Statistics for business and economicsPDF电子书下载

Chapter 1 Data and Statistics 1

Statistics in Practice: Business Week 2

1.1 Applications in Business and Economics 3

Accounting 3

Finance 3

Marketing 4

Production 4

Economics 4

1.2 Data 4

Elements, Variables, and Observations 5

Scales of Measurement 6

Qualitative and Quantitative Data 7

Cross-Sectional and Time Series Data 7

1.3 Data Sources 8

Existing Sources 8

Statistical Studies 10

Data Acquisition Errors 11

1.4 Descriptive Statistics 12

1.5 Statistical Inference 14

Summary 16

Glossary 16

Exercises 17

Chapter 2 Descriptive Statistics: Tabular and Graphical Methods 23

Statistics in Practice: Colgate-Palmolive Company 24

2.1 Summarizing Qualitative Data 25

Frequency Distribution 25

Relative Frequency and Percent Frequency Distributions 26

Bar Graphs and Pie Charts 26

Exercises 28

2.2 Summarizing Quantitative Data 30

Frequency Distribution 30

Relative Frequency and Percent Frequency Distributions 32

Dot Plot 32

Histogram 33

Cumulative Distributions 34

Ogive 35

Exercises 36

2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 39

Exercises 42

2.4 Crosstabulations and Scatter Diagrams 44

Crosstabulation 44

Scatter Diagram 45

Exercises 47

Summary 50

Glossary 51

Key Formulas 52

Supplementary Exercises 52

Case Problem: Consolidated Foods 58

Appendix 2.1 Using Minitab for Tabular and Graphical Methods 59

Appendix 2.2 Using Excel for Tabular and Graphical Methods 61

Chapter 3 Descriptive Statistics: Numerical Methods 72

Statistics in Practice: Small Fry Designs 73

3.1 Measures of Location 74

Mean 74

Median 75

Mode 76

Percentiles 77

Quartiles 78

Exercises 79

3.2 Measures of Variability 83

Range 84

Interquartile Range 84

Variance 84

Standard Deviation 86

Coefficient of Variation 87

Exercises 88

3.3 Measures of Relative Location and Detecting Outliers 89

z-Scores 90

Chebyshev's Theorem 90

Empirical Rule 91

Detecting Outliers 92

Exercises 93

3.4 Exploratory Data Analysis 95

Five-Number Summary 95

Box Plot 96

Exercises 97

3.5 Measures of Association Between Two Variables 100

Co variance 101

Interpretation of the Covariance 102

Correlation Coefficient 104

Interpretation of the Correlation Coefficient 105

Exercises 106

3.6 The Weighted Mean and Working With Grouped Data 109

Weighted Mean 109

Grouped Data 110

Exercises 112

Summary 114

Glossary 115

Key Formulas 116

Supplementary Exercises 117

Case Problem 1: Consolidated Foods, Inc. 123

Case Problem 2: National Health Care Association 124

Case Problem 3: Business Schools of Asia-Pacific 125

Appendix 3.1 Descriptive Statistics With Minitab 125

Appendix 3.2 Descriptive Statistics With Excel 129

Chapter 4 Introduction to Probability 132

Statistics in Practice: Morton International 133

4.1 Experiments, Counting Rules, and Assigning Probabilities 134

Counting Rules, Combinations, and Permutations 135

Assigning Probabilities 139

Probabilities for the KP＆L Project 141

Exercises 142

4.2 Events and Their Probabilities 144

Exercises 146

4.3 Some Basic Relationships of Probability 148

Complement of an Event 148

Addition Law 149

Exercises 152

4.4 Conditional Probability 153

Independent Events 156

Multiplication Law 157

Exercises 158

4.5 Bayes'Theorem 161

Tabular Approach 164

Exercises 165

Summary 167

Glossary 167

Key Formulas 168

Supplementary Exercises 169

Case Problem: Hamilton County Judges 173

Chapter 5 Discrete Probability Distributions 175

Statistics in Practice: Citibank 176

5.1 Random Variables 176

Discrete Random Variables 177

Continuous Random Variables 177

Exercises 178

5.2 Discrete Probability Distributions 179

Exercises 182

5.3 Expected Value and Variance 184

Expected Value 184

Variance 185

Exercises 186

5.4 Binomial Probability Distribution 189

A Binomial Experiment 189

Martin Clothing Store Problem 190

Using Tables of Binomial Probabilities 194

Expected Value and Variance for the Binomial Probability Distribution 196

Exercises 197

5.5 Poisson Probability Distribution 199

An Example Involving Time Intervals 199

An Example Involving Length or Distance Intervals 200

Exercises 201

5.6 Hypergeometric Probability Distribution 203

Exercises 204

Summary 205

Glossary 206

Key Formulas 206

Supplementary Exercises 207

Appendix 5.1 Discrete Probability Distributions With Minitab 210

Appendix 5.2 Discrete Probability Distributions With Excel 210

Chapter 6 Continuous Probability Distributions 212

Statistics in Practice: Procter ＆ Gamble 213

6.1 Uniform Probability Distribution 214

Area as a Measure of Probability 215

Exercises 217

