norm O'Rourke, Larry Hatcher, Edward J. Stepanski, SAS Institute Inc and 9780471226185 An Introduction to Categorical Data Analysis, Second Edition by Alan Agresti.
Preface to the Second Edition xv
Introduction
1(20)
Categorical Response Data
1(2)
Response/Explanatory Variable Distinction
2(1)
Nominal/Ordinal Scale Distinction
2(1)
Organization of this Book
3(1)
Probability Distributions for Categorical Data
3(3)
Binomial Distribution
4(1)
Multinomial Distribution
5(1)
Statistical Inference for a Proportion
6(5)
Likelihood Function and Maximum Likelihood Estimation
6(2)
Significance Test About a Binomial Proportion
8(1)
Survey Results on Legalizing Abortion
8(1)
Confidence Intervals for a Binomial Proportion
9(2)
More on Statistical Inference for Discrete Data
11(5)
Wald, Likelihood-Ratio, and Score Inference
11(1)
Wald, Score, and Likelihood-Ratio Inference for Binomial Parameter
12(1)
Small-Sample Binomial Inference
13(1)
Small-Sample Discrete Inference is Conservative
14(1)
Inference Based on the Mid P-value
15(1)
Summary
16(1)
Problems
16(5)
Contingency Tables
21(44)
Probability Structure for Contingency Tables
21(4)
Joint, Marginal, and Conditional Probabilities
22(1)
Belief in Afterlife
22(1)
Sensitivity and Specificity in Diagnostic Tests
23(1)
Independence
24(1)
Binomial and Multinomial Sampling
25(1)
Comparing Proportions in Two-by-Two Tables
25(3)
Difference of Proportions
26(1)
Aspirin and Heart Attacks
26(1)
Relative Risk
27(1)
The Odds Ratio
28(6)
Properties of the Odds Ratio
29(1)
Odds Ratio for Aspirin Use and heart Attacks
30(1)
Inference for Odds Ratios and Log Odds Ratios
30(2)
Relationship Between Odds Ratio and Relative Risk
32(1)
The Odds Ratio Applies in Case-Control Studies
32(2)
Types of Observational Studies
34(1)
Chi-Squared Tests of Independence
34(7)
Pearson Statistic and the Chi-Squared Distribution
35(1)
Likelihood-Ratio Statistic
36(1)
Tests of Independence
36(1)
Gender Gap in Political Affiliation
37(1)
Residuals for Cells in a Contingency Table
38(1)
Partitioning Chi-Squared
39(1)
Comments About Chi-Squared Tests
40(1)
Testing Independence for Ordinal Data
41(4)
Linear Trend Alternative to Independence
41(1)
Alcohol Use and Infant Malformation
42(1)
Extra Power with Ordinal Tests
43(1)
Choice of Scores
43(1)
Trend Tests for I * 2 and 2 * J Tables
44(1)
Nominal-Ordinal Tables
45(1)
Exact Inference for Small Samples
45(4)
Fisher's Exact Test for 2 * 2 Tables
45(1)
Fisher's Tea Taster
46(1)
P-Values and Conservatism for Actual P(Type I Error)
47(1)
Small-Sample Confidence Interval for Odds Ratio
48(1)
Association in Three-Way Tables
49(6)
Partial Tables
49(1)
Conditional Versus Marginal Associations: Death Penalty Example
49(2)
Simpson's Paradox
51(1)
Conditional and Marginal Odds Ratios
52(1)
