# Essentials of Modern Business Statistics with Microsoft ® Excel ®, 8th Edition PDF by David R Anderson, Dennis J Sweeney, Thomas A Williams, Jeffrey D Camm, James J Cochran, Michael J Fry, and Jeffrey W Ohlmann

## Essentials of Modern Business Statistics with Microsoft ® Excel ®, Eight 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 xix

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

1.2 Data 5

Elements, Variables, and Observations 5

Scales of Measurement 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 Statistical Analysis Using Microsoft Excel 16

Data Sets and Excel Worksheets 17

Using Excel for Statistical Analysis 18

1.7 Analytics 20

1.8 Big Data and Data Mining 21

1.9 Ethical Guidelines for Statistical Practice 22

Summary 24

Glossary 24

Supplementary Exercises 25

Appendix 1.1 Getting Started with R and RStudio (MindTap Reader)

Appendix 1.2 Basic Data Manipulation in R (MindTap Reader)

Chapter 2 Descriptive Statistics: Tabular and Graphical Displays 35

Statistics in Practice: Colgate-Palmolive Company 36

2.1 Summarizing Data for a Categorical Variable 37

Frequency Distribution 37

Relative Frequency and Percent Frequency Distributions 38

Using Excel to Construct a Frequency Distribution, a Relative

Frequency Distribution, and a Percent Frequency Distribution 39

Bar Charts and Pie Charts 40

Using Excel to Construct a Bar Chart 42

2.2 Summarizing Data for a Quantitative Variable 47

Frequency Distribution 47

Relative Frequency and Percent Frequency Distributions 49

Using Excel to Construct a Frequency Distribution 50

Dot Plot 51

Histogram 52

Using Excel’s Recommended Charts Tool to Construct

a Histogram 54

Cumulative Distributions 55

Stem-and-Leaf Display 56

2.3 Summarizing Data for Two Variables Using Tables 65

Crosstabulation 65

Using Excel’s PivotTable Tool to Construct a Crosstabulation 68

2.4 Summarizing Data for Two Variables Using Graphical Displays 75

Scatter Diagram and Trendline 76

Using Excel to Construct a Scatter Diagram and a Trendline 77

Side-by-Side and Stacked Bar Charts 79

Using Excel’s Recommended Charts Tool to Construct

Side-by-Side and Stacked Bar Charts 81

2.5 Data Visualization: Best Practices in Creating Effective Graphical

Displays 85

Creating Effective Graphical Displays 85

Choosing the Type of Graphical Display 86

Data Dashboards 86

Data Visualization in Practice: Cincinnati Zoo

and Botanical Garden 88

Summary 90

Glossary 91

Key Formulas 92

Supplementary Exercises 93

Case Problem 1: Pelican Stores 98

Case Problem 2: Movie Theater Releases 99

Case Problem 3: Queen City 100

Case Problem 4: Cut-Rate Machining, Inc. 100

Appendix 2.1 Creating Tabular and Graphical Presentations with R

Chapter 3 Descriptive Statistics: Numerical Measures 103

Statistics in Practice: Small Fry Design 104

3.1 Measures of Location 105

Mean 105

Median 107

Mode 108

Using Excel to Compute the Mean, Median, and Mode 109

Weighted Mean 109

Geometric Mean 111

Using Excel to Compute the Geometric Mean 112

Percentiles 113

Quartiles 114

Using Excel to Compute Percentiles and Quartiles 115

3.2 Measures of Variability 121

Range 122

Interquartile Range 122

Variance 122

Standard Deviation 124

Using Excel to Compute the Sample Variance and Sample

Standard Deviation 125

Coefficient of Variation 126

