Basic Business Statistics Concepts and Applications, 14th Edition PDF by Mark L. Berenson, David M. Levine, Kathryn A. Szabat and David F. Stephan

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Basic Business Statistics Concepts and Applications, Fourteenth Edition

By Mark L. Berenson, David M. Levine, Kathryn A. Szabat and David F. Stephan

Basic Business Statistics Concepts and Applications, 14th Edition

Contents

Preface xxiv

First Things First 1

USING STATISTICS: “The Price of Admission” 1

FTF.1 Think Differently About Statistics 2

Statistics: A Way of Thinking 2

Statistics: An Important Part of Your Business Education 3

FTF.2 Business Analytics: The Changing Face of Statistics 4

“Big Data” 4

FTF.3 Starting Point for Learning Statistics 5

Statistic 5

Can Statistics (pl., statistic) Lie? 6

FTF.4 Starting Point for Using Software 6

Using Software Properly 8

REFERENCES 9

KEY TERMS 9

EXCEL GUIDE 10

EG.1 Getting Started with Excel 10

EG.2 Entering Data 10

EG.3 Open or Save a Workbook 10

EG.4 Working with a Workbook 11

EG.5 Print a Worksheet 11

EG.6 Reviewing Worksheets 11

EG.7 If You Use the Workbook Instructions 11

JMP GUIDE 12

JG.1 Getting Started with JMP 12

JG.2 Entering Data 13

JG.3 Create New Project or Data Table 13

JG.4 Open or Save Files 13

JG.5 Print Data Tables or Report Windows 13

JG.6 JMP Script Files 13

MINITAB GUIDE 14

MG.1 Getting Started with Minitab 14

MG.2 Entering Data 14

MG.3 Open or Save Files 14

MG.4 Insert or Copy Worksheets 15

MG.5 Print Worksheets 15

1 Defining and Collecting Data 16

USING STATISTICS: Defining Moments 16

1.1 Defining Variables 17

Classifying Variables by Type 17

Measurement Scales 18

1.2 Collecting Data 19

Populations and Samples 20

Data Sources 20

1.3 Types of Sampling Methods 21

Simple Random Sample 22

Systematic Sample 22

Stratified Sample 23

Cluster Sample 23

1.4 Data Cleaning 24

Invalid Variable Values 25

Coding Errors 25

Data Integration Errors 25

Missing Values 26

Algorithmic Cleaning of Extreme Numerical Values 26

1.5 Other Data Preprocessing Tasks 26

Data Formatting 26

Stacking and Unstacking Data 27

Recoding Variables 27

1.6 Types of Survey Errors 28

Coverage Error 28

Nonresponse Error 28

Sampling Error 28

Measurement Error 29

Ethical Issues About Surveys 29

CONSIDER THIS: New Media Surveys/Old Survey Errors 29

USING STATISTICS: Defining Moments, Revisited 31

SUMMARY 31

REFERENCES 31

KEY TERMS 31

CHECKING YOUR UNDERSTANDING 32

CHAPTER REVIEW PROBLEMS 32

CASES FOR CHAPTER 1 33

Managing Ashland MultiComm Services 33

CardioGood Fitness 33

Clear Mountain State Student Survey 34

Learning with the Digital Cases 34

CHAPTER 1 EXCEL GUIDE 35

EG1.1 Defining Variables 35

EG1.2 Collecting Data 35

EG1.3 Types of Sampling Methods 35

EG1.4 Data Cleaning 36

EG1.5 Other Data Preprocessing 36

CHAPTER 1 JMP GUIDE 37

JG1.1 Defining Variables 37

JG1.2 Collecting Data 37

JG1.3 Types of Sampling Methods 37

JG1.4 Data Cleaning 38

JG1.5 Other Preprocessing Tasks 39

CHAPTER 1 MINITAB GUIDE 39

MG1.1 Defining Variables 39

MG1.2 Collecting Data 39

MG1.3 Types of Sampling Methods 39

MG1.4 Data Cleaning 40

MG1.5 Other Preprocessing Tasks 40

2 Organizing and Visualizing Variables 41

USING STATISTICS: “The Choice Is Yours” 41

2.1 Organizing Categorical Variables 42

The Summary Table 42

The Contingency Table 43

2.2 Organizing Numerical Variables 46

The Frequency Distribution 47

Classes and Excel Bins 49

The Relative Frequency Distribution and the Percentage Distribution 49

The Cumulative Distribution 51

2.3 Visualizing Categorical Variables 54

The Bar Chart 54

The Pie Chart and the Doughnut Chart 55

The Pareto Chart 56

Visualizing Two Categorical Variables 58

2.4 Visualizing Numerical Variables 61

The Stem-and-Leaf Display 61

The Histogram 61

The Percentage Polygon 63

The Cumulative Percentage Polygon (Ogive) 64

2.5 Visualizing Two Numerical Variables 67

The Scatter Plot 67

The Time-Series Plot 68

2.6 Organizing a Mix of Variables 70

Drill-down 71

2.7 Visualizing a Mix of Variables 72

Colored Scatter Plot 72

Bubble Charts 73

PivotChart (Excel) 73

Treemap (Excel, JMP) 73

Sparklines (Excel) 74

2.8 Filtering and Querying Data 75

Excel Slicers 75

2.9 Pitfalls in Organizing and Visualizing Variables 77

Obscuring Data 77

Creating False Impressions 78

Chartjunk 79

EXHIBIT: Best Practices for Creating Visual Summaries 80

USING STATISTICS: “The Choice Is Yours,” Revisited 81

SUMMARY 81

REFERENCES 82

KEY EQUATIONS 82

KEY TERMS 83

CHECKING YOUR UNDERSTANDING 83

CHAPTER REVIEW PROBLEMS 83

CASES FOR CHAPTER 2 88

Managing Ashland MultiComm Services 88

Digital Case 88

CardioGood Fitness 89

The Choice Is Yours Follow-Up 89

Clear Mountain State Student Survey 89

CHAPTER 2 EXCEL GUIDE 90

EG2.1 Organizing Categorical Variables 90

EG2.2 Organizing Numerical Variables 92

Charts Group Reference 94

EG2.3 Visualizing Categorical Variables 94

EG2.4 Visualizing Numerical Variables 96

