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Chapter 1 Data and Statistics1

Statistics in Practice: Business Week2

1.1 Applications in Business and Economics3

Accounting3

Finance3

Marketing4

Production4

Economics4

1.2 Data4

Elements, Variables, and Observations5

Scales of Measurement6

Qualitative and Quantitative Data7

Cross-Sectional and Time Series Data7

1.3 Data Sources8

Existing Sources8

Statistical Studies10

Data Acquisition Errors11

1.4 Descriptive Statistics12

1.5 Statistical Inference14

Summary16

Glossary16

Exercises17

Chapter 2 Descriptive Statistics: Tabular and Graphical Methods23

Statistics in Practice: Colgate-Palmolive Company24

2.1 Summarizing Qualitative Data25

Frequency Distribution25

Relative Frequency and Percent Frequency Distributions26

Bar Graphs and Pie Charts26

Exercises28

2.2 Summarizing Quantitative Data30

Frequency Distribution30

Relative Frequency and Percent Frequency Distributions32

Dot Plot32

Histogram33

Cumulative Distributions34

Ogive35

Exercises36

2.3 Exploratory Data Analysis: The Stem-and-Leaf Display39

Exercises42

2.4 Crosstabulations and Scatter Diagrams44

Crosstabulation44

Scatter Diagram45

Exercises47

Summary50

Glossary51

Key Formulas52

Supplementary Exercises52

Case Problem: Consolidated Foods58

Appendix 2.1 Using Minitab for Tabular and Graphical Methods59

Appendix 2.2 Using Excel for Tabular and Graphical Methods61

Chapter 3 Descriptive Statistics: Numerical Methods72

Statistics in Practice: Small Fry Designs73

3.1 Measures of Location74

Mean74

Median75

Mode76

Percentiles77

Quartiles78

Exercises79

3.2 Measures of Variability83

Range84

Interquartile Range84

Variance84

Standard Deviation86

Coefficient of Variation87

Exercises88

3.3 Measures of Relative Location and Detecting Outliers89

z-Scores90

Chebyshev's Theorem90

Empirical Rule91

Detecting Outliers92

Exercises93

3.4 Exploratory Data Analysis95

Five-Number Summary95

Box Plot96

Exercises97

3.5 Measures of Association Between Two Variables100

Co variance101

Interpretation of the Covariance102

Correlation Coefficient104

Interpretation of the Correlation Coefficient105

Exercises106

3.6 The Weighted Mean and Working With Grouped Data109

Weighted Mean109

Grouped Data110

Exercises112

Summary114

Glossary115

Key Formulas116

Supplementary Exercises117

Case Problem 1: Consolidated Foods, Inc.123

Case Problem 2: National Health Care Association124

Case Problem 3: Business Schools of Asia-Pacific125

Appendix 3.1 Descriptive Statistics With Minitab125

Appendix 3.2 Descriptive Statistics With Excel129

Chapter 4 Introduction to Probability132

Statistics in Practice: Morton International133

4.1 Experiments, Counting Rules, and Assigning Probabilities134

Counting Rules, Combinations, and Permutations135

Assigning Probabilities139

Probabilities for the KP＆L Project141

Exercises142

4.2 Events and Their Probabilities144

Exercises146

4.3 Some Basic Relationships of Probability148

Complement of an Event148

Addition Law149

Exercises152

4.4 Conditional Probability153

Independent Events156

Multiplication Law157

Exercises158

4.5 Bayes'Theorem161

Tabular Approach164

Exercises165

Summary167

Glossary167

Key Formulas168

Supplementary Exercises169

Case Problem: Hamilton County Judges173

Chapter 5 Discrete Probability Distributions175

Statistics in Practice: Citibank176

5.1 Random Variables176

Discrete Random Variables177

Continuous Random Variables177

Exercises178

5.2 Discrete Probability Distributions179

Exercises182

5.3 Expected Value and Variance184

Expected Value184

Variance185

Exercises186

5.4 Binomial Probability Distribution189

A Binomial Experiment189

Martin Clothing Store Problem190

Using Tables of Binomial Probabilities194

Expected Value and Variance for the Binomial Probability Distribution196

Exercises197

5.5 Poisson Probability Distribution199

An Example Involving Time Intervals199

An Example Involving Length or Distance Intervals200

Exercises201

5.6 Hypergeometric Probability Distribution203

Exercises204

Summary205

Glossary206

Key Formulas206

Supplementary Exercises207

Appendix 5.1 Discrete Probability Distributions With Minitab210

Appendix 5.2 Discrete Probability Distributions With Excel210

Chapter 6 Continuous Probability Distributions212

Statistics in Practice: Procter ＆ Gamble213

6.1 Uniform Probability Distribution214

Area as a Measure of Probability215

Exercises217

