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Business Statistics: For Contemporary Decision Making, 8th Edition

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Business Statistics: For Contemporary Decision Making, 8th Edition

Ken Black

ISBN: 978-1-118-80048-5 October 2013 880 Pages

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This text is an unbound, binder-ready edition.

Business Statistics: For Contemporary Decision Making, 8th Edition continues the tradition of presenting and explaining the wonders of business statistics through the use of clear, complete, student-friendly pedagogy. Ken Black's text equips readers with the quantitative decision-making skills and analysis techniques they need to make smart decisions based on real-world data.

Preface xiv

About the Author xxiii

UNIT I INTRODUCTION

1 Introduction to Statistics 02

Decision Dilemma: Statistics Describe the State of Business in India’s Countryside 03

1.1 Statistics in Business 04

1.2 Basic Statistical Concepts 05

1.3 Variables and Data 07

1.4 Data Measurement 07

Nominal Level 08

Ordinal Level 09

Interval Level 09

Ratio Level 10

Comparison of the Four Levels of Data 10

Statistical Analysis Using the Computer: Excel and Minitab 11

Summary 13

Key Terms 13

Supplementary Problems 13

Analyzing the Databases 14

Case: DiGiorno Pizza: Introducing a Frozen Pizza to Compete with Carry-Out 16

2 Charts and Graphs 18

Decision Dilemma: Container Shipping Companies 19

2.1 Frequency Distributions 20

Class Midpoint 21

Relative Frequency 21

Cumulative Frequency 21

2.2 Quantitative Data Graphs 23

Histograms 24

Using Histograms to Get an Initial Overview of the Data 24

Frequency Polygons 26

Ogives 26

Dot Plots 27

Stem-and-Leaf Plots 27

2.3 Qualitative Data Graphs 31

Pie Charts 31

Bar Graphs 32

Pareto Charts 34

2.4 Charts and Graphs for Two Variables 38

Cross Tabulation 38

Scatter Plot 39

Summary 42

Key Terms 43

Supplementary Problems 43

Analyzing the Databases 47

Case: Soap Companies Do Battle 48

Using the Computer 49

3 Descriptive Statistics 52

Decision Dilemma: Laundry Statistics 53

3.1 Measures of Central Tendency: Ungrouped Data 53

Mode 54

Median 54

Mean 55

Percentiles 57

Steps in Determining the Location of a Percentile 57

Quartiles 58

3.2 Measures of Variability: Ungrouped Data 61

Range 61

Interquartile Range 62

Mean Absolute Deviation, Variance, and Standard Deviation 63

Mean Absolute Deviation 64

Variance 65

Standard Deviation 66

Meaning of Standard Deviation 66

Empirical Rule 66

Chebyshev’s Theorem 68

Population Versus Sample Variance and Standard Deviation 69

Computational Formulas for Variance and Standard Deviation 70

z Scores 72

Coefficient of Variation 73

3.3 Measures of Central Tendency and Variability: Grouped Data 76

Measures of Central Tendency 76

Mean 77

Median 77

Mode 78

Measures of Variability 78

3.4 Measures of Shape 83

Skewness 83

Skewness and the Relationship of the Mean, Median, and Mode 84

Kurtosis 84

Box-and-Whisker Plots and Five-Number Summary 84

3.5 Descriptive Statistics on the Computer 86

Summary 88

Key Terms 89

Formulas 89

Supplementary Problems 90

Analyzing the Databases 94

Case: Coca-Cola Develops the African Market 95

Using the Computer 96

4 Probability 98

Decision Dilemma: Equity of the Sexes in the Workplace 99

4.1 Introduction to Probability 100

4.2 Methods of Assigning Probabilities 100

Classical Method of Assigning Probabilities 100

Relative Frequency of Occurrence 101

Subjective Probability 102

4.3 Structure of Probability 102

Experiment 102

Event 102

Elementary Events 102

Sample Space 103

Unions and Intersections 103

Mutually Exclusive Events 104

Independent Events 104

Collectively Exhaustive Events 105

Complementary Events 105

Counting the Possibilities 105

The mn Counting Rule 105

Sampling from a Population with Replacement 106

Combinations: Sampling from a Population Without Replacement 106

4.4 Marginal, Union, Joint, and Conditional Probabilities 107

4.5 Addition Laws 109

Joint Probability Tables 110

Complement of a Union 114