Using Excel’s Descriptive Statistics Tool 126

3.3 Measures of Distribution Shape, Relative Location,

and Detecting Outliers 130

Distribution Shape 130

z-Scores 131

Chebyshev’s Theorem 132

Empirical Rule 133

Detecting Outliers 134

3.4 Five-Number Summaries and Boxplots 138

Five-Number Summary 138

Boxplot 138

Using Excel to Construct a Boxplot 139

Comparative Analysis Using Boxplots 139

Using Excel to Construct a Comparative Analysis

Using Boxplots 140

3.5 Measures of Association Between Two Variables 144

Covariance 144

Interpretation of the Covariance 146

Correlation Coefficient 148

Interpretation of the Correlation Coefficient 149

Using Excel to Compute the Sample Covariance

and Sample Correlation Coefficient 151

3.6 Data Dashboards: Adding Numerical Measures to Improve

Effectiveness 153

Summary 156

Glossary 157

Key Formulas 158

Supplementary Exercises 159

Case Problem 1: Pelican Stores 165

Case Problem 2: Movie Theater Releases 166

Case Problem 3: Business Schools of Asia-Pacific 167

Case Problem 4: Heavenly Chocolates Website Transactions 167

Case Problem 5: African Elephant Populations 169

Appendix 3.1 Descriptive Statistics with R (MindTap Reader)

Chapter 4 Introduction to Probability 171

Statistics in Practice: National Aeronautics and Space Administration 172

4.1 Experiments, Counting Rules, and Assigning Probabilities 173

Counting Rules, Combinations, and Permutations 174

Assigning Probabilities 178

Probabilities for the KP&L Project 179

4.2 Events and Their Probabilities 183

4.3 Some Basic Relationships of Probability 187

Complement of an Event 187

4.4 Conditional Probability 193

Independent Events 196

Multiplication Law 196

4.5 Bayes’ Theorem 201

Tabular Approach 204

Summary 206

Glossary 207

Key Formulas 208

Supplementary Exercises 208

Case Problem 1: Hamilton County Judges 213

Case Problem 2: Rob’s Market 215

Chapter 5 Discrete Probability Distributions 217

Statistics in Practice: Voter Waiting Times in Elections 218

5.1 Random Variables 218

Discrete Random Variables 219

Continuous Random Variables 220

5.2 Developing Discrete Probability Distributions 221

5.3 Expected Value and Variance 226

Expected Value 226

Variance 227

Using Excel to Compute the Expected Value, Variance,

and Standard Deviation 228

5.4 Bivariate Distributions, Covariance, and Financial Portfolios 233

A Bivariate Empirical Discrete Probability Distribution 233

Financial Applications 236

Summary 239

5.5 Binomial Probability Distribution 242

A Binomial Experiment 242

Martin Clothing Store Problem 244

Using Excel to Compute Binomial Probabilities 248

Expected Value and Variance for the Binomial

Distribution 249

5.6 Poisson Probability Distribution 252

An Example Involving Time Intervals 253

An Example Involving Length or Distance Intervals 254

Using Excel to Compute Poisson Probabilities 254

5.7 Hypergeometric Probability Distribution 257

Using Excel to Compute Hypergeometric Probabilities 259

Summary 261

Glossary 262

Key Formulas 263

Supplementary Exercises 264

Case Problem 1: Go Bananas! Breakfast Cereal 268

Case Problem 2: McNeil’s Auto Mall 269

Case Problem 3: Grievance Committee at Tuglar Corporation 270

Case Problem 4: Sagittarius Casino 270

Appendix 5.1 Discrete Probability Distributions with R (MindTap Reader)

