应用回归分析和其他多元方法 英文版 第3版PDF电子书下载

1 CONCEPTS AND EXAMPLES OF RESEARCH 1

1-1 Concepts 1

1-2 Examples 2

1-3 Concluding Remarks 5

References 6

2 CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS 7

2-1 Classification of Variables 7

2-2 Overlapping of Classification Schemes 11

2-3 Choice of Analysis 11

References 13

3 BASIC STATISTICS:A REVIEW 14

3-1 Preview 14

3-2 Descriptive Statistics 15

3-3 Random Variables and Distributions 16

3-4 Sampling Distributions of t,χ2,and F 19

3-5 Statistical Inference:Estimation 21

3-6 Statistical Inference:Hypothesis Testing 24

3-7 Error Rates,Power,and Sample Size 28

Problems 30

References 33

4 INTRODUCTION TO REGRESSION ANALYSIS 34

4-1 Preview 34

4-2 Association versus Causality 35

4-3 Statistical versus Deterministic Models 37

4-4 Concluding Remarks 38

References 38

5 STRAIGHT-LINE REGRESSION ANALYSIS 39

5-1 Preview 39

5-2 Regression with a Single Independent Variable 39

5-3 Mathematical Properties of a Straight Line 42

5-4 Statistical Assumptions for a Straight-line Model 43

5-5 Determining the Best-fitting Straight Line 47

5-6 Measure of the Quality of the Straight-line Fit and Estimate of σ2 51

5-7 Inferences About the Slope and Intercept 52

5-8 Interpretations of Tests for Slope and Intercept 54

5-9 Inferences About the Regression Line μY|X=β0+β1X 57

5-10 Prediction of a New Value of Y at X0 59

5-11 Assessing the Appropriateness of the Straight-line Model 60

Problems 60

References 87

6 THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS 88

6-1 Definition of r 88

6-2 r as a Measure of Association 89

6-3 The Bivariate Normal Distribution 90

6-4 r and the Strength of the Straight-line Relationship 93

6-5 What r Does Not Measure 95

6-6 Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient 96

6-7 Testing for the Equality of Two Correlations 99

Problems 101

References 103

7 THE ANALYSIS-OF-VARIANCE TABLE 104

7-1 Preview 104

7-2 The ANOVA Table for Straight-line Regression 104

Problems 108

8 MULTIPLE REGRESSION ANALYSIS:GENERAL CONSIDERATIONS 111

8-1 Preview 111

8-2 Multiple Regression Models 112

8-3 Graphical Look at the Problem 113

8-4 Assumptions of Multiple Regression 115

8-5 Determining the Best Estimate of the Multiple Regression Equation 118

8-6 The ANOVA Table for Multiple Regression 119

8-7 Numerical Examples 121

Problems 123

References 135

9 TESTING HYPOTHESES IN MULTIPLE REGRESSION 136

9-1 Preview 136

9-2 Test for Significant Overall Regression 137

9-3 Partial F Test 138

9-4 Multiple Partial F Test 143

9-5 Strategies for Using Partial F Tests 145

9-6 Tests Involving the Intercept 150

Problems 151

References 159

10 CORRELATIONS:MULTIPLE,PARTIAL,AND MULTIPLE PARTIAL 160

10-1 Preview 160

10-2 Correlation Matrix 161

10-3 Multiple Correlation Coefficient 162

10-4 Relationship of RY|X1,X2,…,Xk to the Multivariate Normal Distribution 164

10-5 Partial Correlation Coefficient 165

10-6 Alternative Representation of the Regression Model 172

10-7 Multiple Partial Correlation 172

10-8 Concluding Remarks 174

Problems 174

Reference 185

11 CONFOUNDING AND INTERACTION IN REGRESSION 186

11-1 Preview 186

11-2 Overview 186

11-3 Interaction in Regression 188

11-4 Confounding in Regression 194

11-5 Summary and Conclusions 199

Problems 199

Reference 211

12-1 Preview 212

12-2 Simple Approaches to Diagnosing Problems in Data 212

12 REGRESSION DIAGNOSTICS 212

12-3 Residual Analysis 216

12-4 Treating Outliers 228

12-5 Collinearity 237

12-6 Scaling Problems 248

12-7 Treating Collinearity and Scaling Problems 248

12-8 Alternate Strategies of Analysis 249

12-9 An Important Caution 252

Problems 253

References 279

13 POLYNOMIAL REGRESSION 281

13-1 Preview 281

13-2 Polynomial Models 282

13-3 Least-squares Procedure for Fitting a Parabola 282

13-5 Inferences Associated with Second-order Polynomial Regression 284

13-4 ANOVA Table for Second-order Polynomial Regression 284

13-6 Example Requiring a Second-order Model 286

13-7 Fitting and Testing Higher-order Models 290

13-8 Lack-of-fit Tests 290

13-9 Orthogonal Polynomials 292

13-10 Strategies for Choosing a Polynomial Model 301

Problems 302

14-2 Definitions 317

14-1 Preview 317

14 DUMMY VARIABLES IN REGRESSION 317

14-3 Rule for Defining Dummy Variables 318

14-4 Comparing Two Straight-line Regression Equations:An Example 319

14-5 Questions for Comparing Two Straight Lines 320

14-6 Methods of Comparing Two Straight Lines 321

14-7 MethodⅠ:Using Separate Regression Fits to Compare Two Straight Lines 322

14-8 MethodⅡ:Using a Single Regression Equation to Compare Two Straight Lines 327

