# Essentials of Statistics for Business and Economics, 9th Edition PDF by David R Anderson, Dennis J Sweeney, Thomas A Williams, Jeffrey D Camm

## Essentials of Statistics for Business and Economics, 9th Edition

By David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry and Jeffrey W. Ohlmann Contents:

PREFACE xxiii

Chapter 1 Data and Statistics 1

Statistics in Practice: Bloomberg Businessweek 2

1.1 Applications in Business and Economics 3

Accounting 3

Finance 3

Marketing 4

Production 4

Economics 4

Information Systems 4

1.2 Data 5

Elements, Variables, and Observations 5

Categorical and Quantitative Data 7

Cross-Sectional and Time Series Data 8

1.3 Data Sources 10

Existing Sources 10

Observational Study 11

Experiment 12

Time and Cost Issues 13

Data Acquisition Errors 13

1.4 Descriptive Statistics 13

1.5 Statistical Inference 15

1.6 Analytics 16

1.7 Big Data and Data Mining 17

1.8 Computers and Statistical Analysis 19

1.9 Ethical Guidelines for Statistical Practice 19

Summary 21

Glossary 21

Supplementary Exercises 22

Appendix 1.1 Opening and Saving DATA Files and Converting to

Stacked form with JMP 30

Appendix 1.2 Getting Started with R and RStudio

Appendix 1.3 Basic Data Manipulation in R

Chapter 2 D escriptive Statistics: Tabular and Graphical

Displays 33

Statistics in Practice: Colgate-Palmolive Company 34

2.1 Summarizing Data for a Categorical Variable 35

Frequency Distribution 35

Relative Frequency and Percent Frequency Distributions 36

Bar Charts and Pie Charts 37

2.2 Summarizing Data for a Quantitative Variable 42

Frequency Distribution 42

Relative Frequency and Percent Frequency Distributions 44

Dot Plot 45

Histogram 45

Cumulative Distributions 47

Stem-and-Leaf Display 47

2.3 Summarizing Data for Two Variables Using Tables 57

Crosstabulation 57

2.4 Summarizing Data for Two Variables Using Graphical

Displays 65

Scatter Diagram and Trendline 65

Side-by-Side and Stacked Bar Charts 66

2.5 Data Visualization: Best Practices in Creating Effective

Graphical Displays 71

Creating Effective Graphical Displays 71

Choosing the Type of Graphical Display 72

Data Dashboards 73

Data Visualization in Practice: Cincinnati Zoo

and Botanical Garden 75

Summary 77

Glossary 78

Key Formulas 79

Supplementary Exercises 80

Case Problem 1: Pelican Stores 85

Case Problem 2: Movie Theater Releases 86

Case Problem 3: Queen City 87

Case Problem 4: Cut-Rate Machining, Inc. 88

Appendix 2.1 Creating Tabular and Graphical Presentations with

JMP 90

Appendix 2.2 Creating Tabular and Graphical Presentations

with Excel 93

Appendix 2.3 Creating Tabular and Graphical Presentations with R

Chapter 3 Descriptive Statistics: Numerical Measures 107

Statistics in Practice: Small Fry Design 108

3.1 Measures of Location 109

Mean 109

Weighted Mean 111

Median 112

Geometric Mean 113

Mode 115

Percentiles 115

Quartiles 116

3.2 Measures of Variability 122

Range 123

Interquartile Range 123

Variance 123

Standard Deviation 125

Coefficient of Variation 126

3.3 Measures of Distribution Shape, Relative Location,

and Detecting Outliers 129

Distribution Shape 129

z-Scores 130

Chebyshev’s Theorem 131

Empirical Rule 132

Detecting Outliers 134

3.4 Five-Number Summaries and Boxplots 137

Five-Number Summary 138

Boxplot 138

Comparative Analysis Using Boxplots 139

3.5 Measures of Association Between Two Variables 142

Covariance 142

Interpretation of the Covariance 144

Correlation Coefficient 146

Interpretation of the Correlation Coefficient 147

3.6 Data Dashboards: Adding Numerical Measures to

Improve Effectiveness 150

Summary 153

Glossary 154

Key Formulas 155

Supplementary Exercises 156

Case Problem 1: Pelican Stores 162

Case Problem 2: Movie Theater Releases 163

Case Problem 3: Business Schools of Asia-Pacific 164

Case Problem 4: Heavenly Chocolates Website Transactions 164

Case Problem 5: African Elephant Populations 166

Appendix 3.1 Descriptive Statistics with JMP 168

Appendix 3.2 Descriptive Statistics with Excel 171

Appendix 3.3 Descriptive Statistics with R (MindTap Reader)

