应用回归分析和其他多元方法 英文版 第3版PDF|Epub|txt|kindle电子书版本网盘下载

应用回归分析和其他多元方法 英文版 第3版PDF格式电子书版下载

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1 CONCEPTS AND EXAMPLES OF RESEARCH1

1-1 Concepts1

1-2 Examples2

1-3 Concluding Remarks5

References6

2 CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS7

2-1 Classification of Variables7

2-2 Overlapping of Classification Schemes11

2-3 Choice of Analysis11

References13

3 BASIC STATISTICS:A REVIEW14

3-1 Preview14

3-2 Descriptive Statistics15

3-3 Random Variables and Distributions16

3-4 Sampling Distributions of t,χ2,and F19

3-5 Statistical Inference:Estimation21

3-6 Statistical Inference:Hypothesis Testing24

3-7 Error Rates,Power,and Sample Size28

Problems30

References33

4 INTRODUCTION TO REGRESSION ANALYSIS34

4-1 Preview34

4-2 Association versus Causality35

4-3 Statistical versus Deterministic Models37

4-4 Concluding Remarks38

References38

5 STRAIGHT-LINE REGRESSION ANALYSIS39

5-1 Preview39

5-2 Regression with a Single Independent Variable39

5-3 Mathematical Properties of a Straight Line42

5-4 Statistical Assumptions for a Straight-line Model43

5-5 Determining the Best-fitting Straight Line47

5-6 Measure of the Quality of the Straight-line Fit and Estimate of σ251

5-7 Inferences About the Slope and Intercept52

5-8 Interpretations of Tests for Slope and Intercept54

5-9 Inferences About the Regression Line μY|X=β0+β1X57

5-10 Prediction of a New Value of Y at X059

5-11 Assessing the Appropriateness of the Straight-line Model60

Problems60

References87

6 THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS88

6-1 Definition of r88

6-2 r as a Measure of Association89

6-3 The Bivariate Normal Distribution90

6-4 r and the Strength of the Straight-line Relationship93

6-5 What r Does Not Measure95

6-6 Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient96

6-7 Testing for the Equality of Two Correlations99

Problems101

References103

7 THE ANALYSIS-OF-VARIANCE TABLE104

7-1 Preview104

7-2 The ANOVA Table for Straight-line Regression104

Problems108

8 MULTIPLE REGRESSION ANALYSIS:GENERAL CONSIDERATIONS111

8-1 Preview111

8-2 Multiple Regression Models112

8-3 Graphical Look at the Problem113

8-4 Assumptions of Multiple Regression115

8-5 Determining the Best Estimate of the Multiple Regression Equation118

8-6 The ANOVA Table for Multiple Regression119

8-7 Numerical Examples121

Problems123

References135

9 TESTING HYPOTHESES IN MULTIPLE REGRESSION136

9-1 Preview136

9-2 Test for Significant Overall Regression137

9-3 Partial F Test138

9-4 Multiple Partial F Test143

9-5 Strategies for Using Partial F Tests145

9-6 Tests Involving the Intercept150

Problems151

References159

10 CORRELATIONS:MULTIPLE,PARTIAL,AND MULTIPLE PARTIAL160

10-1 Preview160

10-2 Correlation Matrix161

10-3 Multiple Correlation Coefficient162

10-4 Relationship of RY|X1,X2,…,Xk to the Multivariate Normal Distribution164

10-5 Partial Correlation Coefficient165

10-6 Alternative Representation of the Regression Model172

10-7 Multiple Partial Correlation172

10-8 Concluding Remarks174

Problems174

Reference185

11 CONFOUNDING AND INTERACTION IN REGRESSION186

11-1 Preview186

11-2 Overview186

11-3 Interaction in Regression188

11-4 Confounding in Regression194

11-5 Summary and Conclusions199

Problems199

Reference211

12-1 Preview212

12-2 Simple Approaches to Diagnosing Problems in Data212

12 REGRESSION DIAGNOSTICS212

12-3 Residual Analysis216

12-4 Treating Outliers228

12-5 Collinearity237

12-6 Scaling Problems248

12-7 Treating Collinearity and Scaling Problems248

12-8 Alternate Strategies of Analysis249

12-9 An Important Caution252

Problems253

References279

13 POLYNOMIAL REGRESSION281

13-1 Preview281

13-2 Polynomial Models282

13-3 Least-squares Procedure for Fitting a Parabola282

13-5 Inferences Associated with Second-order Polynomial Regression284

13-4 ANOVA Table for Second-order Polynomial Regression284

13-6 Example Requiring a Second-order Model286

13-7 Fitting and Testing Higher-order Models290

13-8 Lack-of-fit Tests290

13-9 Orthogonal Polynomials292

13-10 Strategies for Choosing a Polynomial Model301

Problems302

14-2 Definitions317

14-1 Preview317

14 DUMMY VARIABLES IN REGRESSION317

14-3 Rule for Defining Dummy Variables318

14-4 Comparing Two Straight-line Regression Equations:An Example319

14-5 Questions for Comparing Two Straight Lines320

14-6 Methods of Comparing Two Straight Lines321

14-7 MethodⅠ:Using Separate Regression Fits to Compare Two Straight Lines322

14-8 MethodⅡ:Using a Single Regression Equation to Compare Two Straight Lines327

14-10 Testing Strategies and Interpretation:Comparing Two Straight Lines330

14-9 Comparison of Methods Ⅰ and Ⅱ330

14-11 Other Dummy Variable Models332

14-12 Comparing Four Regression Equations334

14-13 Comparing Several Regression Equations Involving Two Nominal Variables336