6.2 Normal Probability Distribution218

Normal Curve219

Standard Normal Probability Distribution221

Computing Probabilities for Any Normal Probability Distribution226

Grear Tire Company Problem227

Exercises229

6.3 Exponential Probability Distribution232

Computing Probabilities for the Exponential Distribution232

Relationship Between the Poisson and Exponential Distributions234

Exercises234

Summary236

Glossary236

Key Formulas236

Supplementary Exercises237

Appendix 6.1 Continuous Probability Distributions With Minitab239

Appendix 6.2 Continuous Probability Distributions With Excel240

Chapter 7 Sampling and Sampling Distributions241

Statistics in Practice: Mead Corporation242

7.1 The Electronics Associates Sampling Problem243

7.2 Simple Random Sampling244

Sampling from a Finite Population244

Sampling from an Infinite Population246

Exercises247

7.3 Point Estimation249

Exercises251

7.4 Introduction to Sampling Distributions252

7.5 Sampling Distribution of x255

Expected Value of x256

Standard Deviation of x256

Central Limit Theorem258

Sampling Distribution of x of the EAI Sampling Problem259

Practical Value of the Sampling Distribution of x260

Relationship Between the Sample Size and the Sampling Distribution of x261

Exercises263

7.6 Sampling Distribution of p265

Expected Value of p266

Standard Deviation of p267

Form of the Sampling Distribution of p267

Practical Value of the Sampling Distribution of p268

Exercises269

7.7 Properties of Point Estimators271

Unbiasedness271

Efficiency272

Consistency273

7.8 Other Sampling Methods273

Stratified Random Sampling274

Cluster Sampling274

Systematic Sampling275

Convenience Sampling275

Judgment Sampling276

Summary276

Glossary277

Key Formulas278

Supplementary Exercises278

Appendix 7.1 The Expected Value and Standard Deviation of x280

Appendix 7.2 Random Sampling With Minitab282

Appendix 7.3 Random Sampling With Excel283

Chapter 8 Interval Estimation284

Statistics in Practice: Dollar General Corporation285

8.1 Interval Estimation of a Population Mean: Large-Sample Case286

CJW Estimation Problem286

Sampling Error287

Large-Sample Case With a Assumed Known288

Large-Sample Case With a Estimated by s291

Exercises293

8.2 Interval Estimation of a Population Mean: Small-Sample Case294

Small-Sample Case With a Assumed Known295

Small-Sample Case With a Estimated by s295

The Role of the Population Distribution299

Exercises300

8.3 Determining the Sample Size303

Exercises304

8.4 Interval Estimation of a Population Proportion305

Determining the Sample Size307

Exercises309

Summary310

Glossary311

Key Formulas312

Supplementary Exercises312

Case Problem 1: Bock Investment Services315

Case Problem 2: Gulf Real Estate Properties317

Case Problem 3: Metropolitan Research, Inc.317

Appendix 8.1 Interval Estimation of a Population Mean With Minitab319

Appendix 8.2 Interval Estimation of a Population Mean With Excel320

Chapter 9 Hypothesis Testing323

Statistics in Practice: Harris Corporation324

9.1 Developing Null and Alternative Hypotheses325

Testing Research Hypotheses325

Testing the Validity of a Claim325

Testing in Decision-Making Situations326

A Summary of Forms for Null and Alternative Hypotheses326

Exercises327

9.2 Type I and Type II Errors327

Exercises329

9.3 One-Tailed Tests About a Population Mean: Large-Sample Case329

Using the Test Statistic332

Using the p-Value333

Summary: One-Tailed Tests About a Population Mean335

Steps of Hypothesis Testing336

Exercises337

9.4 Two-Tailed Tests About a Population Mean: Large-Sample Case339

p-Values for Two-Tailed Tests341

Summary: Two-Tailed Tests About a Population Mean342

Relationship Between Interval Estimation and Hypothesis Testing342

Exercises345

9.5 Tests About a Population Mean: Small-Sample Case347

p-Values and the t Distribution348

A Two-Tailed Test349

Exercises350

9.6 Test About a Population Proportion353

Exercises357

9.7 Hypothesis Testing and Decision Making359

9.8 Calculating the Probability of Type II Errors360

Exercises363

9.9 Determining the Sample Size for a Hypothesis Test About a Population Mean365

Exercises368

Summary369

Glossary370

Key Formulas371

Supplementary Exercises371

Case Problem 1: Quality Associates, Inc.374

Case Problem 2: Unemployment Study375

Appendix 9.1 Hypothesis Testing With Minitab376

Appendix 9.2 Hypothesis Testing With Excel377

Chapter 10 Statistical Inference About Means and Proportions With Two Populations380