14-10 Testing Strategies and Interpretation:Comparing Two Straight Lines 330

14-9 Comparison of Methods Ⅰ and Ⅱ 330

14-11 Other Dummy Variable Models 332

14-12 Comparing Four Regression Equations 334

14-13 Comparing Several Regression Equations Involving Two Nominal Variables 336

Problems 338

References 360

15 ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA 361

15-1 Preview 361

15-2 Adjustment Problem 361

15-3 Analysis of Covariance 363

15-4 Assumption of Parallelism:A Potential Drawback 365

15-5 Analysis of Covariance:Several Groups and Several Covariates 366

15-6 Comments and Cautions 368

15-7 Summary 371

Problems 371

Reference 385

16 SELECTING THE BEST REGRESSION EQUATION 386

16-1 Preview 386

16-2 Steps in Selecting the Best Regression Equation 387

16-3 Step 1:Specifying the Maximum Model 387

16-4 Step 2:Specifying a Criterion for Selecting a Model 390

16-5 Step 3:Specifying a Strategy for Selecting Variables 392

16-6 Step 4:Conducting the Analysis 401

16-7 Step 5:Evaluating Reliability with Split Samples 401

16-8 Example Analysis of Actual Data 403

16-9 Issues in Selecting the Most Valid Model 409

Problems 409

References 422

17 ONE-WAY ANALYSIS OF VARIANCE 423

17-1 Preview 423

17-2 One-way ANOVA:The Problem,Assumptions,and Data Configuration 426

17-3 Methodology for One-way Fixed-effects ANOVA 429

17-4 Regression Model for Fixed-effects One-way ANOVA 435

17-5 Fixed-effects Model for One-way ANOVA 438

17-6 Random-effects Model for One-way ANOVA 440

17-7 Multiple-comparison Procedures for Fixed-effects One-way ANOVA 443

17-8 Choosing a Multiple-comparison Technique 456

17-9 Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares 457

Problems 463

References 483

18 RANDOMIZED BLOCKS:SPECIAL CASE OF TWO-WAY ANOVA 484

18-1 Preview 484

18-2 Equivalent Analysis of a Matched Pairs Experiment 488

18-3 Principle of Blocking 491

18-4 Analysis of a Randomized-blocks Experiment 493

18-5 ANOVA Table for a Randomized-blocks Experiment 495

18-6 Regression Models for a Randomized-blocks Experiment 499

18-7 Fixed-effects ANOVA Model for a Randomized-blocks Experiment 502

Problems 503

References 515

19 TWO-WAY ANOVA WITH EQUAL CELL NUMBERS 516

19-1 Preview 516

19-2 Using a Table of Cell Mcans 518

19-3 General Methodology 522

19-4 F Tests for Two-way ANOVA 527

19-5 Regression Model for Fixed-effects Two-way ANOVA 530

19-6 Interactions in Two-way ANOVA 534

19-7 Random- and Mixed-effects Two-way ANOVA Models 541

Problems 544

References 560

20 TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS 561

20-1 Preview 561

20-2 Problems with Unequal Cell Numbers:Nonorthogonality 563

20-3 Regression Approach for Unequal Cell Sample Sizes 567

20-4 Higher-way ANOVA 571

Problems 572

References 588

21 ANALYSIS OF REPEATED MEASURES DATA 589

21-1 Preview 589

21-2 Examples 590

21-3 General Approach for Repeated Measures ANOVA 592

21-4 Overview of Selected Repeated Measures Designs and ANOVA-based Analyses 594

21-5 Repeated Measures ANOVA for Unbalanced Data 611

21-6 Other Approaches to Analyzing Repeated Measures Data 612

Appendix 2l-A Examples of SAS s GLM and MIXED Procedures 613

Problems 616

References 638

22-1 Preview 639

22-2 The Principle of Maximum Likelihood 639

22 THE METHOD OF MAXIMUM LIKELIHOOD 639

22-3 Statistical Inference via Maximum Likelihood 642

22-4 Summary 652

Problems 653

References 655

23 LOGISTIC REGRESSION ANALYSIS 656

23-1 Preview 656

23-2 The Logistic Model 656

23-3 Estimating the Odds Ratio Using Logistic Regression 658

23-4 A Numerical Example of Logistic Regression 664

23-5 Theoretical Considerations 671

23-6 An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates 677

23-7 Summary 681

Problems 682

References 686

24-2 The Poisson Distribution 687

24 POISSON REGRESSION ANALYSIS 687

24-1 Preview 687

24-3 An Example of Poisson Regression 688

24-4 Poisson Regression:General Considerations 690

24-5 Measures of Goodness of Fit 694

24-6 Continuation of Skin Cancer Data Example 696

24-7 A Second Illustration of Poisson Regression Analysis 701

24-8 Summary 704

Problems 705

References 709

A APPENDIX A—TABLES 711

A-1 Standard Normal Cumulative Probabilities 712

A-2 Percentiles of the t Distribution 715

A-3 Percentiles of the Chi-square Distribution 716

A-4 Percentiles of the F Distribution 717

A-5 Values of?ln? 724

A-6 Upper α Point of Studentized Range 726

A-7 Orthogonal Polynomial Coefficients 728

A-8 BonferToni Corrected Jackknife and Studentized Residual Critical Values 729

A-9 Critical Values for Leverages 730

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 ANALYSIS 732

C APPENDIX C—ANOVA INFORMATION FOR FOUR COMMON BALANCED REPEATED MEASURES DESIGNS 744

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

C-2 Balanced Repeated Measures Design with Two Crossover Factors 746

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 Factor 752

C-5 Balanced Two-group Pre/Posttest Design 755

References 757

D SOLUTIONS TO EXERCISES 758

INDEX 787