6.2 Normal Probability Distribution 218

Normal Curve 219

Standard Normal Probability Distribution 221

Computing Probabilities for Any Normal Probability Distribution 226

Grear Tire Company Problem 227

Exercises 229

6.3 Exponential Probability Distribution 232

Computing Probabilities for the Exponential Distribution 232

Relationship Between the Poisson and Exponential Distributions 234

Exercises 234

Summary 236

Glossary 236

Key Formulas 236

Supplementary Exercises 237

Appendix 6.1 Continuous Probability Distributions With Minitab 239

Appendix 6.2 Continuous Probability Distributions With Excel 240

Chapter 7 Sampling and Sampling Distributions 241

Statistics in Practice: Mead Corporation 242

7.1 The Electronics Associates Sampling Problem 243

7.2 Simple Random Sampling 244

Sampling from a Finite Population 244

Sampling from an Infinite Population 246

Exercises 247

7.3 Point Estimation 249

Exercises 251

7.4 Introduction to Sampling Distributions 252

7.5 Sampling Distribution of x 255

Expected Value of x 256

Standard Deviation of x 256

Central Limit Theorem 258

Sampling Distribution of x of the EAI Sampling Problem 259

Practical Value of the Sampling Distribution of x 260

Relationship Between the Sample Size and the Sampling Distribution of x 261

Exercises 263

7.6 Sampling Distribution of p 265

Expected Value of p 266

Standard Deviation of p 267

Form of the Sampling Distribution of p 267

Practical Value of the Sampling Distribution of p 268

Exercises 269

7.7 Properties of Point Estimators 271

Unbiasedness 271

Efficiency 272

Consistency 273

7.8 Other Sampling Methods 273

Stratified Random Sampling 274

Cluster Sampling 274

Systematic Sampling 275

Convenience Sampling 275

Judgment Sampling 276

Summary 276

Glossary 277

Key Formulas 278

Supplementary Exercises 278

Appendix 7.1 The Expected Value and Standard Deviation of x 280

Appendix 7.2 Random Sampling With Minitab 282

Appendix 7.3 Random Sampling With Excel 283

Chapter 8 Interval Estimation 284

Statistics in Practice: Dollar General Corporation 285

8.1 Interval Estimation of a Population Mean: Large-Sample Case 286

CJW Estimation Problem 286

Sampling Error 287

Large-Sample Case With a Assumed Known 288

Large-Sample Case With a Estimated by s 291

Exercises 293

8.2 Interval Estimation of a Population Mean: Small-Sample Case 294

Small-Sample Case With a Assumed Known 295

Small-Sample Case With a Estimated by s 295

The Role of the Population Distribution 299

Exercises 300

8.3 Determining the Sample Size 303

Exercises 304

8.4 Interval Estimation of a Population Proportion 305

Determining the Sample Size 307

Exercises 309

Summary 310

Glossary 311

Key Formulas 312

Supplementary Exercises 312

Case Problem 1: Bock Investment Services 315

Case Problem 2: Gulf Real Estate Properties 317

Case Problem 3: Metropolitan Research, Inc. 317

Appendix 8.1 Interval Estimation of a Population Mean With Minitab 319

Appendix 8.2 Interval Estimation of a Population Mean With Excel 320

Chapter 9 Hypothesis Testing 323

Statistics in Practice: Harris Corporation 324

9.1 Developing Null and Alternative Hypotheses 325

Testing Research Hypotheses 325

Testing the Validity of a Claim 325

Testing in Decision-Making Situations 326

A Summary of Forms for Null and Alternative Hypotheses 326

Exercises 327

9.2 Type I and Type II Errors 327

Exercises 329

9.3 One-Tailed Tests About a Population Mean: Large-Sample Case 329

Using the Test Statistic 332

Using the p-Value 333

Summary: One-Tailed Tests About a Population Mean 335

Steps of Hypothesis Testing 336

Exercises 337

9.4 Two-Tailed Tests About a Population Mean: Large-Sample Case 339

p-Values for Two-Tailed Tests 341

Summary: Two-Tailed Tests About a Population Mean 342

Relationship Between Interval Estimation and Hypothesis Testing 342

Exercises 345

9.5 Tests About a Population Mean: Small-Sample Case 347

p-Values and the t Distribution 348

A Two-Tailed Test 349

Exercises 350

9.6 Test About a Population Proportion 353

Exercises 357

9.7 Hypothesis Testing and Decision Making 359

9.8 Calculating the Probability of Type II Errors 360

Exercises 363

9.9 Determining the Sample Size for a Hypothesis Test About a Population Mean 365

Exercises 368

Summary 369

Glossary 370

Key Formulas 371

Supplementary Exercises 371

Case Problem 1: Quality Associates, Inc. 374

Case Problem 2: Unemployment Study 375

Appendix 9.1 Hypothesis Testing With Minitab 376

Appendix 9.2 Hypothesis Testing With Excel 377

Chapter 10 Statistical Inference About Means and Proportions With Two Populations 380