EG2.5 Visualizing Two Numerical Variables 99

EG2.6 Organizing a Mix of Variables 100

EG2.7 Visualizing a Mix of Variables 101

EG2.8 Filtering and Querying Data 102

CHAPTER 2 JMP GUIDE 102

JG2 JMP Choices for Creating Summaries 102

JG2.1 Organizing Categorical Variables 103

JG2.2 Organizing Numerical Variables 104

JG2.3 Visualizing Categorical Variables 106

JG2.4 Visualizing Numerical Variables 107

JG2.5 Visualizing Two Numerical Variables 109

JG2.6 Organizing a Mix of Variables 110

JG2.7 Visualizing a Mix of Variables 110

JG2.8 Filtering and Querying Data 111

JMP Guide Gallery 112

CHAPTER 2 MINITAB GUIDE 113

MG2.1 Organizing Categorical Variables 113

MG2.2 Organizing Numerical Variables 113

MG2.3 Visualizing Categorical Variables 113

MG2.4 Visualizing Numerical Variables 115

MG2.5 Visualizing Two Numerical Variables 117

MG2.6 Organizing a Mix of Variables 118

MG2.7 Visualizing a Mix of Variables 118

MG2.8 Filtering and Querying Data 119

3 Numerical Descriptive Measures 120

USING STATISTICS: More Descriptive Choices 120

3.1 Measures of Central Tendency 121

The Mean 121

The Median 123

The Mode 124

The Geometric Mean 125

3.2 Measures of Variation and Shape 126

The Range 126

The Variance and the Standard Deviation 127

The Coefficient of Variation 130

Z Scores 130

Shape: Skewness 132

Shape: Kurtosis 132

3.3 Exploring Numerical Variables 137

Quartiles 137

EXHIBIT: Rules for Calculating the Quartiles from a Set

of Ranked Values 137

The Interquartile Range 139

The Five-Number Summary 139

The Boxplot 141

3.4 Numerical Descriptive Measures for a Population 143

The Population Mean 144

The Population Variance and Standard Deviation 144

The Empirical Rule 145

Chebyshev’s Theorem 146

3.5 The Covariance and the Coefficient of Correlation 148

The Covariance 148

The Coefficient of Correlation 148

3.6 Descriptive Statistics: Pitfalls and Ethical Issues 152

USING STATISTICS: More Descriptive Choices, Revisited 153

SUMMARY 153

REFERENCES 154

KEY EQUATIONS 154

KEY TERMS 154

CHECKING YOUR UNDERSTANDING 155

CHAPTER REVIEW PROBLEMS 155

CASES FOR CHAPTER 3 158

Managing Ashland MultiComm Services 158

Digital Case 158

CardioGood Fitness 158

More Descriptive Choices Follow-up 159

Clear Mountain State Student Survey 159

CHAPTER 3 EXCEL GUIDE 160

EG3.1 Measures of Central Tendency 160

EG3.2 Measures of Variation and Shape 161

EG3.3 Exploring Numerical Variables 161

EG3.4 Numerical Descriptive Measures for a Population 162

EG3.5 The Covariance and the Coefficient of Correlation 162

CHAPTER 3 JMP GUIDE 163

JG3.1 Measures of Central Tendency 163

JG3.2 Measures of Variation and Shape 163

JG3.3 Exploring Numerical Variables 164

JG3.4 Numerical Descriptive Measures for a Population 164

JG3.5 The Covariance and the Coefficient of Correlation 164

CHAPTER 3 MINITAB GUIDE 165

MG3.1 Measures of Central Tendency 165

MG3.2 Measures of Variation and Shape 166

MG3.3 Exploring Numerical Variables 166

MG3.4 Numerical Descriptive Measures for a Population 167

MG3.5 The Covariance and the Coefficient of Correlation 167

4 Basic Probability 168

USING STATISTICS: Possibilities at M&R Electronics World 168

4.1 Basic Probability Concepts 169

Events and Sample Spaces 169

Types of Probability 170

Summarizing Sample Spaces 171

Simple Probability 172

Joint Probability 173

Marginal Probability 174

General Addition Rule 174

4.2 Conditional Probability 178

Computing Conditional Probabilities 178

Decision Trees 179

Independence 181

Multiplication Rules 182

Marginal Probability Using the General Multiplication Rule 183

4.3 Ethical Issues and Probability 185

4.4 Bayes’ Theorem 186

CONSIDER THIS: Divine Providence and Spam 188

4.5 Counting Rules 189

USING STATISTICS: Possibilities at M&R Electronics World, Revisited 192

SUMMARY 193

REFERENCES 193

KEY EQUATIONS 193

KEY TERMS 194

CHECKING YOUR UNDERSTANDING 194

CHAPTER REVIEW PROBLEMS 194

CASES FOR CHAPTER 4 196

Digital Case 196

CardioGood Fitness 196

The Choice Is Yours Follow-Up 196

Clear Mountain State Student Survey 196

CHAPTER 4 EXCEL GUIDE 197

EG4.1 Basic Probability Concepts 197

EG4.4 Bayes’ Theorem 197

EG4.5 Counting Rules 197

CHAPTER 4 JMP

JG4.4 Bayes’ Theorem 198

CHAPTER 4 MINITAB GUIDE 198

MG4.5 Counting Rules 198

5 Discrete Probability

Distributions 199

USING STATISTICS: Events of Interest at Ricknel Home Centers 199

5.1 The Probability Distribution for a Discrete Variable 200

Expected Value of a Discrete Variable 200

Variance and Standard Deviation of a Discrete Variable 201

5.2 Binomial Distribution 204

EXHIBIT: Properties of the Binomial Distribution 204

Histograms for Discrete Variables 207

Summary Measures for the Binomial Distribution 208

5.3 Poisson Distribution 211

5.4 Covariance of a Probability Distribution and Its Application in Finance 214

5.5 Hypergeometric Distribution (online) 214

5.6 Using the Poisson Distribution to Approximate the Binomial Distribution (online) 214