Chapter 4 Introduction to Probability 177

Statistics in Practice: National Aeronautics and Space

4.1 Random Experiments, Counting Rules, and Assigning Probabilities 179

Counting Rules, Combinations, and Permutations 180

Assigning Probabilities 184

Probabilities for the KP&L Project 185

4.2 Events and Their Probabilities 189

4.3 Some Basic Relationships of Probability 193

Complement of an Event 193

4.4 Conditional Probability 199

Independent Events 202

Multiplication Law 202

4.5 Bayes’ Theorem 207

Tabular Approach 210

Summary 212

Glossary 213

Key Formulas 214

Supplementary Exercises 214

Case Problem 1: Hamilton County Judges 219

Case Problem 2: Rob’s Market 221

Chapter 5 Discrete Probability Distributions 223

Statistics in Practice: Voter Waiting Times in Elections 224

5.1 Random Variables 225

Discrete Random Variables 225

Continuous Random Variables 225

5.2 Developing Discrete Probability Distributions 228

5.3 Expected Value and Variance 233

Expected Value 233

Variance 233

5.4 Bivariate Distributions, Covariance, and Financial

Portfolios 238

A Bivariate Empirical Discrete Probability Distribution 238

Financial Applications 241

Summary 244

5.5 Binomial Probability Distribution 247

A Binomial Experiment 248

Martin Clothing Store Problem 249

Using Tables of Binomial Probabilities 253

Expected Value and Variance for the Binomial

Distribution 254

5.6 Poisson Probability Distribution 258

An Example Involving Time Intervals 259

An Example Involving Length or Distance Intervals 260

5.7 Hypergeometric Probability Distribution 262

Summary 265

Glossary 266

Key Formulas 266

Supplementary Exercises 268

Case Problem 1: Go Bananas! Breakfast Cereal 272

Case Problem 2: McNeil’s Auto Mall 272

Case Problem 3: Grievance Committee at Tuglar Corporation 273

Appendix 5.1 Discrete Probability Distributions with JMP 275

Appendix 5.2 Discrete Probability Distributions with Excel 278

Appendix 5.3 Discrete Probability Distributions with R

Chapter 6 Continuous Probability Distributions 281

Statistics in Practice: Procter & Gamble 282

6.1 Uniform Probability Distribution 283

Area as a Measure of Probability 284

6.2 Normal Probability Distribution 287

Normal Curve 287

Standard Normal Probability Distribution 289

Computing Probabilities for Any Normal Probability

Distribution 294

Grear Tire Company Problem 294

6.3 Normal Approximation of Binomial Probabilities 299

6.4 Exponential Probability Distribution 302

Computing Probabilities for the Exponential

Distribution 302

Relationship Between the Poisson and Exponential

Distributions 303

Summary 305

Glossary 305

Key Formulas 306

Supplementary Exercises 306

Case Problem 1: Specialty Toys 309

Case Problem 2: Gebhardt Electronics 311

Appendix 6.1 Continuous Probability Distributions

with JMP 312

Appendix 6.2 Continuous Probability Distributions

with Excel 317

Appendix 6.3 Continuous Probability Distribution with R

Chapter 7 Sampling and Sampling Distributions 319

Statistics in Practice: Meadwestvaco Corporation 320

7.1 The Electronics Associates Sampling Problem 321

7.2 Selecting a Sample 322

Sampling from a Finite Population 322

Sampling from an Infinite Population 324

7.3 Point Estimation 327

7.4 Introduction to Sampling Distributions 331

7.5 Sampling Distribution of x 333

Expected Value of x 334

Standard Deviation of x 334