Special Law of Addition 114

4.6 Multiplication Laws 117

General Law of Multiplication 117

Special Law of Multiplication 119

4.7 Conditional Probability 122

Independent Events 125

4.8 Revision of Probabilities: Bayes’ Rule 129

Summary 135

Key Terms 135

Formulas 135

Supplementary Problems 136

Analyzing the Databases 140

Case: Colgate-Palmolive Makes a “Total” Effort 140

UNIT II DISTRIBUTIONS AND SAMPLING

5 Discrete Distributions 142

Decision Dilemma: Life with a Cell Phone 143

5.1 Discrete Versus Continuous Distributions 144

5.2 Describing a Discrete Distribution 145

Mean, Variance, and Standard Deviation of Discrete Distributions 146

Mean or Expected Value 146

Variance and Standard Deviation of a Discrete Distribution 146

5.3 Binomial Distribution 149

Solving a Binomial Problem 150

Using the Binomial Table 153

Using the Computer to Produce a Binomial Distribution 154

Mean and Standard Deviation of a Binomial Distribution 155

Graphing Binomial Distributions 156

5.4 Poisson Distribution 161

Working Poisson Problems by Formula 162

What to Do When the Intervals Are Different 162

Using the Poisson Tables 163

Mean and Standard Deviation of a Poisson Distribution 164

Graphing Poisson Distributions 165

Using the Computer to Generate Poisson Distributions 165

Approximating Binomial Problems by the Poisson Distribution 166

5.5 Hypergeometric Distribution 170

Using the Computer to Solve for Hypergeometric Distribution Probabilities 172

Summary 175

Key Terms 175

Formulas 175

Supplementary Problems 176

Analyzing the Databases 180

Case: Whole Foods Market Grows Through Mergers and Acquisitions 181

Using the Computer 182

6 Continuous Distributions 184

Decision Dilemma: The Cost of Human Resources 185

6.1 The Uniform Distribution 186

Solving for the Height and Length of a Uniform Distribution 186

The Mean and Standard Deviation of a Uniform Distribution 187

Determining Probabilities in a Uniform Distribution 188

Using the Computer to Solve for Uniform Distribution Probabilities 191

6.2 Normal Distribution 192

Characteristics of the Normal Distribution 192

History of the Normal Distribution 193

Probability Density Function of the Normal Distribution 193

Standardized Normal Distribution 193

Solving for Probabilities Using the Normal Curve 194

Using Probabilities to Solve for the Mean, the Standard Deviation, or an x Value in a Normal Distribution 198

Using the Computer to Solve for Normal Distribution Probabilities 202

6.3 Using the Normal Curve to Approximate Binomial Distribution Problems 204

Correcting for Continuity 206

6.4 Exponential Distribution 210

Probabilities of the Exponential Distribution 211

Using the Computer to Determine Exponential Distribution Probabilities 213

Summary 215

Key Terms 216

Formulas 216

Supplementary Problems 216

Analyzing the Databases 220

Case: Mercedes Goes After Younger Buyers 220

Using the Computer 221

7 Sampling and Sampling

Distributions 224

Decision Dilemma: What Is the Attitude of Maquiladora Workers? 225

7.1 Sampling 225

Reasons for Sampling 226

Reasons for Taking a Census 226

Frame 227

Random Versus Nonrandom Sampling 228

Random Sampling Techniques 228

Simple Random Sampling 228

Stratified Random Sampling 230

Systematic Sampling 231

Cluster (or Area) Sampling 232

Nonrandom Sampling 234

Convenience Sampling 234

Judgment Sampling 234

Quota Sampling 234

Snowball Sampling 235

Sampling Error 235

Nonsampling Errors 235

7.2 Sampling Distribution of  237

Sampling from a Finite Population 244

7.3 Sampling Distribution of  246

Summary 250

Key Terms 251

Formulas 251

Supplementary Problems 251

Analyzing the Databases 254

Case: Shell Attempts to Return to Premiere Status 254

Using the Computer 255

UNIT III MAKING INFERENCES ABOUT POPULATION PARAMETERS

8 Statistical Inference: Estimation for Single Populations 260

Decision Dilemma: Batteries and Bulbs: How Long Do They Last? 261

8.1 Estimating the Population Mean Using the z Statistic (σ Known) 263

Finite Correction Factor 266

Estimating the Population Mean Using the z Statistic When the Sample Size Is Small 267