USING STATISTICS: Events of Interest …. , Revisited 215

SUMMARY 215

REFERENCES 215

KEY EQUATIONS 215

KEY TERMS 216

CHECKING YOUR UNDERSTANDING 216

CHAPTER REVIEW PROBLEMS 216

CASES FOR CHAPTER 5 218

Managing Ashland MultiComm Services 218

Digital Case 218

CHAPTER 5 EXCEL GUIDE 219

EG5.1 The Probability Distribution for a Discrete Variable 219

EG5.2 Binomial Distribution 219

EG5.3 Poisson Distribution 219

CHAPTER 5 JMP GUIDE 220

JG5.1 The Probability Distribution for a Discrete Variable 220

JG5.2 Binomial Distribution 220

JG5.3 Poisson Distribution 221

CHAPTER 5 MINITAB GUIDE 221

MG5.1 The Probability Distribution for a Discrete Variable 221

MG5.2 Binomial Distribution 222

MG5.3 Poisson Distribution 222

6 The Normal Distribution

and Other Continuous

Distributions 223

USING STATISTICS: Normal Load Times at MyTVLab 223

6.1 Continuous Probability Distributions 224

6.2 The Normal Distribution 224

EXHIBIT: Normal Distribution Important Theoretical Properties 225

Role of the Mean and the Standard Deviation 226

Calculating Normal Probabilities 227

VISUAL EXPLORATIONS: Exploring the Normal Distribution 231

Finding X Values 232

CONSIDER THIS: What Is Normal? 235

6.3 Evaluating Normality 237

Comparing Data Characteristics to Theoretical Properties 237

Constructing the Normal Probability Plot 238

6.4 The Uniform Distribution 241

6.5 The Exponential Distribution (online) 243

6.6 The Normal Approximation to the Binomial Distribution (online) 243

USING STATISTICS: Normal Load Times … , Revisited 243

SUMMARY 243

REFERENCES 244

KEY EQUATIONS 244

KEY TERMS 244

CHECKING YOUR UNDERSTANDING 245

CHAPTER REVIEW PROBLEMS 245

CASES FOR CHAPTER 6 246

Managing Ashland MultiComm Services 246

CardioGood Fitness 247

More Descriptive Choices Follow-up 247

Clear Mountain State Student Survey 247

Digital Case 247

CHAPTER 6 EXCEL GUIDE 248

EG6.2 The Normal Distribution 248

EG6.3 Evaluating Normality 248

CHAPTER 6 JMP GUIDE 249

JG6.2 The Normal Distribution 249

JG6.3 Evaluating Normality 249

CHAPTER 6 MINITAB GUIDE 250

MG6.2 The Normal Distribution 250

MG6.3 Evaluating Normality 250

7 Sampling Distributions 252

USING STATISTICS: Sampling Oxford Cereals 252

7.1 Sampling Distributions 253

7.2 Sampling Distribution of the Mean 253

The Unbiased Property of the Sample Mean 253

Standard Error of the Mean 255

Sampling from Normally Distributed Populations 256

Sampling from Non-normally Distributed Populations—The Central Limit Theorem 259

EXHIBIT: Normality and the Sampling Distribution of the Mean 260

VISUAL EXPLORATIONS: Exploring Sampling Distributions 263

7.3 Sampling Distribution of the Proportion 264

7.4 Sampling from Finite Populations (online) 267

USING STATISTICS: Sampling Oxford Cereals, Revisited 267

SUMMARY 268

REFERENCES 268

KEY EQUATIONS 268

KEY TERMS 268

CHECKING YOUR UNDERSTANDING 269

CHAPTER REVIEW PROBLEMS 269

CASES FOR CHAPTER 7 270

Managing Ashland MultiComm Services 270

Digital Case 271

CHAPTER 7 EXCEL GUIDE 272

EG7.2 Sampling Distribution of the Mean 272

CHAPTER 7 JMP GUIDE 273

JG7.2 Sampling Distribution of the Mean 273

CHAPTER 7 MINITAB GUIDE 274

MG7.2 Sampling Distribution of the Mean 274

8 Confidence Interval Estimation 275

USING STATISTICS: Getting Estimates at Ricknel Home Centers 275

8.1 Confidence Interval Estimate for the Mean (s Known) 276

Sampling Error 277

Can You Ever Know the Population Standard Deviation? 280

8.2 Confidence Interval Estimate for the Mean (s Unknown) 281

Student’s t Distribution 282

The Concept of Degrees of Freedom 282

Properties of the t Distribution 282

The Confidence Interval Statement 284

8.3 Confidence Interval Estimate for the Proportion 289

8.4 Determining Sample Size 292

Sample Size Determination for the Mean 292

Sample Size Determination for the Proportion 294

8.5 Confidence Interval Estimation and Ethical Issues 297

8.6 Application of Confidence Interval Estimation in Auditing (online) 297

8.7 Estimation and Sample Size Estimation for Finite Populations (online) 298

8.8 Bootstrapping (online) 298

USING STATISTICS: Getting Estimates …. , Revisited 298

SUMMARY 298

REFERENCES 299

KEY EQUATIONS 299

KEY TERMS 299

CHECKING YOUR UNDERSTANDING 299

CHAPTER REVIEW PROBLEMS 300

CASES FOR CHAPTER 8 302

Managing Ashland MultiComm Services 302

Digital Case 303

Sure Value Convenience Stores 304

CardioGood Fitness 304

More Descriptive Choices Follow-Up 304

Clear Mountain State Student Survey 304

CHAPTER 8 EXCEL GUIDE 305

EG8.1 Confidence Interval Estimate for the Mean (s Known) 305

EG8.2 Confidence Interval Estimate for the Mean (s Unknown) 305

EG8.3 Confidence Interval Estimate for the Proportion 306

EG8.4 Determining Sample Size 306

CHAPTER 8 JMP GUIDE 307

JG8.1 Confidence Interval Estimate for the Mean (s Known) 307

JG8.2 Confidence Interval Estimate for the Mean (s Unknown) 307

JG8.3 Confidence Interval Estimate for the Proportion 308

JG8.4 Determining Sample Size 308