Form of the Sampling Distribution of x 335

Sampling Distribution of x for the EAI Problem 337

Practical Value of the Sampling Distribution of x 338

Relationship Between the Sample Size and the Sampling

Distribution of x 339

7.6 Sampling Distribution of p 343

Expected Value of p 344

Standard Deviation of p 344

Form of the Sampling Distribution of p 345

Practical Value of the Sampling Distribution of p 345

7.7 Properties of Point Estimators 349

Unbiased 349

Efficiency 350

Consistency 351

7.8 Other Sampling Methods 351

Stratified Random Sampling 352

Cluster Sampling 352

Systematic Sampling 353

Convenience Sampling 353

Judgment Sampling 354

7.9 Big Data and Standard Errors of Sampling Distributions 354

Sampling Error 354

Nonsampling Error 355

Big Data 356

Understanding What Big Data Is 356

Implications of Big Data for Sampling Error 357

Summary 360

Glossary 361

Key Formulas 362

Supplementary Exercises 363

Case Problem: Marion Dairies 366

Appendix 7.1 The Expected Value and Standard Deviation

of x– 367

Appendix 7.2 Random Sampling with JMP 368

Appendix 7.3 Random Sampling with Excel 371

Appendix 7.4 Random Sampling with R

Chapter 8 Interval Estimation 373

Statistics in Practice: Food Lion 374

8.1 Population Mean: s Known 375

Margin of Error and the Interval Estimate 375

8.2 Population Mean: s Unknown 381

Margin of Error and the Interval Estimate 382

Using a Small Sample 385

Summary of Interval Estimation Procedures 386

8.3 Determining the Sample Size 390

8.4 Population Proportion 393

Determining the Sample Size 394

8.5 Big Data and Confidence Intervals 398

Big Data and the Precision of Confidence Intervals 398

Implications of Big Data for Confidence Intervals 399

Summary 401

Glossary 402

Key Formulas 402

Supplementary Exercises 403

Case Problem 1: Young Professional Magazine 406

Case Problem 2: Gulf Real Estate Properties 407

Case Problem 3: Metropolitan Research, Inc. 409

Appendix 8.1 Interval Estimation with JMP 410

Appendix 8.2 Interval Estimation Using Excel 413

Appendix 8.3 Interval Estimation with R (MindTap Reader)

Chapter 9 Hypothesis Tests 417

Statistics in Practice: John Morrell & Company 418

9.1 Developing Null and Alternative Hypotheses 419

The Alternative Hypothesis as a Research Hypothesis 419

The Null Hypothesis as an Assumption to Be

Challenged 420

Summary of Forms for Null and Alternative

Hypotheses 421

9.2 Type I and Type II Errors 422

9.3 Population Mean: s Known 425

One-Tailed Test 425

Two-Tailed Test 430

Relationship Between Interval Estimation and

Hypothesis Testing 434

9.4 Population Mean: s Unknown 439

One-Tailed Test 439

Two-Tailed Test 440

9.5 Population Proportion 445

Summary 447

9.6 Hypothesis Testing and Decision Making 450

9.7 Calculating the Probability of Type II Errors 450

9.8 Determining the Sample Size for a Hypothesis Test

9.9 Big Data and Hypothesis Testing 459

Big Data, Hypothesis Testing, and p Values 459

Implications of Big Data in Hypothesis Testing 460

Summary 462

Glossary 462

Key Formulas 463

Supplementary Exercises 463

Case Problem 1: Quality Associates, Inc. 467

Case Problem 2: Ethical Behavior of Business Students at Bayview University 469

Appendix 9.1 Hypothesis Testing with JMP 471

Appendix 9.2 Hypothesis Testing with Excel 475

Appendix 9.3 Hypothesis Testing with R (MindTap Reader)