Problems338

References360

15 ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA361

15-1 Preview361

15-2 Adjustment Problem361

15-3 Analysis of Covariance363

15-4 Assumption of Parallelism:A Potential Drawback365

15-5 Analysis of Covariance:Several Groups and Several Covariates366

15-6 Comments and Cautions368

15-7 Summary371

Problems371

Reference385

16 SELECTING THE BEST REGRESSION EQUATION386

16-1 Preview386

16-2 Steps in Selecting the Best Regression Equation387

16-3 Step 1:Specifying the Maximum Model387

16-4 Step 2:Specifying a Criterion for Selecting a Model390

16-5 Step 3:Specifying a Strategy for Selecting Variables392

16-6 Step 4:Conducting the Analysis401

16-7 Step 5:Evaluating Reliability with Split Samples401

16-8 Example Analysis of Actual Data403

16-9 Issues in Selecting the Most Valid Model409

Problems409

References422

17 ONE-WAY ANALYSIS OF VARIANCE423

17-1 Preview423

17-2 One-way ANOVA:The Problem,Assumptions,and Data Configuration426

17-3 Methodology for One-way Fixed-effects ANOVA429

17-4 Regression Model for Fixed-effects One-way ANOVA435

17-5 Fixed-effects Model for One-way ANOVA438

17-6 Random-effects Model for One-way ANOVA440

17-7 Multiple-comparison Procedures for Fixed-effects One-way ANOVA443

17-8 Choosing a Multiple-comparison Technique456

17-9 Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares457

Problems463

References483

18 RANDOMIZED BLOCKS:SPECIAL CASE OF TWO-WAY ANOVA484

18-1 Preview484

18-2 Equivalent Analysis of a Matched Pairs Experiment488

18-3 Principle of Blocking491

18-4 Analysis of a Randomized-blocks Experiment493

18-5 ANOVA Table for a Randomized-blocks Experiment495

18-6 Regression Models for a Randomized-blocks Experiment499

18-7 Fixed-effects ANOVA Model for a Randomized-blocks Experiment502

Problems503

References515

19 TWO-WAY ANOVA WITH EQUAL CELL NUMBERS516

19-1 Preview516

19-2 Using a Table of Cell Mcans518

19-3 General Methodology522

19-4 F Tests for Two-way ANOVA527

19-5 Regression Model for Fixed-effects Two-way ANOVA530

19-6 Interactions in Two-way ANOVA534

19-7 Random- and Mixed-effects Two-way ANOVA Models541

Problems544

References560

20 TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS561

20-1 Preview561

20-2 Problems with Unequal Cell Numbers:Nonorthogonality563

20-3 Regression Approach for Unequal Cell Sample Sizes567

20-4 Higher-way ANOVA571

Problems572

References588

21 ANALYSIS OF REPEATED MEASURES DATA589

21-1 Preview589

21-2 Examples590

21-3 General Approach for Repeated Measures ANOVA592

21-4 Overview of Selected Repeated Measures Designs and ANOVA-based Analyses594

21-5 Repeated Measures ANOVA for Unbalanced Data611

21-6 Other Approaches to Analyzing Repeated Measures Data612

Appendix 2l-A Examples of SAS s GLM and MIXED Procedures613

Problems616

References638

22-1 Preview639

22-2 The Principle of Maximum Likelihood639

22 THE METHOD OF MAXIMUM LIKELIHOOD639

22-3 Statistical Inference via Maximum Likelihood642

22-4 Summary652

Problems653

References655

23 LOGISTIC REGRESSION ANALYSIS656

23-1 Preview656

23-2 The Logistic Model656

23-3 Estimating the Odds Ratio Using Logistic Regression658

23-4 A Numerical Example of Logistic Regression664

23-5 Theoretical Considerations671

23-6 An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates677

23-7 Summary681

Problems682

References686

24-2 The Poisson Distribution687

24 POISSON REGRESSION ANALYSIS687

24-1 Preview687

24-3 An Example of Poisson Regression688

24-4 Poisson Regression:General Considerations690

24-5 Measures of Goodness of Fit694

24-6 Continuation of Skin Cancer Data Example696

24-7 A Second Illustration of Poisson Regression Analysis701

24-8 Summary704

Problems705

References709

A APPENDIX A—TABLES711

A-1 Standard Normal Cumulative Probabilities712

A-2 Percentiles of the t Distribution715

A-3 Percentiles of the Chi-square Distribution716

A-4 Percentiles of the F Distribution717

A-5 Values of?ln?724

A-6 Upper α Point of Studentized Range726

A-7 Orthogonal Polynomial Coefficients728

A-8 BonferToni Corrected Jackknife and Studentized Residual Critical Values729

A-9 Critical Values for Leverages730

A-10 Critical Values for the Maximum of N Values of Cook s d(i)times(n-k-1)731

B APPENDIX B—MATRICES AND THEIR RELATIONSHIP TO REGRESSION ANALYSIS732

C APPENDIX C—ANOVA INFORMATION FOR FOUR COMMON BALANCED REPEATED MEASURES DESIGNS744

C-1 Balanced Repeated Measures Design with One Crossover Factor (Treatments)744

C-2 Balanced Repeated Measures Design with Two Crossover Factors746

C-3 Balanced Repeated Measures Design with One Nest Factor (Treatments)750

C-4 Balanced Repeated Measures Design with One Crossover Factor and One Nest Factor752

C-5 Balanced Two-group Pre/Posttest Design755

References757

D SOLUTIONS TO EXERCISES758

INDEX787