CHAPTER 8 MINITAB GUIDE 309

MG8.1 Confidence Interval Estimate for the Mean (s Known) 309

MG8.2 Confidence Interval Estimate for the Mean (s Unknown) 309

MG8.3 Confidence Interval Estimate for the Proportion 310

MG8.4 Determining Sample Size 310

9 Fundamentals of Hypothesis

Testing: One-Sample Tests 311

USING STATISTICS: Significant Testing at Oxford Cereals 311

9.1 Fundamentals of Hypothesis Testing 312

EXHIBIT: Fundamental Hypothesis Testing Concepts 313

The Critical Value of the Test Statistic 313

Regions of Rejection and Nonrejection 314

Risks in Decision Making Using Hypothesis Testing 314

Z Test for the Mean (s Known) 316

Hypothesis Testing Using the Critical Value Approach 317

EXHIBIT: The Critical Value Approach to Hypothesis Testing 318

Hypothesis Testing Using the p-Value Approach 320

EXHIBIT: The p-Value Approach to Hypothesis Testing 321

A Connection Between Confidence Interval Estimation and Hypothesis Testing 322

Can You Ever Know the Population Standard Deviation? 323

9.2 t Test of Hypothesis for the Mean (s Unknown) 324

The Critical Value Approach 325

p-Value Approach 326

Checking the Normality Assumption 327

9.3 One-Tail Tests 330

The Critical Value Approach 330

The p-Value Approach 331

EXHIBIT: The Null and Alternative Hypotheses in One-Tail Tests 333

9.4 Z Test of Hypothesis for the Proportion 334

The Critical Value Approach 335

The p-Value Approach 336

9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues 338

EXHIBIT: Questions for the Planning Stage of Hypothesis

Testing 338

Statistical Significance Versus Practical Significance 338

Statistical Insignificance Versus Importance 339

Reporting of Findings 339

Ethical Issues 339

9.6 Power of the Test (online) 339

USING STATISTICS: Significant Testing …. Revisited 340

SUMMARY 340

REFERENCES 340

KEY EQUATIONS 341

KEY TERMS 341

CHECKING YOUR UNDERSTANDING 341

CHAPTER REVIEW PROBLEMS 341

CASES FOR CHAPTER 9 343

Managing Ashland MultiComm Services 343

Digital Case 343

Sure Value Convenience Stores 344

CHPATER 9 EXCEL GUIDE 345

EG9.1 Fundamentals of Hypothesis Testing 345

EG9.2 t Test Of Hypothesis for The Mean (s Unknown) 345

EG9.3 One-Tail Tests 346

EG9.4 Z Test Of Hypothesis For The Proportion 346

CHAPTER 9 JMP GUIDE 347

JG9.1 Fundamentals of Hypothesis Testing 347

JG9.2 t Test of Hypothesis for the Mean (s Unknown) 347

JG9.3 One-Tail Tests 348

JG9.4 Z Test Of Hypothesis For The Proportion 348

CHAPTER 9 MINITAB GUIDE 348

MG9.1 Fundamentals of Hypothesis Testing 348

MG9.2 t Test Of Hypothesis for The Mean (s Unknown) 349

MG9.3 One-Tail Tests 349

MG9.4 Z Test Of Hypothesis for The Proportion 349

10 Two-Sample Tests 351

USING STATISTICS: Differing Means for Selling

Streaming Media Players at Arlingtons? 351

10.1 Comparing the Means of Two Independent Populations 352

Pooled-Variance t Test for the Difference Between

Two Means Assuming Equal Variances 352

Evaluating the Normality Assumption 355

Confidence Interval Estimate for the Difference Between Two Means 357

Separate-Variance t Test for the Difference Between

Two Means, Assuming Unequal Variances 358

CONSIDER THIS: Do People Really Do This? 359

10.2 Comparing the Means of Two Related Populations 361

Paired t Test 362

Confidence Interval Estimate for the Mean Difference 367

10.3 Comparing the Proportions of Two Independent

Populations 369

Z Test for the Difference Between Two Proportions 369

Confidence Interval Estimate for the Difference Between

Two Proportions 374

10.4 F Test for the Ratio of Two Variances 376

10.5 Effect Size (online) 380

USING STATISTICS: Differing Means for Selling … ,

Revisited 381

SUMMARY 381

REFERENCES 382

KEY EQUATIONS 382

KEY TERMS 383

CHECKING YOUR UNDERSTANDING 383

CHAPTER REVIEW PROBLEMS 383

CASES FOR CHAPTER 10 385

Managing Ashland MultiComm Services 385

Digital Case 386

Sure Value Convenience Stores 386

CardioGood Fitness 386

More Descriptive Choices Follow-Up 386

Clear Mountain State Student Survey 387

CHAPTER 10 EXCEL GUIDE 388

EG10.1 Comparing The Means of Two Independent Populations 388

EG10.2 Comparing The Means of Two Related Populations 390

EG10.3 Comparing The Proportions of Two Independent

Populations 391

EG10.4 F Test For The Ratio of Two Variances 392

CHAPTER 10 JMP GUIDE 393

JG10.1 Comparing The Means of Two Independent Populations 393

JG10.2 Comparing The Means of Two Related Populations 394

JG10.3 Comparing The Proportions of Two Independent

Populations 394

JG10.4 F Test For The Ratio of Two Variances 394

CHAPTER 10 MINITAB GUIDE 395

MG10.1 Comparing The Means of Two Independent Populations 395

MG10.2 Comparing The Means of Two Related Populations 396

MG10.3 Comparing The Proportions of Two Independent

Populations 396

MG10.4 F Test For The Ratio of Two Variances 397

11 Analysis of Variance 398

USING STATISTICS: The Means to Find Differences at Arlingtons 398

11.1 The Completely Randomized Design: One-Way ANOVA 399

Analyzing Variation in One-Way ANOVA 400