Chapter 6 Continuous Probability Distributions 273

Statistics in Practice: Procter & Gamble 274

6.1 Uniform Probability Distribution 275

Area as a Measure of Probability 276

6.2 Normal Probability Distribution 279

Normal Curve 279

Standard Normal Probability Distribution 281

Computing Probabilities for Any Normal Probability

Distribution 285

Grear Tire Company Problem 286

Using Excel to Compute Normal Probabilities 288

6.3 Exponential Probability Distribution 293

Computing Probabilities for the Exponential Distribution 294

Relationship Between the Poisson

and Exponential Distributions 295

Using Excel to Compute Exponential Probabilities 295

Summary 298

Glossary 298

Key Formulas 298

Supplementary Exercises 299

Case Problem 1: Specialty Toys 301

Case Problem 2: Gebhardt Electronics 302

Appendix 6.1 Continuous Probability Distributions with R

Chapter 7 Sampling and Sampling Distributions 305

Statistics in Practice: The Food and Agriculture Organization 306

7.1 The Electronics Associates Sampling Problem 307

7.2 Selecting a Sample 308

Sampling from a Finite Population 308

Sampling from an Infinite Population 312

7.3 Point Estimation 316

7.4 Introduction to Sampling Distributions 319

7.5 Sampling Distribution of x 322

Expected Value of x 322

Standard Deviation of x 322

Form of the Sampling Distribution of x 324

Sampling Distribution of x for the EAI Problem 324

Practical Value of the Sampling Distribution of x 325

Relationship Between the Sample Size

and the Sampling Distribution of x 327

7.6 Sampling Distribution of p 331

Expected Value of p 332

Standard Deviation of p 332

Form of the Sampling Distribution of p 333

Practical Value of the Sampling Distribution of p 333

7.7 Other Sampling Methods 337

Stratified Random Sampling 337

Cluster Sampling 337

Systematic Sampling 338

Convenience Sampling 338

Judgment Sampling 339

7.8 Practical Advice: Big Data and Errors in Sampling 339

Sampling Error 339

Nonsampling Error 340

Big Data 341

Understanding What Big Data Is 342

Implications of Big Data for Sampling Error 343

Summary 348

Glossary 348

Key Formulas 349

Supplementary Exercises 350

Case Problem: Marion Dairies 353

Appendix 7.1 Random Sampling with R (MindTap Reader)

Chapter 8 Interval Estimation 355

Statistics in Practice: Food Lion 356

8.1 Population Mean: _ Known 357

Margin of Error and the Interval Estimate 357

Using Excel 361

8.2 Population Mean: _ Unknown 364

Margin of Error and the Interval Estimate 365

Using Excel 368

Using a Small Sample 369

Summary of Interval Estimation Procedures 371

8.3 Determining the Sample Size 374

8.4 Population Proportion 377

Using Excel 378

Determining the Sample Size 380

8.5 Practical Advice: Big Data and Interval Estimation 384

Big Data and the Precision of Confidence Intervals 384

Implications of Big Data for Confidence Intervals 385

Summary 387

Glossary 388

Key Formulas 388

Supplementary Exercises 389

Case Problem 1: Young Professional Magazine 392

Case Problem 2: GULF Real Estate Properties 393

Case Problem 3: Metropolitan Research, Inc. 395

Appendix 8.1 Interval Estimation with R (MindTap Reader)

Chapter 9 Hypothesis Tests 397

Statistics in Practice: John Morrell & Company 398

9.1 Developing Null and Alternative Hypotheses 399

The Alternative Hypothesis as a Research Hypothesis 399

The Null Hypothesis as an Assumption to Be Challenged 400

Summary of Forms for Null and Alternative Hypotheses 401

9.2 Type I and Type II Errors 402

9.3 Population Mean: s Known 405

One-Tailed Test 405

Two-Tailed Test 410

Using Excel 413

Relationship Between Interval Estimation

and Hypothesis Testing 415

9.4 Population Mean: s Unknown 420

One-Tailed Test 421

Two-Tailed Test 422

Using Excel 423

9.5 Population Proportion 428

Using Excel 430

Summary 431

9.6 Practical Advice: Big Data and Hypothesis Testing 434

Big Data, Hypothesis Testing, and p-Values 434

Implications of Big Data in Hypothesis Testing 436

Summary 437

Glossary 438

Key Formulas 438

Supplementary Exercises 439

Case Problem 1: Quality Associates, Inc. 442

Case Problem 2: Ethical Behavior of Business Students at Bayview

University 443

Appendix 9.1 Hypothesis Testing with R (MindTap Reader)