Statistics in Practice: Fisons Corporation381

10.1 Estimation of the Difference Between the Means of Two Populations: Independent Samples382

Sampling Distributions of x1-x2,383

Interval Estimate of u1-u2: Large-Sample Case384

Interval Estimate of u1-u2: Small-Sample Case386

Exercises389

10.2 Hypothesis Tests About the Difference Between the Means of Two Populations: Independent Samples391

Large-Sample Case391

Small-Sample Case394

Exercises397

10.3 Inferences About the Difference Between the Means of Two Populations: Matched Samples399

Exercises402

10.4 Inferences About the Difference Between the Proportions of Two Populations405

Sampling Distribution of px -- p2405

Interval Estimation of px -- p2406

Hypothesis Tests About p1 - p2407

Exercises409

Summary411

Glossary411

Key Formulas412

Supplementary Exercises414

Case Problem: Par, Inc.416

Appendix 10.1 Two Population Means With Minitab417

Appendix 10.2 Two Population Means With Excel418

Chapter 11 Inferences About Population Variances420

Statistics in Practice: U.S. General Accounting Office421

11.1 Inferences About a Population Variance422

Interval Estimation of a2422

Hypothesis Testing426

Exercises430

11.2 Inferences About the Variances of Two Populations432

Exercises437

Summary439

Key Formulas439

Supplementary Exercises440

Case Problem: Air Force Training Program441

Appendix 11.1 Population Variances With Minitab442

Appendix 11.2 Population Variances With Excel444

Chapter 12 Tests of Goodness of Fit and Independence446

Statistics in Practice: United Way447

12.1 Goodness of Fit Test: A Multinomial Population448

Exercises451

12.2 Test of Independence453

Exercises457

12.3 Goodness of Fit Test: Poisson and Normal Distributions460

Poisson Distribution460

Normal Distribution464

Exercises467

Summary469

Glossary469

Key Formulas469

Supplementary Exercises470

Case Problem: A Bipartisan Agenda for Change473

Appendix 12.1 Tests of Goodness of Fit and Independence With Minitab474

Appendix 12.2 Tests of Goodness of Fit and Independence With Excel475

Chapter 13 Analysis of Variance and Experimental Design477

Statistics in Practice: Burke Marketing Services, Inc.478

13.1 An Introduction to Analysis of Variance478

Assumptions for Analysis of Variance480

A Conceptual Overview480

13.2 Analysis of Variance: Testing for the Equality of k Population Means482

Between-Treatments Estimate of Population Variance483

Within-Treatments Estimate of Population Variance484

Comparing the Variance Estimates: The F Test485

ANOVA Table487

Computer Results for Analysis of Variance487

Exercises489

13.3 Multiple Comparison Procedures492

Fisher's LSD492

Type I Error Rates495

Exercises496

13.4 An Introduction to Experimental Design497

Data Collection499

13.5 Completely Randomized Designs500

Between-Treatments Estimate of Population Variance500

Within-Treatments Estimate of Population Variance500

Comparing the Variance Estimates: The F Test501

ANOVA Table501

Pairwise Comparisons501

Exercises502

13.6 Randomized Block Design505

Air Traffic Controller Stress Test505

ANOVA Procedure506

Computations and Conclusions508

Exercises510

13.7 Factorial Experiments511

ANOVA Procedure513

Computations and Conclusions513

Exercises517

Summary519

Glossary519

Key Formulas520

Supplementary Exercises523

Case Problem 1: Wentworth Medical Center529

Case Problem 2: Compensation for ID Professionals530

Appendix 13.1 Analysis of Variance and Experimental Design With Minitab531

Appendix 13.2 Analysis of Variance and Experimental Design With Excel532

Chapter 14 Simple Linear Regression537

Statistics in Practice: Polaroid Corporation538

14.1 Simple Linear Regression Model539

Regression Model and Regression Equation539

Estimated Regression Equation540