F Test for Differences Among More Than Two Means 402

One-Way ANOVA F Test Assumptions 407

Levene Test for Homogeneity of Variance 407

Multiple Comparisons: The Tukey-Kramer Procedure 409

The Analysis of Means (ANOM) 411

11.2 The Factorial Design: Two-Way ANOVA 414

Factor and Interaction Effects 415

Testing for Factor and Interaction Effects 416

Multiple Comparisons: The Tukey Procedure 420

Visualizing Interaction Effects: The Cell Means Plot 421

Interpreting Interaction Effects 422

11.3 The Randomized Block Design (online) 426

11.4 Fixed Effects, Random Effects, and Mixed Effects Models (online) 426

USING STATISTICS: The Means to Find Differences

at Arlingtons Revisited 426

SUMMARY 426

REFERENCES 427

KEY EQUATIONS 427

KEY TERMS 428

CHECKING YOUR UNDERSTANDING 428

CHAPTER REVIEW PROBLEMS 428

CASES FOR CHAPTER 11 430

Managing Ashland MultiComm Services 430

Digital Case 431

Sure Value Convenience Stores 431

CardioGood Fitness 431

More Descriptive Choices Follow-Up 431

Clear Mountain State Student Survey 431

CHAPTER 11 EXCEL GUIDE 432

EG11.1 The Completely Randomized Design: One-Way ANOVA 432

EG11.2 The Factorial Design: Two-Way Anova 434

CHAPTER 11 JMP GUIDE 435

JG11.1 The Completely Randomized Design: One-Way ANOVA 435

JG11.2 The Factorial Design: Two-Way Anova 436

CHAPTER 11 MINITAB GUIDE 437

MG11.1 The Completely Randomized Design: One-Way ANOVA 437

MG11.2 The Factorial Design: Two-Way Anova 438

12 Chi-Square and

Nonparametric Tests 440

USING STATISTICS: Avoiding Guesswork About

Resort Guests 440

12.1 Chi-Square Test for the Difference Between Two

Proportions 441

12.2 Chi-Square Test for Differences Among More Than

Two Proportions 448

The Marascuilo Procedure 451

The Analysis of Proportions (ANOP) 453

12.3 Chi-Square Test of Independence 454

12.4 Wilcoxon Rank Sum Test for Two Independent

Populations 460

12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA 466

Assumptions of the Kruskal-Wallis Rank Test 469

12.6 McNemar Test for the Difference Between Two

Proportions (Related Samples) (online) 470

12.7 Chi-Square Test for the Variance or Standard

Deviation (online) 470

12.8 Wilcoxon Signed Ranks Test for Two Related

Populations (online) 471

12.9 Friedman Rank Test for the Randomized Block

Design (online) 471

USING STATISTICS: Avoiding Guesswork … ,

Revisited 471

SUMMARY 471

REFERENCES 472

KEY EQUATIONS 472

KEY TERMS 473

CHECKING YOUR UNDERSTANDING 473

CHAPTER REVIEW PROBLEMS 473

CASES FOR CHAPTER 12 475

Managing Ashland MultiComm Services 475

Digital Case 476

Sure Value Convenience Stores 476

CardioGood Fitness 476

More Descriptive Choices Follow-Up 477

Clear Mountain State Student Survey 477

CHAPTER 12 EXCEL GUIDE 478

EG12.1 Chi-Square Test for the Difference Between

Two Proportions 478

EG12.2 Chi-Square Test for Differences Among More Than

Two Proportions 478

EG12.3 Chi-Square Test of Independence 479

EG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for

Two Independent Populations 479

EG12.5 Kruskal-Wallis Rank Test: A Nonparametric Method for

the One-Way ANOVA 480

CHAPTER 12 JMP GUIDE 481

JG12.1 Chi-Square Test for the Difference Between

Two Proportions 481

JG12.2 Chi-Square Test tor Difference Among More Than

Two Proportions 481

JG12.3 Chi-Square Test Of Independence 481

JG12.4 Wilcoxon Rank Sum Test For Two Independent

Populations 481

JG12.5 Kruskal-Wallis Rank Test For The One-Way Anova 482

CHAPTER 12 MINITAB GUIDE 482

MG12.1 Chi-Square Test for The Difference Between

Two Proportions 482

MG12.2 Chi-Square Test for Differences Among More Than

Two Proportions 483

MG12.3 Chi-Square Test of Independence 483

MG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method

for Two Independent Populations 483

MG12.5 Kruskal-Wallis Rank Test: A Nonparametric Method

for The One-Way ANOVA 483

13 Simple Linear Regression 484

USING STATISTICS: Knowing Customers at Sunflowers Apparel 484

Preliminary Analysis 485

13.1 Simple Linear Regression Models 486

13.2 Determining the Simple Linear Regression

Equation 487

The Least-Squares Method 487

Predictions in Regression Analysis: Interpolation Versus Extrapolation 490

Computing the Y Intercept, b0 and the Slope, b1 491

VISUAL EXPLORATIONS: Exploring Simple Linear

Regression Coefficients 493

13.3 Measures of Variation 495

Computing the Sum of Squares 495

The Coefficient of Determination 496

Standard Error of the Estimate 498

13.4 Assumptions of Regression 500

13.5 Residual Analysis 500

Evaluating the Assumptions 500

13.6 Measuring Autocorrelation: The Durbin-Watson Statistic 504

Residual Plots to Detect Autocorrelation 504

The Durbin-Watson Statistic 505

13.7 Inferences About the Slope and Correlation Coefficient 508

t Test for the Slope 508

F Test for the Slope 509

Confidence Interval Estimate for the Slope 511

13.8 Estimation of Mean Values and Prediction of

Individual Values 514

The Confidence Interval Estimate for the Mean Response 515