Conditional Independence Versus Marginal Independence
53(1)
Homogeneous Association
54(1)
Problems
55(10)
Generalized Linear Models
65(34)
Components of a Generalized Linear Model
66(2)
Random Component
66(1)
Systematic Component
66(1)
Link Function
66(1)
Normal GLM
67(1)
Generalized Linear Models for Binary Data
68(6)
Linear Probability Model
68(1)
Snoring and Heart Disease
69(1)
Logistic Regression Model
70(2)
Probit Regression Model
72(1)
Binary Regression and Cumulative Distribution Functions
72(2)
Generalized Linear Models for Count Data
74(10)
Poisson Regression
75(1)
Female Horseshoe Crabs and their Satellites
75(5)
Overdispersion: Greater Variability than Expected
80(1)
Negative Binomial Regression
81(1)
Count Regression for Rate Data
82(1)
British Train Accidents over Time
83(1)
Statistical Inference and Model Checking
84(4)
Inference about Model Parameters
84(1)
Snoring and Heart Disease Revisited
85(1)
The Deviance
85(1)
Model Comparison Using the Deviance
86(1)
Residuals Comparing Observations to the Model Fit
87(1)
Fitting Generalized Linear Models
88(2)
The Newton-Raphson Algorithm Fits GLMs
88(1)
Wald, Likelihood-Ratio, and Score Inference Use the Likelihood Function
89(1)
Advantages of GLMs
90(1)
Problems
90(9)
Logistic Regression
99(38)
Interpreting the Logistic Regression Model
99(7)
Linear Approximation Interpretations
100(1)
Horseshoe Crabs: Viewing and Smoothing a Binary Outcome
101(1)
Horseshoe Crabs: Interpreting the Logistic Regression Fit
101(3)
Odds Ratio Interpretation
104(1)
Logistic Regression with Retrospective Studies
105(1)
Normally Distributed X Implies Logistic Regression for Y
105(1)
Inference for Logistic Regression
106(4)
Binary Data can be Grouped or Ungrouped
106(1)
Confidence Intervals for Effects
106(1)
Significance Testing
107(1)
Confidence Intervals for Probabilities
108(1)
Why Use a Model to Estimate Probabilities?
108(1)
Confidence Intervals for Probabilities: Details
108(1)
Standard Errors of Model Parameter Estimates
109(1)
Logistic Regression with Categorical Predictors
110(5)
Indicator Variables Represent Categories of Predictors
110(1)
AZT Use and AIDS
111(2)
ANOVA-Type Model Representation of Factors
113(1)
The Cochran-Mantel-Haenszel Test for 2 * 2 * K Contingency Tables
114(1)
Testing the Homogeneity of Odds Ratios
115(1)
Multiple Logistic Regression
115(5)
Horseshoe Crabs with Color and Width Predictors
116(2)
Model Comparison to Check Whether a Term is Needed
118(1)
Quantitative Treatment of Ordinal Predictor
118(1)
Allowing Interaction
119(1)
Summarizing Effects in Logistic Regression
120(1)
Probability-Based Interpretations
120(1)
Standardized Interpretations
121(1)
Problems
121(16)
Building and Applying Logistic Regression Models
137(36)
Strategies in Model Selection
137(7)
How Many Predictors Can You Use?