Statistics in Practice: Fisons Corporation 381

10.1 Estimation of the Difference Between the Means of Two Populations: Independent Samples 382

Sampling Distributions of x1-x2, 383

Interval Estimate of u1-u2: Large-Sample Case 384

Interval Estimate of u1-u2: Small-Sample Case 386

Exercises 389

10.2 Hypothesis Tests About the Difference Between the Means of Two Populations: Independent Samples 391

Large-Sample Case 391

Small-Sample Case 394

Exercises 397

10.3 Inferences About the Difference Between the Means of Two Populations: Matched Samples 399

Exercises 402

10.4 Inferences About the Difference Between the Proportions of Two Populations 405

Sampling Distribution of px -- p2 405

Interval Estimation of px -- p2 406

Hypothesis Tests About p1 - p2 407

Exercises 409

Summary 411

Glossary 411

Key Formulas 412

Supplementary Exercises 414

Case Problem: Par, Inc. 416

Appendix 10.1 Two Population Means With Minitab 417

Appendix 10.2 Two Population Means With Excel 418

Chapter 11 Inferences About Population Variances 420

Statistics in Practice: U.S. General Accounting Office 421

11.1 Inferences About a Population Variance 422

Interval Estimation of a2 422

Hypothesis Testing 426

Exercises 430

11.2 Inferences About the Variances of Two Populations 432

Exercises 437

Summary 439

Key Formulas 439

Supplementary Exercises 440

Case Problem: Air Force Training Program 441

Appendix 11.1 Population Variances With Minitab 442

Appendix 11.2 Population Variances With Excel 444

Chapter 12 Tests of Goodness of Fit and Independence 446

Statistics in Practice: United Way 447

12.1 Goodness of Fit Test: A Multinomial Population 448

Exercises 451

12.2 Test of Independence 453

Exercises 457

12.3 Goodness of Fit Test: Poisson and Normal Distributions 460

Poisson Distribution 460

Normal Distribution 464

Exercises 467

Summary 469

Glossary 469

Key Formulas 469

Supplementary Exercises 470

Case Problem: A Bipartisan Agenda for Change 473

Appendix 12.1 Tests of Goodness of Fit and Independence With Minitab 474

Appendix 12.2 Tests of Goodness of Fit and Independence With Excel 475

Chapter 13 Analysis of Variance and Experimental Design 477

Statistics in Practice: Burke Marketing Services, Inc. 478

13.1 An Introduction to Analysis of Variance 478

Assumptions for Analysis of Variance 480

A Conceptual Overview 480

13.2 Analysis of Variance: Testing for the Equality of k Population Means 482

Between-Treatments Estimate of Population Variance 483

Within-Treatments Estimate of Population Variance 484

Comparing the Variance Estimates: The F Test 485

ANOVA Table 487

Computer Results for Analysis of Variance 487

Exercises 489

13.3 Multiple Comparison Procedures 492

Fisher's LSD 492

Type I Error Rates 495

Exercises 496

13.4 An Introduction to Experimental Design 497

Data Collection 499

13.5 Completely Randomized Designs 500

Between-Treatments Estimate of Population Variance 500

Within-Treatments Estimate of Population Variance 500

Comparing the Variance Estimates: The F Test 501

ANOVA Table 501

Pairwise Comparisons 501

Exercises 502

13.6 Randomized Block Design 505

Air Traffic Controller Stress Test 505

ANOVA Procedure 506

Computations and Conclusions 508

Exercises 510

13.7 Factorial Experiments 511

ANOVA Procedure 513

Computations and Conclusions 513

Exercises 517

Summary 519

Glossary 519

Key Formulas 520

Supplementary Exercises 523

Case Problem 1: Wentworth Medical Center 529

Case Problem 2: Compensation for ID Professionals 530

Appendix 13.1 Analysis of Variance and Experimental Design With Minitab 531

Appendix 13.2 Analysis of Variance and Experimental Design With Excel 532

Chapter 14 Simple Linear Regression 537

Statistics in Practice: Polaroid Corporation 538

14.1 Simple Linear Regression Model 539

Regression Model and Regression Equation 539

Estimated Regression Equation 540