The Prediction Interval for an Individual Response 516

13.9 Potential Pitfalls in Regression 518

EXHIBIT: Seven Steps for Avoiding the Potential Pitfalls 518

USING STATISTICS: Knowing Customers … , Revisited 520

SUMMARY 521

REFERENCES 522

KEY EQUATIONS 522

KEY TERMS 523

CHECKING YOUR UNDERSTANDING 523

CHAPTER REVIEW PROBLEMS 524

CASES FOR CHAPTER 13 527

Managing Ashland MultiComm Services 527

Digital Case 527

Brynne Packaging 528

CHAPTER 13 EXCEL GUIDE 529

EG13.2 Determining the Simple Linear Regression Equation 529

EG13.3 Measures of Variation 530

EG13.4 Assumptions of Regression 530

EG13.5 Residual Analysis 530

EG13.6 Measuring Autocorrelation: the Durbin-Watson Statistic 531

EG13.7 Inferences about the Slope and Correlation Coefficient 531

EG13.8 Estimation of Mean Values and Prediction of Individual Values 531

CHAPTER 13 JMP GUIDE 532

JG13.2 Determining The Simple Linear Regression Equation 532

JG13.3 Measures Of Variation 532

JG13.4 Assumptions Of Regression 532

JG13.5 Residual Analysis 532

JG13.6 Measuring Autocorrelation: The Durbin-Watson Statistic 532

JG13.7 Inferences About The Slope And Correlation Coefficient 532

JG13.8 Estimation Of Mean Values And Prediction Of Individual Values 533

CHAPTER 13 MINITAB GUIDE 534

MG13.2 Determining The Simple Linear Regression Equation 534

MG13.3 Measures Of Variation 535

MG13.4 Assumptions Of Regression 535

MG13.5 Residual Analysis 535

MG13.6 Measuring Autocorrelation: The Durbin-Watson Statistic 535

MG13.7 Inferences about The Slope and Correlation Coefficient 535

MG13.8 Estimation of Mean Values and Prediction of Individual Values 535

14 Introduction to Multiple Regression 536

USING STATISTICS: The Multiple Effects of OmniPower Bars 536

14.1 Developing a Multiple Regression Model 537

Interpreting the Regression Coefficients 538

Predicting the Dependent Variable Y 540

14.2 r2, Adjusted r2, and the Overall F Test 542

Coefficient of Multiple Determination 542

Adjusted r2 543

Test for the Significance of the Overall Multiple Regression

Model 543

14.3 Multiple Regression Residual Analysis 546

14.4 Inferences About the Population Regression

Coefficients 547

Tests of Hypothesis 548

Confidence Interval Estimation 549

14.5 Testing Portions of the Multiple Regression Model 551

Coefficients of Partial Determination 555

14.6 Using Dummy Variables and Interaction Terms 557

Interactions 560

14.7 Logistic Regression 569

14.8 Influence Analysis (online) 575

USING STATISTICS: The Multiple Effects … , Revisited 575

SUMMARY 575

REFERENCES 577

KEY EQUATIONS 577

KEY TERMS 578

CHECKING YOUR UNDERSTANDING 578

CHAPTER REVIEW PROBLEMS 578

CASES FOR CHAPTER 14 581

Managing Ashland MultiComm Services 581

Digital Case 581

CHAPTER 14 EXCEL GUIDE 582

EG14.1 Developing a Multiple Regression Model 582

EG14.2 r2, Adjusted r2, and the Overall F Test 583

EG14.3 Multiple Regression Residual Analysis 583

EG14.4 Inferences about the Population Regression Coefficients 584

EG14.5 Testing Portions of the Multiple Regression Model 584

EG14.6 Using Dummy Variables and Interaction Terms 584

EG14.7 Logistic Regression 585

CHAPTER 14 JMP GUIDE 585

JG14.1 Developing a Multiple Regression Model 585

JG14.2 r2, Adjusted r2, and the Overall F Test Measures of Variation 586

JG14.3 Multiple Regression Residual Analysis 586

JG14.4 Inferences About the Population 586

JG14.5 Testing Portions of the Multiple Regression Model 587

JG14.6 Using Dummy Variables and Interaction Terms 587

JG14.7 Logistic Regression 587

CHAPTER 14 MINITAB GUIDE 588

MG14.1 Developing a Multiple Regression Model 588

MG14.2 r2, Adjusted r2, and the Overall F Test 589

MG14.3 Multiple Regression Residual Analysis 589

MG14.4 Inferences About the Population Regression Coefficients 589

MG14.5 Testing Portions of the Multiple Regression Model 589

MG14.6 Using Dummy Variables and Interaction Terms

in Regression Models 589

MG14.7 Logistic Regression 590

MG14.8 Influence Analysis 591

15 Multiple Regression Model Building 592

USING STATISTICS: Valuing Parsimony at WSTA-TV 592

15.1 Quadratic Regression Model 593

Finding the Regression Coefficients and Predicting Y 594

Testing for the Significance of the Quadratic Model 596

Testing the Quadratic Effect 597

The Coefficient of Multiple Determination 599

15.2 Using Transformations in Regression Models 601

The Square-Root Transformation 601

The Log Transformation 603

15.3 Collinearity 605

15.4 Model Building 607

EXHIBIT: Sucessful Model Building 607

The Stepwise Regression Approach to Model Building 609

The Best Subsets Approach to Model Building 610

Model Validation 613

15.5 Pitfalls in Multiple Regression and Ethical Issues 615

Pitfalls in Multiple Regression 615

Ethical Issues 616

USING STATISTICS: Valuing Parsimony … , Revisited 616

SUMMARY 617

REFERENCES 618

KEY EQUATIONS 618

KEY TERMS 618

CHECKING YOUR UNDERSTANDING 618

CHAPTER REVIEW PROBLEMS 618

CASES FOR CHAPTER 15 620