Chapter 10 Inference About Means and Proportions with Two Populations 481

Statistics in Practice: U.S. Food and Drug Administration 482

10.1 Inferences About the Difference Between Two

Population Means: s1 and s2 Known 483

Interval Estimation of m1 − m2 483

Hypothesis Tests About m1 − m2 485

10.2 Inferences About the Difference Between Two Population Means: s1 and s2 Unknown 489

Interval Estimation of m1 − m2 489

Hypothesis Tests About m1 − m2 491

10.3 Inferences About the Difference Between Two Population Means: Matched Samples 497

10.4 Inferences About the Difference Between Two Population Proportions 503

Interval Estimation of p1 − p2 503

Hypothesis Tests About p1 − p2 505

Summary 509

Glossary 509

Key Formulas 509

Supplementary Exercises 511

Case Problem: Par, Inc. 514

Appendix 10.1 Inferences About Two Populations with JMP 515

Appendix 10.2 Inferences About Two Populations with Excel 519

Chapter 11 Inferences About Population Variances 525

Statistics in Practice: U.S. Government Accountability Office 526

11.1 Inferences About a Population Variance 527

Interval Estimation 527

Hypothesis Testing 531

11.2 Inferences About Two Population Variances 537

Summary 544

Key Formulas 544

Supplementary Exercises 544

Case Problem 1: Air Force Training Program 546

Case Problem 2: Meticulous Drill & Reamer 547

Appendix 11.1 Population Variances with JMP 549

Appendix 11.2 Population Variances with Excel 551

Appendix 11.3 Population Variances with R (Mind Tap Reader)

Chapter 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit 553

Statistics in Practice: United Way 554

12.1 Testing the Equality of Population Proportions for Three or More Populations 555

A Multiple Comparison Procedure 560

12.2 Test of Independence 565

12.3 Goodness of Fit Test 573

Multinomial Probability Distribution 573

Normal Probability Distribution 576

Summary 582

Glossary 582

Key Formulas 583

Supplementary Exercises 583

Case Problem 1: A Bipartisan Agenda for Change 587

Case Problem 2: Fuentes Salty Snacks, Inc. 588

Case Problem 3: Fresno Board Games 588

Appendix 12.1 Chi-Square Tests with JMP 590

Appendix 12.2 Chi-Square Tests with Excel 593

Appendix 12.3 Chi-Squared Tests with R (Mind Tap Reader)

Chapter 13 Experimental Design and Analysis of Variance 597

Statistics in Practice: Burke Marketing Services, Inc. 598

13.1 An Introduction to Experimental Design

and Analysis of Variance 599

Data Collection 600

Assumptions for Analysis of Variance 601

Analysis of Variance: A Conceptual Overview 601

13.2 Analysis of Variance and the Completely

Randomized Design 604

Between-Treatments Estimate of Population Variance 605

Within-Treatments Estimate of Population Variance 606

Comparing the Variance Estimates: The F Test 606

ANOVA Table 608

Computer Results for Analysis of Variance 609

Testing for the Equality of k Population Means:

An Observational Study 610

13.3 Multiple Comparison Procedures 615

Fisher’s LSD 615

Type I Error Rates 617

13.4 Randomized Block Design 621

Air Traffic Controller Stress Test 621

ANOVA Procedure 623

Computations and Conclusions 623

13.5 Factorial Experiment 627

ANOVA Procedure 629

Computations and Conclusions 629

Summary 635

Glossary 635

Key Formulas 636

Supplementary Exercises 638

Case Problem 1: Wentworth Medical Center 643

Case Problem 2: Compensation for Sales

Professionals 644

Case Problem 3: Touristopia Travel 644

Appendix 13.1 Analysis of Variance with JMP 646

Appendix 13.2 Analysis of Variance with Excel 649

Appendix 13.3 Analysis Variance with R (MindTap Reader)