138(1)
Horseshoe Carbs Revisited
138(1)
Stepwise Variable Selection Algorithms
139(1)
Backward Elimination for Horseshoe Crabs
140(1)
AIC, Model Selection, and the ``Correct'' Model
141(1)
Summarizing Predictive Power: Classification Tables
142(1)
Summarizing Predictive Power: ROC Curves
143(1)
Summarizing Predictive Power: A Correlation
144(1)
Model Checking
144(8)
Likelihood-Ratio Model Comparison Tests
144(1)
Goodness of Fit and the Deviance
145(1)
Checking Fit: Grouped Data, Ungrouped Data, and Continuous Predictors
146(1)
Residuals for Logit Models
147(2)
Graduate Admissions at University of Florida
149(1)
Influence Diagnostics for Logistic Regression
150(1)
Heart Disease and Blood Pressure
151(1)
Effects of Sparse Data
152(5)
Infinite Effect Estimate: Quantitative Predictor
152(1)
Infinite Effect Estimate: Categorical Predictors
153(1)
Clinical Trial with Sparse Data
154(2)
Effect of Small Samples on X2 and G2 Tests
156(1)
Conditional Logistic Regression and Exact Inference
157(3)
Conditional Maximum Likelihood Inference
157(1)
Small-Sample Tests for Contingency Tables
158(1)
Promotion Discrimination
159(1)
Small-Sample Confidence Intervals for Logistic Parameters and Odds Ratios
159(1)
Limitations of Small-Sample Exact Methods
160(1)
Sample Size and Power for Logistic Regression
160(3)
Sample Size for Comparing Two Proportions
161(1)
Sample Size in Logistic Regression
161(1)
Sample Size in Multiple Logistic Regression
162(1)
Problems
163(10)
Multicategory Logit Models
173(31)
Logit Models for Nominal Responses
173(7)
Baseline-Category Logits
173(1)
Alligator Food Choice
174(2)
Estimating Response Probabilities
176(2)
Belief in Afterlife
178(1)
Discrete Choice Models
179(1)
Cumulative Logit Models for Ordinal Responses
180(9)
Cumulative Logit Models with Proportional Odds Property
180(2)
Political Ideology and Party Affiliation
182(2)
Inference about Model Parameters
184(1)
Checking Model Fit
184(1)
Modeling Mental Health
185(2)
Interpretations Comparing Cumulative Probabilities
187(1)
Latent Variable Motivation
187(2)
Invariance to Choice of Response Categories
189(1)
Paired-Category Ordinal Logits
189(4)
Adjacent-Categories Logits
190(1)
Political Ideology Revisited
190(1)
Continuation-Ratio Logits
191(1)
A Developmental Toxicity Study
191(1)
Overdispersion in Clustered Data
192(1)
Tests of Conditional Independence
193(3)
Job Satisfaction and Income
193(1)
Generalized Cochran-Mantel-Haenszel Tests
194(1)
Detecting Nominal-Ordinal Conditional Association
195(1)
Detecting Nominal-Nominal Conditional Association
196(1)
Problems
196(8)
Loglinear Models for Contingency Tables
204(40)
Loglinear Models for Two-Way and Three-Way Tables
204(8)
Loglinear Model of Independence for Two-Way Table
205(1)
Interpretation of Parameters in Independence Model
205(1)
Saturated Model for Two-Way Tables
206(2)
Loglinear Models for Three-Way Tables
208(1)
Two-Factor Parameters Describe Conditional Associations
209(1)
Alcohol, Cigarette, and Marijuana Use
209(3)
Inference for Loglinear Models
212(7)
Chi-Squared Goodness-of-Fit Tests
212(1)
Loglinear Cell Residuals
213(1)
Tests about Conditional Associations
214(1)
Confidence Intervals for Conditional Odds Ratios
214(1)
Loglinear Models for Higher Dimensions
215(1)
Automobile Accidents and Seat Belts
215(3)
Three-Factor Interaction
218(1)
Large Samples and Statistical vs Practical Significance
218(1)
The Loglinear-Logistic Connection
219(4)
Using Logistic Models to Interpret Loglinear Models
219(1)
Auto Accident Data Revisited
220(1)
Correspondence Between Loglinear and Logistic Models
221(1)
Strategies in Model Selection
221(2)
Independence Graphs and Collapsibility
223(5)
Independence Graphs
223(1)
Collapsibility Conditions for Three-Way Tables
224(1)
Collapsibility and Logistic Models
225(1)
Collapsibility and Independence Graphs for Multiway Tables
225(1)
Model Building for Student Drug Use
226(2)
Graphical Models
228(1)
Modeling Ordinal Associations
228(4)
Linear-by-Linear Association Model
229(1)
Sex Opinions
230(2)
Ordinal Tests of Independence
232(1)
Problems
232(12)
Models for Matched Pairs
244(32)
Comparing Dependent Proportions
245(2)
McNemar Test Comparing Marginal Proportions
245(1)
Estimating Differences of Proportions
246(1)
Logistic Regression for Matched Pairs
247(5)
Marginal Models for Marginal Proportions
247(1)
Subject-Specific and Population-Averaged Tables
248(1)
Conditional Logistic Regression for Matched-Pairs
249(1)
Logistic Regression for Matched Case-Control Studies
250(2)
Connection between McNemar and Cochran-Mantel-Haenszel Tests
252(1)
Comparing Margins of Square Contingency Tables
252(4)
Marginal Homogeneity and Nominal Classifications
253(1)
Coffee Brand Market Share
253(1)
Marginal Homogeneity and Ordered Categories
254(1)
Example:Recycle or Drive Less to Help Environment?