14.2 Least Squares Method541

Exercises546

14.3 Coefficient of Determination551

Correlation Coefficient555

Exercises556

14.4 Model Assumptions558

14.5 Testing for Significance560

Estimate of a2560

t Test561

Confidence Interval for B1563

F Test564

Some Cautions About the Interpretation of Significance Tests566

Exercises567

14.6 Using the Estimated Regression Equation for Estimation and Prediction569

Point Estimation569

Interval Estimation569

Confidence Interval Estimate of the Mean Value of y570

Prediction Interval Estimate of the Individual Value of y572

Exercises573

14.7 Computer Solution575

Exercises577

14.8 Residual Analysis: Validating Model Assumptions579

Residual Plot Against x580

Residual Plot Against y583

Standardized Residuals584

Normal Probability Plot585

Exercises587

14.9 Residual Analysis: Outliers and Influential Observations588

Detecting Outliers588

Detecting Influential Observations591

Exercises593

Summary595

Glossary596

Key Formulas597

Supplementary Exercises599

Case Problem 1: Spending and Student Achievement604

Case Problem 2: U.S. Department of Transportation606

Case Problem 3: Alumni Giving607

Appendix 14.1 Calculus-Based Derivation of Least-Squares Formulas607

Appendix 14.2 A Test for Significance Using Correlation609

Appendix 14.3 Regression Analysis With Minitab610

Appendix 14.4 Regression Analysis With Excel611

Chapter 15 Multiple Regression614

Statistics in Practice: Champion International Corporation615

15.1 Multiple Regression Model616

Regression Model and Regression Equation616

Estimated Multiple Regression Equation616

15.2 Least Squares Method617

An Example: Butler Trucking Company618

Note on Interpretation of Coefficients621

Exercises621

15.3 Multiple Coefficient of Determination626

Exercises627

15.4 Model Assumptions629

15.5 Testing for Significance630

F Test630

t Test633

Multicollinearity634

Exercises635

15.6 Using the Estimated Regression Equation for Estimation and Prediction637

Exercises638

15.7 Qualitative Independent Variables639

An Example: Johnson Filtration, Inc.639

Interpreting the Parameters640

More Complex Qualitative Variables642

Exercises644

15.8 Residual Analysis647

Detecting Outliers648

Studentized Deleted Residuals and Outliers649

Influential Observations650

Using Cook's Distance Measure to Identify Influential Observations650

Exercises652

Summary654

Glossary655

Key Formulas656

Supplementary Exercises657

Case Problem 1: Consumer Research, Inc.662

Case Problem 2: NFL Quarterback Rating663

Case Problem 3: Predicting Student Proficiency Test Scores665

Case Problem 4: Alumni Giving665

Chapter 16 Regression Analysis: Model Building668

Statistics in Practice: Monsanto Company669

16.1 General Linear Model670

Modeling Curvilinear Relationships670

Interaction674

Transformations Involving the Dependent Variable676

Nonlinear Models That Are Intrinsically Linear680

Exercises681

16.2 Determining When to Add or Delete Variables685

General Case686

Use of p-Values688

Exercises688

16.3 Analysis of a Larger Problem691

16.4 Variable Selection Procedures695

Stepwise Regression695

Forward Selection697

Backward Elimination697

Best-Subsets Regression697

Making the Final Choice698

Exercises699

16.5 Residual Analysis702

Autocorrelation and the Durbin-Watson Test702

Exercises708

16.6 Multiple Regression Approach to Analysis of Variance and Experimental Design708

Exercises711

Summary712

Glossary713

Key Formulas713

Supplementary Problems713

Case Problem 1: Unemployment Study717

Case Problem 2: Analysis of PGA Tour Statistics718

Case Problem 3: Predicting Graduation Rates for Colleges and Universities719

Chapter 17 Index Numbers721

Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics722