14.2 Least Squares Method 541

Exercises 546

14.3 Coefficient of Determination 551

Correlation Coefficient 555

Exercises 556

14.4 Model Assumptions 558

14.5 Testing for Significance 560

Estimate of a2 560

t Test 561

Confidence Interval for B1 563

F Test 564

Some Cautions About the Interpretation of Significance Tests 566

Exercises 567

14.6 Using the Estimated Regression Equation for Estimation and Prediction 569

Point Estimation 569

Interval Estimation 569

Confidence Interval Estimate of the Mean Value of y 570

Prediction Interval Estimate of the Individual Value of y 572

Exercises 573

14.7 Computer Solution 575

Exercises 577

14.8 Residual Analysis: Validating Model Assumptions 579

Residual Plot Against x 580

Residual Plot Against y 583

Standardized Residuals 584

Normal Probability Plot 585

Exercises 587

14.9 Residual Analysis: Outliers and Influential Observations 588

Detecting Outliers 588

Detecting Influential Observations 591

Exercises 593

Summary 595

Glossary 596

Key Formulas 597

Supplementary Exercises 599

Case Problem 1: Spending and Student Achievement 604

Case Problem 2: U.S. Department of Transportation 606

Case Problem 3: Alumni Giving 607

Appendix 14.1 Calculus-Based Derivation of Least-Squares Formulas 607

Appendix 14.2 A Test for Significance Using Correlation 609

Appendix 14.3 Regression Analysis With Minitab 610

Appendix 14.4 Regression Analysis With Excel 611

Chapter 15 Multiple Regression 614

Statistics in Practice: Champion International Corporation 615

15.1 Multiple Regression Model 616

Regression Model and Regression Equation 616

Estimated Multiple Regression Equation 616

15.2 Least Squares Method 617

An Example: Butler Trucking Company 618

Note on Interpretation of Coefficients 621

Exercises 621

15.3 Multiple Coefficient of Determination 626

Exercises 627

15.4 Model Assumptions 629

15.5 Testing for Significance 630

F Test 630

t Test 633

Multicollinearity 634

Exercises 635

15.6 Using the Estimated Regression Equation for Estimation and Prediction 637

Exercises 638

15.7 Qualitative Independent Variables 639

An Example: Johnson Filtration, Inc. 639

Interpreting the Parameters 640

More Complex Qualitative Variables 642

Exercises 644

15.8 Residual Analysis 647

Detecting Outliers 648

Studentized Deleted Residuals and Outliers 649

Influential Observations 650

Using Cook's Distance Measure to Identify Influential Observations 650

Exercises 652

Summary 654

Glossary 655

Key Formulas 656

Supplementary Exercises 657

Case Problem 1: Consumer Research, Inc. 662

Case Problem 2: NFL Quarterback Rating 663

Case Problem 3: Predicting Student Proficiency Test Scores 665

Case Problem 4: Alumni Giving 665

Chapter 16 Regression Analysis: Model Building 668

Statistics in Practice: Monsanto Company 669

16.1 General Linear Model 670

Modeling Curvilinear Relationships 670

Interaction 674

Transformations Involving the Dependent Variable 676

Nonlinear Models That Are Intrinsically Linear 680

Exercises 681

16.2 Determining When to Add or Delete Variables 685

General Case 686

Use of p-Values 688

Exercises 688

16.3 Analysis of a Larger Problem 691

16.4 Variable Selection Procedures 695

Stepwise Regression 695

Forward Selection 697

Backward Elimination 697

Best-Subsets Regression 697

Making the Final Choice 698

Exercises 699

16.5 Residual Analysis 702

Autocorrelation and the Durbin-Watson Test 702

Exercises 708

16.6 Multiple Regression Approach to Analysis of Variance and Experimental Design 708

Exercises 711

Summary 712

Glossary 713

Key Formulas 713

Supplementary Problems 713

Case Problem 1: Unemployment Study 717

Case Problem 2: Analysis of PGA Tour Statistics 718

Case Problem 3: Predicting Graduation Rates for Colleges and Universities 719

Chapter 17 Index Numbers 721

Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics 722

17.1 Price Relatives 723