255(1)
Symmetry and Quasi-Symmetry Models for Square Tables
256(4)
Symmetry as a Logistic Model
257(1)
Quasi-Symmetry
257(1)
Coffee Brand Market Share Revisited
257(1)
Testing Marginal Homogeneity Using Symmetry and Quasi-Symmetry
258(1)
An Ordinal Quasi-Symmetry Model
258(1)
Recycle or Drive Less?
259(1)
Testing Marginal Homogeneity Using Symmetry and Ordinal Quasi-Symmetry
259(1)
Analyzing Rater Agreement
260(4)
Cell Residuals for Independence Model
261(1)
Quasi-independence Model
261(1)
Odds Ratios Summarizing Agreement
262(1)
Quasi-Symmetry and Agreement Modeling
263(1)
Kappa Measure of Agreement
264(1)
Bradley-Terry Model for Paired Preferences
264(2)
The Bradley-Terry Model
265(1)
Ranking Men Tennis Players
265(1)
Problems
266(10)
Modeling Correlated, Clustered Responses
276(21)
Marginal Models Versus Conditional Models
277(2)
Marginal Models for a Clustered Binary Response
277(1)
Longitudinal Study of Treatments for Depression
277(2)
Conditional Models for a Repeated Response
279(1)
Marginal Modeling: The GEE Approach
279(6)
Quasi-Likelihood Methods
280(1)
Generalized Estimating Equation Methodology: Basic Ideas
280(1)
GEE for Binary Data: Depression Study
281(2)
Teratology Overdispersion
283(1)
Limitations of GEE Compared with ML
284(1)
Extending GEE: Multinomial Responses
285(3)
Marginal Modeling of a Clustered Multinomial Response
285(1)
Insomnia Study
285(2)
Another Way of Modeling Association with GEE
287(1)
Dealing with Missing Data
287(1)
Transitional Modeling, Given the Past
288(2)
Transitional Models with Explanatory Variables
288(1)
Respiratory Illness and Maternal Smoking
288(1)
Comparisons that Control for Initial Response
289(1)
Transitional Models Relate to Loglinear Models
290(1)
Problems
290(7)
Random Effects: Generalized Linear Mixed Models
297(28)
Random Effects Modeling of Clustered Categorical Data
297(5)
The Generalized Linear Mixed Model
298(1)
A Logistic GLMM for Binary Matched Pairs
299(1)
Sacrifices for the Environment Revisited
300(1)
Differing Effects in Conditional Models and Marginal Models
300(2)
Examples of Random Effects Models for Binary Data
302(8)
Small-Area Estimation of Binomial Probabilities
302(1)
Estimating Basketball Free Throw Success
303(1)
Teratology Overdispersion Revisited
304(1)
Repeated Responses on Similar Survey Items
305(2)
Item Response Models: The Rasch Model
307(1)
Depression Study Revisited
307(1)
Choosing Marginal or Conditional Models
308(1)
Conditional Models: Random Effects Versus Conditional ML
309(1)
Extensions to Multinomial Responses or Multiple Random Effect Terms
310(3)
Insomnia Study Revisited
310(1)
Bivariate Random Effects and Association Heterogeneity
311(2)
Multilevel (Hierarchical) Models
313(3)
Two-Level Model for Student Advancement
314(1)
Grade Retention
315(1)
Model Fitting and Inference for GLMMS
316(2)
Fitting GLMMs
316(1)
Inference for Model Parameters and Prediction
317(1)
Problems
318(7)
A Historical Tour of Categorical Data Analysis
325(7)
The Pearson-Yule Association Controversy
325(1)
R. A. Fisher's Contributions
326(2)
Logistic Regression
328(1)
Multiway Contingency Tables and Loglinear Models
329(2)
Final Comments
331(1)
Appendix A: Software for Categorical Data Analysis 332(11)
Appendix B: Chi-Squared Distribution Values 343(1)
Bibliography 344(2)
Index of Examples 346(4)