The Mountain States Potato Company 620

Sure Value Convenience Stores 621

Digital Case 621

The Craybill Instrumentation Company Case 621

More Descriptive Choices Follow-Up 622

CHAPATER 15 EXCEL GUIDE 623

EG15.1 The Quadratic Regression Model 623

EG15.2 Using Transformations in Regression Models 623

EG15.3 Collinearity 624

EG15.4 Model Building 624

CHAPATER 15 JMP GUIDE 625

JG15.1 The Quadratic Regression Model 625

JG15.2 Using Transformations in Regression Models 625

JG15.3 Collinearity 625

JG15.4 Model Building 625

CHAPATER 15 MINITAB GUIDE 626

MG15.1 The Quadratic Regression Model 626

MG15.2 Using Transformations in Regression Models 627

MG15.3 Collinearity 627

MG15.4 Model Building 627

16 Time-Series Forecasting 629

USING STATISTICS: Is the ByYourDoor Service

Trending? 629

16.1 Time Series Component Factors 630

16.2 Smoothing an Annual Time Series 632

Moving Averages 633

Exponential Smoothing 635

16.3 Least-Squares Trend Fitting and Forecasting 637

The Linear Trend Model 637

The Quadratic Trend Model 639

The Exponential Trend Model 640

Model Selection Using First, Second, and Percentage

Differences 642

EXHIBIT: Model Selection Using First, Second, and Percentage

Differences 642

16.4 Autoregressive Modeling for Trend Fitting and

Forecasting 647

Selecting an Appropriate Autoregressive Model 648

Determining the Appropriateness of a

Selected Model 649

EXHIBIT: Autoregressive Modeling Steps 651

16.5 Choosing an Appropriate Forecasting Model 655

Residual Analysis 655

The Magnitude of the Residuals Through Squared

or Absolute Differences 656

The Principle of Parsimony 656

A Comparison of Four Forecasting Methods 657

16.6 Time-Series Forecasting of Seasonal Data 659

Least-Squares Forecasting with Monthly or Quarterly Data 659

16.7 Index Numbers (online) 665

CONSIDER THIS: Let the Model User Beware 665

USING STATISTICS: Is the ByYourDoor … , Revisited 665

SUMMARY 665

REFERENCES 666

KEY EQUATIONS 666

KEY TERMS 667

CHECKING YOUR UNDERSTANDING 668

CHAPTER REVIEW PROBLEMS 668

CASES FOR CHAPTER 16 669

Managing Ashland MultiComm Services 669

Digital Case 669

CHAPTER 16 EXCEL GUIDE 670

EG16.2 Smoothing an Annual Time Series 670

EG16.3 Least-Squares Trend Fitting and Forecasting 671

EG16.4 Autoregressive Modeling for Trend Fitting and

Forecasting 671

EG16.5 Choosing An Appropriate Forecasting Model 672

EG16.6 Time-Series Forecasting Of Seasonal Data 672

CHAPTER 16 JMP GUIDE 673

JG16.2 Smoothing an Annual Time Series 673

JG16.3 Least-Squares Trend Fitting and Forecasting 674

JG16.4 Autoregressive Modeling for Trend Fitting and Forecasting 674

JG16.5 Choosing an Appropriate Forecasting Model 675

JG16.6 Time-Series Forecasting of Seasonal Data 675

CHAPTER 16 MINITAB GUIDE 675

MG16.2 Smoothing an Annual Time Series 675

MG16.3 Least-Squares Trend Fitting and Forecasting 676

MG16.4 Autoregressive Modeling for Trend Fitting and

Forecasting 677

MG16.5 Choosing an Appropriate Forecasting Model 677

MG16.6 Time-Series Forecasting of Seasonal Data 677

17 Business Analytics 678

USING STATISTICS: Back to Arlingtons for the Future 678

17.1 Business Analytics Categories 679

Inferential Statistics and Predictive Analytics 680

Supervised and Unsupervised Methods 680

CONSIDER THIS: What’s My Major if I Want to be a

Data Miner? 681

17.2 Descriptive Analytics 682

Dashboards 682

Data Dimensionality and Descriptive Analytics 683

17.3 Predictive Analytics for Prediction 684

17.4 Predictive Analytics for Classification 687

17.5 Predictive Analytics for Clustering 688

17.6 Predictive Analytics for Association 691

Multidimensional scaling (MDS) 692

17.7 Text Analytics 693

17.8 Prescriptive Analytics 694

USING STATISTICS: Back to Arlingtons … , Revisited 695

REFERENCES 695

KEY EQUATIONS 696

KEY TERMS 696

CHECKING YOUR UNDERSTANDING 696

CHAPTER REVIEW PROBLEMS 696

CASES FOR CHAPTER 17 698

The Mountain States Potato Company 698

The Craybill Instrumentation Company 698

CHAPTER 17 SOFTWARE GUIDE 699

Introduction 699

SG17.2 Descriptive Analytics 699

SG17.3 Predictive Analytics for Prediction 701

SG17.4 Predictive Analytics for Classification 701

SG17.5 Predictive Analytics for Clustering 702

SG17.6 Predictive Analytics for Association 702

18 Getting Ready to Analyze

Data in the Future 704

USING STATISTICS: Mounting Future Analyses 704

18.1 Analyzing Numerical Variables 705

EXHIBIT: Questions to Ask When Analyzing Numerical Variables 705

Describe the Characteristics of a Numerical Variable? 705

Reach Conclusions About the Population Mean or the Standard Deviation? 705

Determine Whether the Mean and/or Standard Deviation

Differs Depending on the Group? 706

Determine Which Factors Affect the Value of a Variable? 706

Predict the Value of a Variable Based on the Values of Other Variables? 707

Classify or Associate Items 707

Determine Whether the Values of a Variable Are Stable

Over Time? 707

18.2 Analyzing Categorical Variables 707