17.2 Aggregate Price Indexes 723

Exercises 726

17.3 Computing an Aggregate Price Index from Price Relatives 727

Exercises 728

17.4 Some Important Price Indexes 729

Consumer Price Index 729

Producer Price Index 730

Dow Jones Averages 730

17.5 Deflating a Series by Price Indexes 731

Exercises 733

17.6 Price Indexes: Other Considerations 734

Selection of Items 735

Selection of a Base Period 735

Quality Changes 735

17.7 Quantity Indexes 736

Exercises 737

Summary 737

Glossary 738

Key Formulas 738

Supplementary Exercises 739

Chapter 18 Forecasting 742

Statistics in Practice: Nevada Occupational Health Clinic 743

18.1 Components of a Time Series 744

Trend Component 744

Cyclical Component 745

Seasonal Component 746

Irregular Component 747

18.2 Smoothing Methods 747

Moving Averages 748

Weighted Moving Averages 750

Exponential Smoothing 751

Exercises 756

18.3 Trend Projection 758

Exercises 761

18.4 Trend and Seasonal Components 763

Multiplicative Model 764

Calculating the Seasonal Indexes 764

Deseasonalizing the Time Series 768

Using the Deseasonalized Time Series to Identify Trend 768

Seasonal Adjustments 771

Models Based on Monthly Data 771

Cyclical Component 771

Exercises 772

18.5 Regression Analysis 773

18.6 Qualitative Approaches 775

Delphi Method 775

Expert Judgment 776

Scenario Writing 776

Intuitive Approaches 776

Summary 776

Glossary 777

Key Formulas 778

Supplementary Exercises 778

Case Problem 1: Forecasting Food and Beverage Sales 782

Case Problem 2: Forecasting Lost Sales 783

Appendix 18.1 Forecasting With Minitab 785

Appendix 18.2 Forecasting With Excel 786

Chapter 19 Nonparametric Methods 788

Statistics in Practice: West Shell Realtors 789

19.1 Sign Test 791

Small-Sample Case 791

Large-Sample Case 793

Hypothesis Testing About a Median 794

Exercises 795

19.2 Wilcoxon Signed-Rank Test 797

Exercises 800

19.3 Mann-Whitney-Wilcoxon Test 802

Small-Sample Case 802

Large-Sample Case 804

Exercises 807

19.4 Kruskal-Wallis Test 810

Exercises 812

19.5 Rank Correlation 813

Test for Significant Rank Correlation 815

Exercises 815

Summary 817

Glossary 818

Key Formulas 818

Supplementary Exercises 819

Chapter 20 Statistical Methods for Quality Control 821

Statistics in Practice: Dow Chemical U.S.A. 822

20.1 Statistical Process Control 823

Control Charts 824

x Chart: Process Mean and Standard Deviation Known 825

x Chart: Process Mean and Standard Deviation Unknown 827

R Chart 830

p Chart 831

np Chart 833

Interpretation of Control Charts 834

Exercises 834

20.2 Acceptance Sampling 836

KALI, Inc.: An Example of Acceptance Sampling 838

Computing the Probability of Accepting a Lot 838

Selecting an Acceptance Sampling Plan 842

Multiple Sampling Plans 843

Exercises 844

Summary 845

Glossary 845

Key Formulas 846

Supplementary Exercises 847

Appendix 20.1 Control Charts With Minitab 849

Chapter 21 Sample Survey 850

Statistics in Practice: Cinergy 851

21.1 Terminology Used in Sample Surveys 851

21.2 Types of Surveys and Sampling Methods 852

21.3 Survey Errors 854

Nonsampling Error 854

Sampling Error 854

21.4 Simple Random Sampling 855

Population Mean 855

Population Total 856

Population Proportion 858

Determining the Sample Size 858

Exercises 860

21.5 Stratified Simple Random Sampling 861

Population Mean 862

Population Total 864

Population Proportion 864

Determining the Sample Size 865

Exercises 869

21.6 Cluster Sampling 870

Population Mean 872

Population Total 874

Population Proportion 874

Determining the Sample Size 876

Exercises 876

21.7 Systematic Sampling 878

Summary 878

Glossary 879

Key Formulas 879

Supplementary Exercises 883

Appendix A References and Bibliography 886

Appendix B Tables 888

Appendix C Summation Notation 916

Appendix D Answers to Even-Numbered Exerclses 918

Appendix E Solutions to Self-Test Exercises 936

Index 969