Subject Index 350(7)
Brief Solutions to Some Odd-Numbered Problems 357(142)
Acknowledgments xi
Using This Book xiii
Basic Concepts in Research and DATA Analysis
1(20)
Introduction: A Common Language for Researchers
2(1)
Steps to Follow When Conducting Research
2(3)
Variables, Values, and Observations
5(2)
Scales of Measurement
7(2)
Basic Approaches to Research
9(3)
Descriptive versus Inferential Statistical Analysis
12(1)
Hypothesis Testing
13(6)
Conclusion
19(2)
Introduction to SAS Programs, SAS Logs, and SAS Output
21(8)
Introduction: What is SAS?
22(1)
Three Types of SAS Files
23(5)
SAS Customer Support Center
28(1)
Conclusion
28(1)
Reference
28(1)
Data Input
29(28)
Introduction: Inputting Questionnaire Data versus Other Types of Data
30(1)
Entering Data: An Illustrative Example
31(4)
Inputting Data Using the DATALINES Statement
35(5)
Additional Guidelines
40(8)
Inputting a Correlation or Covariance Matrix
48(5)
Inputting Data Using the INFILE Statement Rather than the DATALINES Statement
53(1)
Controlling the Output Size and Log Pages with the OPTIONS Statement
54(1)
Conclusion
55(1)
Reference
55(2)
Working with Variables and Observations in SAS Datasets
57(32)
Introduction: Manipulating, Subsetting, Concatenating, and Merging Data
58(1)
Placement of Data Manipulation and Data Subsetting Statements
59(4)
Data Manipulation
63(11)
Data Subsetting
74(5)
A More Comprehensive Example
79(1)
Concatenating and Merging Datasets
80(7)
Conclusion
87(2)
Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE
89(30)
Introduction: Why Perform Simple Descritive Analyses?
90(1)
Example: An Abridged Volunteerism Survey
91(2)
Computing Descriptive Statistics with PROC MEANS
93(3)
Creating Frequency Tables with PROC FREQ
96(2)
Printing Raw Data with PROC PRINT
98(1)
Testing for Normality with PROC UNIVARIATE
99(19)
Conclusion
118(1)
References
118(1)
Measures of Bivariate Association
119(36)
Introduction: Significance Tests versus Measures of Association
120(1)
Choosing the Correct Statistic
121(4)
Pearson Correlations
125(15)
Spearman Correlations
140(2)
The Chi-Square Test of Independence
142(11)
Conclusion
153(1)
Assumptions Underlying the Tests
153(1)
References
154(1)
Assessing Scale Reliability with Coefficient Alpha
155(12)
Introduction: The Basics of Scale Reliability
156(3)
Coefficient Alpha
159(1)
Assessing Coefficient Alpha with PROC CORR
160(5)
Summarizing the Results
165(1)
Conclusion
166(1)
References
166(1)
t Tests: Independent Samples and Paired Samples
167(42)
Introduction: Two Types of t Tests
168(1)
The Independent-Samples t Test
169(19)
The Paired-Samples t Test
188(19)
Conclusion
207(1)
Assumptions Underlying the t Test
207(1)
References
208(1)
One-Way ANOVA with One Between-Subjects Factor
209(28)
Introduction: The Basics of One-Way ANOVA, Between-Subjects Design
210(4)
Example with Significant Differences between Experimental Conditions
214(13)
Example with Nonsignificant Differences between Experimental Conditions
227(5)
Understanding the Meaning of the F Statistic
232(1)
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
233(2)
Conclusion
235(1)
Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor
235(1)
References
235(2)
Factorial ANOVA with Two Between-Subjects Factors
237(42)
Introduction to Factorial Designs
238(3)