17.1 Price Relatives723

17.2 Aggregate Price Indexes723

Exercises726

17.3 Computing an Aggregate Price Index from Price Relatives727

Exercises728

17.4 Some Important Price Indexes729

Consumer Price Index729

Producer Price Index730

Dow Jones Averages730

17.5 Deflating a Series by Price Indexes731

Exercises733

17.6 Price Indexes: Other Considerations734

Selection of Items735

Selection of a Base Period735

Quality Changes735

17.7 Quantity Indexes736

Exercises737

Summary737

Glossary738

Key Formulas738

Supplementary Exercises739

Chapter 18 Forecasting742

Statistics in Practice: Nevada Occupational Health Clinic743

18.1 Components of a Time Series744

Trend Component744

Cyclical Component745

Seasonal Component746

Irregular Component747

18.2 Smoothing Methods747

Moving Averages748

Weighted Moving Averages750

Exponential Smoothing751

Exercises756

18.3 Trend Projection758

Exercises761

18.4 Trend and Seasonal Components763

Multiplicative Model764

Calculating the Seasonal Indexes764

Deseasonalizing the Time Series768

Using the Deseasonalized Time Series to Identify Trend768

Seasonal Adjustments771

Models Based on Monthly Data771

Cyclical Component771

Exercises772

18.5 Regression Analysis773

18.6 Qualitative Approaches775

Delphi Method775

Expert Judgment776

Scenario Writing776

Intuitive Approaches776

Summary776

Glossary777

Key Formulas778

Supplementary Exercises778

Case Problem 1: Forecasting Food and Beverage Sales782

Case Problem 2: Forecasting Lost Sales783

Appendix 18.1 Forecasting With Minitab785

Appendix 18.2 Forecasting With Excel786

Chapter 19 Nonparametric Methods788

Statistics in Practice: West Shell Realtors789

19.1 Sign Test791

Small-Sample Case791

Large-Sample Case793

Hypothesis Testing About a Median794

Exercises795

19.2 Wilcoxon Signed-Rank Test797

Exercises800

19.3 Mann-Whitney-Wilcoxon Test802

Small-Sample Case802

Large-Sample Case804

Exercises807

19.4 Kruskal-Wallis Test810

Exercises812

19.5 Rank Correlation813

Test for Significant Rank Correlation815

Exercises815

Summary817

Glossary818

Key Formulas818

Supplementary Exercises819

Chapter 20 Statistical Methods for Quality Control821

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

20.1 Statistical Process Control823

Control Charts824

x Chart: Process Mean and Standard Deviation Known825

x Chart: Process Mean and Standard Deviation Unknown827

R Chart830

p Chart831

np Chart833

Interpretation of Control Charts834

Exercises834

20.2 Acceptance Sampling836

KALI, Inc.: An Example of Acceptance Sampling838

Computing the Probability of Accepting a Lot838

Selecting an Acceptance Sampling Plan842

Multiple Sampling Plans843

Exercises844

Summary845

Glossary845

Key Formulas846

Supplementary Exercises847

Appendix 20.1 Control Charts With Minitab849

Chapter 21 Sample Survey850

Statistics in Practice: Cinergy851

21.1 Terminology Used in Sample Surveys851

21.2 Types of Surveys and Sampling Methods852

21.3 Survey Errors854

Nonsampling Error854

Sampling Error854

21.4 Simple Random Sampling855

Population Mean855

Population Total856

Population Proportion858

Determining the Sample Size858

Exercises860

21.5 Stratified Simple Random Sampling861

Population Mean862

Population Total864

Population Proportion864

Determining the Sample Size865

Exercises869

21.6 Cluster Sampling870

Population Mean872

Population Total874

Population Proportion874

Determining the Sample Size876

Exercises876

21.7 Systematic Sampling878

Summary878

Glossary879

Key Formulas879

Supplementary Exercises883

Appendix A References and Bibliography886

Appendix B Tables888

Appendix C Summation Notation916

Appendix D Answers to Even-Numbered Exerclses918

Appendix E Solutions to Self-Test Exercises936

Index969