EXHIBIT: Questions to Ask When Analyzing Categorical Variables 707

Describe the Proportion of Items of Interest in Each Category? 707

Reach Conclusions About the Proportion of Items of Interest? 708

Determine Whether the Proportion of Items of Interest Differs

Depending on the Group? 708

Predict the Proportion of Items of Interest Based on the

Values of Other Variables? 708

Classify or Associate Items 708

Determine Whether the Proportion of Items of Interest Is

Stable Over Time? 708

USING STATISTICS: The Future to Be Visited 709

CHAPTER REVIEW PROBLEMS 709

19 Statistical Applications in Quality Management (online) 19-1

USING STATISTICS: Finding Quality at the Beachcomber 19-1

19.1 The Theory of Control Charts 19-2

19.2 Control Chart for the Proportion: The p Chart 19-4

19.3 The Red Bead Experiment: Understanding Process Variability 19-10

19.4 Control Chart for an Area of Opportunity: The c Chart 19-11

19.5 Control Charts for the Range and the Mean 19-15

The R Chart 19-15

The X Chart 19-18

19.6 Process Capability 19-21

Customer Satisfaction and Specification Limits 19-21

Capability Indices 19-23

CPL, CPU, and Cpk 19-24

19.7 Total Quality Management 19-26

19.8 Six Sigma 19-27

The DMAIC Model 19-28

Roles in a Six Sigma Organization 19-29

Lean Six Sigma 19-29

USING STATISTICS: Finding Quality at the Beachcomber, Revisited 19-30

SUMMARY 19-30

REFERENCES 19-31

KEY EQUATIONS 19-31

KEY TERMS 19-32

CHAPTER REVIEW PROBLEMS 19-32

CASES FOR CHAPTER 19 19-34

The Harnswell Sewing Machine Company Case 19-34

Managing Ashland Multicomm Services 19-37

CHAPTER 19 EXCEL GUIDE 19-38

EG19.2 Control Chart for the Proportion: The p Chart 19-38

EG19.4 Control Chart for an Area of Opportunity: The c Chart 19-39

EG19.5 Control Charts for the Range and the Mean 19-40

EG19.6 Process Capability 19-41

CHAPTER 19 JMP GUIDE 19-41

JG19.2 Control Chart for the Proportion: The p Chart 19-41

JG19.4 Control Chart for an Area of Opportunity: The c Chart 19-41

JG19.5 Control Charts for the Range and the Mean 19-42

JG19.6 Process Capability 19-42

CHAPTER 19 MINITAB GUIDE 19-42

MG19.2 Control Chart for the Proportion: The p Chart 19-42

MG19.4 Control Chart for an Area of Opportunity:

The c Chart 19-43

MG19.5 Control Charts for the Range and the Mean 19-43

MG19.6 Process Capability 19-43

20 Decision Making (online) 20-1

USING STATISTICS: Reliable Decision Making 20-1

20.1 Payoff Tables and Decision Trees 20-2

20.2 Criteria for Decision Making 20-6

Maximax Payoff 20-6

Maximin Payoff 20-7

Expected Monetary Value 20-7

Expected Opportunity Loss 20-9

Return-to-Risk Ratio 20-11

20.3 Decision Making with Sample Information 20-16

20.4 Utility 20-21

CONSIDER THIS: Risky Business 20-22

USING STATISTICS: Reliable Decision-Making,

Revisited 20-22

SUMMARY 20-23

REFERENCES 20-23

KEY EQUATIONS 20-23

KEY TERMS 20-23

CHAPTER REVIEW PROBLEMS 20-23

CASES FOR CHAPTER 20 20-26

Digital Case 20-26

CHAPTER 20 EXCEL GUIDE 20-27

EG20.1 Payoff Tables and Decision Trees 20-27

EG20.2 Criteria for Decision Making 20-27

Appendices 711

  1. Basic Math Concepts and Symbols 712

A.1 Operators 712

A.2 Rules for Arithmetic Operations 712

A.3 Rules for Algebra: Exponents and Square Roots 712

A.4 Rules for Logarithms 713

A.5 Summation Notation 714

A.6 Greek Alphabet 717

Important Software Skills and Concepts 718

B.1 Identifying the Software Version 718

B.2 Formulas 718

B.3 Excel Cell References 720

B.4 Excel Worksheet Formatting 721

B.5E Excel Chart Formatting 722

B.5J JMP Chart Formatting 723

B.5M Minitab Chart Formatting 724

B.6 Creating Histograms for Discrete Probability

Distributions (Excel) 724

B.7 Deleting the “Extra” Histogram Bar (Excel) 725

Online Resources 726

C.1 About the Online Resources for This Book 726

C.2 Data Files 726

C.3 Files Integrated With Microsoft Excel 733

C.4 Supplemental Files 733

Configuring Software 734

D.1 Microsoft Excel Configuration 734

D.2 JMP Configuration 736

D.3 Minitab Configuration 736

Table 737

E.1 Table of Random Numbers 737

E.2 The Cumulative Standardized Normal

Distribution 739

E.3 Critical Values of t 741

E.4 Critical Values of x2 743

E.5 Critical Values of F 744

E.6 Lower and Upper Critical Values, T1, of the Wilcoxon

Rank Sum Test 748

E.7 Critical Values of the Studentized Range, Q 749

E.8 Critical Values, dL and dU, of the Durbin–Watson

Statistic, D (Critical Values Are One–Sided) 751

E.9 Control Chart Factors 752

E.10 The Standardized Normal Distribution 753

Useful Knowledge 754

F.1 Keyboard Shortcuts 754

F.2 Understanding the Nonstatistical Functions 754

Software FAQs 756

G.1 Microsoft Excel FAQs 756

G.2 PHStat FAQs 756

G.3 JMP FAQs 757

G.4 Minitab FAQs 757

All About PHStat 758

H.1 What is PHStat? 758

H.2 Obtaining and Setting Up PHStat 759

H.3 Using PHStat 759

H.4 PHStat Procedures, by Category 760

Self-Test Solutions and Answers to

Selected Even-Numbered Problems 761

Index 793

Credits 805

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