Some Possible Results from a Factorial ANOVA
241(7)
Example with a Nonsignificant Interaction
248(12)
Example with a Significant Interaction
260(15)
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
275(3)
Conclusion
278(1)
Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors
278(1)
Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
279(20)
Introduction: The Basics of Multivariate Analysis of Variance
280(3)
Example with Significant Differences between Experimental Conditions
283(11)
Example with Nonsignificant Differences between Experimental Conditions
294(2)
Conclusion
296(1)
Assumptions Underlying Multivariate ANOVA with One Between-Subjects Factor
296(1)
References
297(2)
One-Way ANOVA with One Repeated-Measures Factor
299(26)
Introduction: What Is a Repeated-Measures Design?
300(2)
Example: Significant Differences in Investment Size Across Time
302(13)
Further Notes on Repeated-Measures Analyses
315(7)
Conclusion
322(1)
Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor
322(2)
References
324(1)
Factorlal ANOVA with Repeated-Measures Factors and Between-Subjects Factors
325(42)
Introduction: The Basics of Mixed-Design ANOVA
326(5)
Some Possible Results from a Two-Way Mixed-Design ANOVA
331(5)
Problems with the Mixed-Design ANOVA
336(1)
Example with a Nonsignificant Interaction
336(13)
Example with a Significant Interaction
349(15)
Use of Other Post-Hoc Tests with the Repeated-Measures Variable
364(1)
Conclusion
364(1)
Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
364(2)
References
366(1)
Multiple Regression
367(62)
Introduction: Answering Questions with Multiple Regression
368(5)
Background: Predicting a Criterion Variable from Multiple Predictors
373(8)
The Results of a Multiple Regression Analysis
381(19)
Example: A Test of the Investment Model
400(1)
Overview of the Analysis
401(1)
Gathering and Entering Data
402(4)
Computing Bivariate Correlations with PROC CORR
406(3)
Estimating the Full Multiple Regression Equation with PROC REG
409(6)
Computing Uniqueness Indices with PROC REG
415(8)
Summarizing the Results in Tables
423(1)
Getting the Big Picture
424(1)
Formal Description of Results for a Paper
425(1)
Conclusion: Learning More about Multiple Regression
426(1)
Assumptions Underlying Multiple Regression
427(1)
References
428(1)
Principal Component Analysis
429(54)
Introduction: The Basics of Principal Component Analysis
430(8)
Example: Analysis of the Prosocial Orientation Inventory
438(3)
SAS Program and Output
441(8)
Steps in Conducting Principal Component Analysis
449(19)
An Example with Three Retained Components
468(13)
Conclusion
481(1)
Assumptions Underlying Principal Component Analysis
481(1)
References
481(2)
Appendix A Choosing the Correct Statistic
483(8)
Introduction: Thinking about the Number and Scale of Your Variables
484(2)
Guidelines for Choosing the Correct Statistic
486(4)
Conclusion
490(1)
Reference
490(1)
Appendix B Datasets
491(4)
Assessing Scale Reliability with Coefficient Alpha
492(1)
Multiple Regression
493(1)
Principal Component Analysis
494(1)
Appendix C Critical Values of the F Distribution
495(4)
Index 499
Norm O'Rourke and Larry Hatcher are the authors of A Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, 2nd Edition + An Introduction to Categorical Data Analysis, 2nd Edition, published by Wiley.