Using the Computer to Construct z Confidence Intervals for the Mean 267

8.2 Estimating the Population Mean Using the t Statistic (σ Unknown) 270

The t Distribution 271

Robustness 271

Characteristics of the t Distribution 271

Reading the t Distribution Table 271

Confidence Intervals to Estimate the Population Mean Using the t Statistic 272

Using the Computer to Construct t Confidence Intervals for the Mean 274

8.3 Estimating the Population Proportion 277

Using the Computer to Construct Confidence Intervals of the Population Proportion 280

8.4 Estimating the Population Variance 281

8.5 Estimating Sample Size 285

Sample Size When Estimating μ 285

Determining Sample Size when Estimating p 287

Summary 290

Key Terms 291

Formulas 291

Supplementary Problems 291

Analyzing the Databases 294

Case: The Container Store 295

Using the Computer 296

9 Statistical Inference: Hypothesis

Testing for Single Populations 298

Decision Dilemma: Valero: Refining and Retailing 299

9.1 Introduction to Hypothesis Testing 300

Types of Hypotheses 301

Research Hypotheses 301

Statistical Hypotheses 302

Substantive Hypotheses 304

Eight-Step Process for Testing Hypotheses 305

Rejection and Nonrejection Regions 306

Type I and Type II Errors 307

Comparing Type I and Type II Errors 308

9.2 Testing Hypotheses About a Population Mean Using the z Statistic (σ Known) 310

An Example Using the Eight-Step Approach 310

Using the p-Value to Test Hypotheses 312

Testing the Mean with a Finite Population 313

Using the Critical Value Method to Test Hypotheses 314

Using the Computer to Test Hypotheses About a Population Mean Using the z Statistic 317

9.3 Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown) 319

Using the Computer to Test Hypotheses About a Population Mean Using the t Test 323

9.4 Testing Hypotheses About a Proportion 326

Using the Computer to Test Hypotheses About a Population Proportion 331

9.5 Testing Hypotheses About a Variance 333

9.6 Solving for Type II Errors 336

Some Observations About Type II Errors 341

Operating Characteristic and Power Curves 341

Effect of Increasing Sample Size on the Rejection Limits 343

Summary 347

Key Terms 347

Formulas 347

Supplementary Problems 348

Analyzing the Databases 351

Case: Frito-Lay Targets the Hispanic Market 351

Using the Computer 352

10 Statistical Inferences About Two Populations 354

Decision Dilemma: L. L. Bean 355

10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the z Statistic (Population Variances Known) 358

Hypothesis Testing 359

Confidence Intervals 363

Using the Computer to Test Hypotheses About the Difference in Two Population Means Using the z Test 365

10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown 368

Hypothesis Testing 368

Using the Computer to Test Hypotheses and Construct Confidence Intervals About the Difference in Two Population Means Using the t Test 370

Confidence Intervals 373

10.3 Statistical Inferences for Two Related Populations 378

Hypothesis Testing 378

Using the Computer to Make Statistical Inferences about Two Related Populations 380

Confidence Intervals 383

10.4 Statistical Inferences About Two Population Proportions, p1 _ p2 388

Hypothesis Testing 388

Confidence Intervals 392

Using the Computer to Analyze the Difference in Two Proportions 393

10.5 Testing Hypotheses About Two Population Variances 395

Using the Computer to Test Hypotheses About Two Population Variances 399

Summary 404

Key Terms 404

Formulas 404

Supplementary Problems 405

Analyzing the Databases 410

Case: Seitz Corporation: Producing Quality Gear-Driven and Linear-Motion Products 410

Using the Computer 411

11 Analysis of Variance and Design of Experiments 414

Decision Dilemma: Job and Career Satisfaction of Foreign Self-Initiated Expatriates 415

11.1 Introduction to Design of Experiments 416

11.2 The Completely Randomized Design (One-Way ANOVA) 418

One-Way Analysis of Variance 419

Reading the F Distribution Table 422

Using the Computer for One-Way ANOVA 423

Comparison of F and t Values 425

11.3 Multiple Comparison Tests 430

Tukey’s Honestly Significant Difference (HSD) Test: The Case of Equal Sample Sizes 430