Chapter 10 Inference About Means and Proportions with Two

Populations 445

Statistics in Practice: U.S. Food and Drug Administration 446

10.1 Inferences About the Difference Between Two Population Means:

s1 and s2 Known 447

Interval Estimation of m1 2 m2 447

Using Excel to Construct a Confidence Interval 449

Hypothesis Tests About m1 2 m2 451

Using Excel to Conduct a Hypothesis Test 452

10.2 Inferences About the Difference Between

Two Population Means: s1 and s2 Unknown 456

Interval Estimation of m1 2 m2 457

Using Excel to Construct a Confidence Interval 458

Hypothesis Tests About m1 2 m2 460

Using Excel to Conduct a Hypothesis Test 462

10.3 Inferences About the Difference Between Two Population Means:

Matched Samples 467

Using Excel to Conduct a Hypothesis Test 469

10.4 Inferences About the Difference Between

Two Population Proportions 474

Interval Estimation of p1 2 p2 474

Using Excel to Construct a Confidence Interval 476

Hypothesis Tests About p1 2 p2 477

Using Excel to Conduct a Hypothesis Test 479

Summary 483

Glossary 483

Key Formulas 483

Supplementary Exercises 485

Case Problem: Par, Inc. 488

Chapter 11 Inferences About Population Variances 489

Statistics in Practice: U.S. Government Accountability Office 490

11.1 Inferences About a Population Variance 491

Interval Estimation 491

Using Excel to Construct a Confidence Interval 495

Hypothesis Testing 496

Using Excel to Conduct a Hypothesis Test 498

11.2 Inferences About Two Population Variances 503

Using Excel to Conduct a Hypothesis Test 507

Summary 511

Key Formulas 511

Supplementary Exercises 511

Case Problem 1: Air Force Training Program 513

Case Problem 2: Meticulous Drill & Reamer 514

Appendix 11.1 Population Variances with R (MindTap Reader)

Chapter 12 Tests of Goodness of Fit, Independence, and Multiple

Proportions 517

Statistics in Practice: United Way 518

12.1 Goodness of Fit Test 519

Multinomial Probability Distribution 519

Using Excel to Conduct a Goodness of Fit Test 523

12.2 Test of Independence 525

Using Excel to Conduct a Test of Independence 529

12.3 Testing for Equality of Three or More Population Proportions 534

A Multiple Comparison Procedure 537

Using Excel to Conduct a Test of Multiple Proportions 539

Summary 543

Glossary 544

Key Formulas 544

Supplementary Exercises 544

Case Problem 1: A Bipartisan Agenda for Change 547

Case Problem 2: Fuentes Salty Snacks, Inc. 548

Case Problem 3: Fresno Board Games 549

Appendix 12.1 Chi-Square Tests with R (MindTap Reader)

Chapter 13 Experimental Design and Analysis of Variance 551

Statistics in Practice: Burke, Inc. 552

13.1 An Introduction to Experimental Design and Analysis of

Variance 553

Data Collection 554

Assumptions for Analysis of Variance 556

Analysis of Variance: A Conceptual Overview 556

13.2 Analysis of Variance and the Completely Randomized Design 558

Between-Treatments Estimate of Population Variance 559

Within-Treatments Estimate of Population Variance 560

Comparing the Variance Estimates: The F Test 561

ANOVA Table 562

Using Excel 563

Testing for the Equality of k Population Means:

An Observational Study 564

13.3 Multiple Comparison Procedures 570

Fisher’s LSD 570

Type I Error Rates 572

13.4 Randomized Block Design 575

Air Traffic Controller Stress Test 576

ANOVA Procedure 577

Computations and Conclusions 578

Using Excel 579

13.5 Factorial Experiment 584

ANOVA Procedure 585

Computations and Conclusions 586

Using Excel 589

Summary 593

Glossary 594

Key Formulas 595

Completely Randomized Design 595

Multiple Comparison Procedures 596

Randomized Block Design 596

Factorial Experiment 596

Supplementary Exercises 596

Case Problem 1: Wentworth Medical Center 601

Case Problem 2: Compensation for Sales Professionals 602

Case Problem 3: TourisTopia Travel 603

Appendix 13.1 Analysis of Variance with R (MindTap Reader)