Chapter 14 Simple Linear Regression 653

Statistics in Practice: Alliance Data Systems 654

14.1 Simple Linear Regression Model 655

Regression Model and Regression Equation 655

Estimated Regression Equation 656

14.2 Least Squares Method 658

14.3 Coefficient of Determination 668

Correlation Coefficient 671

14.4 Model Assumptions 675

14.5 Testing for Significance 676

Estimate of s2 676

t Test 677

Confidence Interval for b1 679

F Test 679

Some Cautions About the Interpretation of Significance

Tests 681

14.6 Using the Estimated Regression Equation

for Estimation and Prediction 684

Interval Estimation 685

Confidence Interval for the Mean Value of y 685

Prediction Interval for an Individual Value of y 686

14.7 Computer Solution 691

14.8 Residual Analysis: Validating Model Assumptions 694

Residual Plot Against x 695

Residual Plot Against yˆ 697

Standardized Residuals 698

Normal Probability Plot 699

14.9 Residual Analysis: Outliers and Influential Observations 703

Detecting Outliers 703

Detecting Influential Observations 704

14.10 Practical Advice: Big Data and Hypothesis Testing in Simple

Linear Regression 710

Summary 711

Glossary 711

Key Formulas 712

Supplementary Exercises 714

Case Problem 1: Measuring Stock Market Risk 721

Case Problem 2: U.S. Department of Transportation 721

Case Problem 3: Selecting a Point-and-Shoot Digital Camera 722

Case Problem 4: Finding the Best Car Value 723

Case Problem 5: Buckeye Creek Amusement Park 724

Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 726

Appendix 14.2 A Test for Significance Using Correlation 727

Appendix 14.3 Simple Linear Regression with JMP 727

Appendix 14.4 Regression Analysis with Excel 728

Appendix 14.5 Simple Linear Regression with R

Chapter 15 Multiple Regression 731

Statistics in Practice: 84.51° 732

15.1 Multiple Regression Model 733

Regression Model and Regression Equation 733

Estimated Multiple Regression Equation 733

15.2 Least Squares Method 734

An Example: Butler Trucking Company 735

Note on Interpretation of Coefficients 737

15.3 Multiple Coefficient of Determination 743

15.4 Model Assumptions 746

15.5 Testing for Significance 747

F Test 747

t Test 750

Multicollinearity 750

15.6 Using the Estimated Regression Equation

for Estimation and Prediction 753

15.7 Categorical Independent Variables 755

An Example: Johnson Filtration, Inc. 756

Interpreting the Parameters 758

More Complex Categorical Variables 760

15.8 Residual Analysis 764

Detecting Outliers 766

Studentized Deleted Residuals and Outliers 766

Influential Observations 767

Using Cook’s Distance Measure to Identify

Influential Observations 767

15.9 Logistic Regression 771

Logistic Regression Equation 772

Estimating the Logistic Regression Equation 773

Testing for Significance 774

Managerial Use 775

Interpreting the Logistic Regression Equation 776

Logit Transformation 778

15.10 Practical Advice: Big Data and Hypothesis Testing

in Multiple Regression 782

Summary 783

Glossary 783

Key Formulas 784

Supplementary Exercises 786

Case Problem 1: Consumer Research, Inc. 790

Case Problem 2: Predicting Winnings for NASCAR Drivers 791

Case Problem 3: Finding the Best Car Value 792

Appendix 15.1 Multiple Linear Regression with JMP 794

Appendix 15.2 Logistic Regression with JMP 796

Appendix 15.3 Multiple Regression with Excel 797

Appendix 15.4 Multiple Linear Regression with R

Appendix 15.5 Logistics Regression with R

Appendix A References and Bibliography 800

Appendix B Tables 802

Appendix C Summation Notation 829

Appendix D  _Answers to Even-Numbered Exercises

Appendix E  _Microsoft Excel 2016 and Tools for Statistical

Analysis 831

Appendix F Computing p-Values with JMP and Excel 839

Index 843

University of Iowa

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