Chapter 14 Simple Linear Regression 605

Statistics in Practice: walmart.com 606

14.1 Simple Linear Regression Model 607

Regression Model and Regression Equation 607

Estimated Regression Equation 609

14.2 Least Squares Method 610

Using Excel to Construct a Scatter Diagram, Display

the Estimated Regression Line, and Display the Estimated

Regression Equation 614

14.3 Coefficient of Determination 621

Using Excel to Compute the Coefficient of Determination 625

Correlation Coefficient 626

14.4 Model Assumptions 629

14.5 Testing for Significance 631

Estimate of s2 631

t Test 632

Confidence Interval for b1 633

F Test 634

Some Cautions About the Interpretation of Significance Tests 636

14.6 Using the Estimated Regression Equation for Estimation

and Prediction 639

Interval Estimation 640

Confidence Interval for the Mean Value of y 640

Prediction Interval for an Individual Value of y 641

14.7 Excel’s Regression Tool 646

Using Excel’s Regression Tool for the Armand’s Pizza

Parlors Example 646

Interpretation of Estimated Regression Equation Output 647

Interpretation of ANOVA Output 648

Interpretation of Regression Statistics Output 649

14.8 Residual Analysis: Validating Model Assumptions 651

Residual Plot Against x 652

Residual Plot Against y⁄ 653

Standardized Residuals 655

Using Excel to Construct a Residual Plot 657

Normal Probability Plot 660

14.9 Outliers and Influential Observations 663

Detecting Outliers 663

Detecting Influential Observations 665

14.10 Practical Advice: Big Data and Hypothesis Testing in Simple

Linear Regression 670

Summary 671

Glossary 671

Key Formulas 672

Supplementary Exercises 674

Case Problem 1: Measuring Stock Market Risk 678

Case Problem 2: U.S. Department of Transportation 679

Case Problem 3: Selecting a Point-and-Shoot Digital Camera 680

Case Problem 4: Finding the Best Car Value 681

Case Problem 5: Buckeye Creek Amusement Park 682

Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 683

Appendix 14.2 A Test for Significance Using Correlation 684

Appendix 14.3 Simple Linear Regression with R (MindTap Reader)

Chapter 15 Multiple Regression 685

Statistics in Practice: International Paper 686

15.1 Multiple Regression Model 687

Regression Model and Regression Equation 687

Estimated Multiple Regression Equation 687

15.2 Least Squares Method 688

An Example: Butler Trucking Company 689

Using Excel’s Regression Tool to Develop the Estimated Multiple

Regression Equation 691

Note on Interpretation of Coefficients 693

15.3 Multiple Coefficient of Determination 698

15.4 Model Assumptions 700

15.5 Testing for Significance 702

F Test 702

t Test 704

Multicollinearity 705

15.6 Using the Estimated Regression Equation for Estimation

and Prediction 708

15.7 Categorical Independent Variables 710

An Example: Johnson Filtration, Inc. 710

Interpreting the Parameters 712

More Complex Categorical Variables 713

15.8 Residual Analysis 718

Residual Plot Against y⁄ 718

Standardized Residual Plot Against y⁄719

15.9 Practical Advice: Big Data and Hypothesis

Testing in Multiple Regression 722

Summary 723

Glossary 723

Key Formulas 724

Supplementary Exercises 725

Case Problem 1: Consumer Research, Inc. 729

Case Problem 2: Predicting Winnings for NASCAR Drivers 730

Case Problem 3: Finding the Best Car Value 732

Appendix 15.1 Multiple Linear Regression with R (MindTap Reader)

Appendix A  References and Bibliography 734

Appendix B Tables 736

Appendix C Summation Notation 747

Appendix E  _Microsoft Excel and Tools for Statistical Analysis 749

Appendix F Microsoft Excel Online and Tools for Statistical Analysis 757

Index 765

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