Using the Computer to Do Multiple Comparisons 432

Tukey-Kramer Procedure: The Case of Unequal Sample Sizes 434

11.4 The Randomized Block Design 438

Using the Computer to Analyze Randomized Block Designs 442

11.5 A Factorial Design (Two-Way ANOVA) 448

Advantages of the Factorial Design 448

Factorial Designs with Two Treatments 449

Applications 449

Statistically Testing the Factorial Design 450

Interaction 451

Using a Computer to Do a Two-Way ANOVA 456

Summary 465

Key Terms 465

Formulas 466

Supplementary Problems 467

Analyzing the Databases 470

Case: The Clarkson Company: A Division of Tyco International 471

Using the Computer 472

UNIT IV REGRESSION ANALYSIS AND FORECASTING

12 Simple Regression Analysis and Correlation 476

Decision Dilemma: Predicting International Hourly Wages by the Price of a Big Mac 477

12.1 Correlation 478

12.2 Introduction to Simple Regression Analysis 481

12.3 Determining the Equation of the Regression Line 482

12.4 Residual Analysis 489

Using Residuals to Test the Assumptions of the Regression Model 491

Using the Computer for Residual Analysis 492

12.5 Standard Error of the Estimate 496

12.6 Coefficient of Determination 499

Relationship Between r and r2 501

12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model 501

Testing the Slope 501

Testing the Overall Model 505

12.8 Estimation 506

Confidence Intervals to Estimate the Conditional Mean of y:μy|x 506

Prediction Intervals to Estimate a Single Value of y 507

12.9 Using Regression to Develop a Forecasting Trend Line 510

Determining the Equation of the Trend Line 511

Forecasting Using the Equation of the Trend Line 512

Alternate Coding for Time Periods 513

12.10 Interpreting the Output 516

Summary 520

Key Terms 521

Formulas 521

Supplementary Problems 521

Analyzing the Databases 525

Case: Delta Wire Uses Training as a Weapon 525

Using the Computer 527

13 Multiple Regression Analysis 528

Decision Dilemma: Are You Going to Hate Your New Job? 529

13.1 The Multiple Regression Model 530

Multiple Regression Model with Two Independent Variables (First-Order) 531

Determining the Multiple Regression Equation 532

A Multiple Regression Model 532

13.2 Significance Tests of the Regression Model and Its Coefficients 537

Testing the Overall Model 537

Significance Tests of the Regression Coefficients 539

13.3 Residuals, Standard Error of the Estimate, and R2 542

Residuals 542

SSE and Standard Error of the Estimate 543

Coefficient of Multiple Determination (R2) 544

Adjusted R2 545

13.4 Interpreting Multiple Regression Computer Output 547

A Reexamination of the Multiple Regression Output 547

Summary 551

Key Terms 552

Formulas 552

Supplementary Problems 552

Analyzing the Databases 555

Case: Starbucks Introduces Debit Card 555

Using the Computer 556

14 Building Multiple Regression Models 558

Decision Dilemma: Determining Compensation for CEOs 559

14.1 Nonlinear Models: Mathematical Transformation 560

Polynomial Regression 560

Tukey’s Ladder of Transformations 563

Regression Models with Interaction 564

Model Transformation 566

14.2 Indicator (Dummy) Variables 572

14.3 Model-Building: Search Procedures 578

Search Procedures 580

All Possible Regressions 580

Stepwise Regression 580

Forward Selection 584

Backward Elimination 584

14.4 Multicollinearity 588

14.5 Logistic Regression 590

An Example 590

The Logistic Regression Model 592

Interpreting the Output 593

Determining Logistic Regression Model 594

Testing the Overall Model 594

Testing Individual Predictor Variables 595

Summary 599

Key Terms 600

Formulas 600

Supplementary Problems 601

Analyzing the Databases 604

Case: Virginia Semiconductor 604

Using the Computer 606

15 Time-Series Forecasting and Index Numbers 608

Decision Dilemma: Forecasting Air Pollution 609

15.1 Introduction to Forecasting 610

Time-Series Components 610

The Measurement of Forecasting Error 611

Error 611

Mean Absolute Deviation (MAD) 611

Mean Square Error (MSE) 612

15.2 Smoothing Techniques 614

Naïve Forecasting Models 614

Averaging Models 615

Simple Averages 615

Moving Averages 615

Weighted Moving Averages 617

Exponential Smoothing 619

15.3 Trend Analysis 624

Linear Regression Trend Analysis 624

Regression Trend Analysis Using Quadratic Models 626

Holt’s Two-Parameter Exponential Smoothing Method 629

15.4 Seasonal Effects 631

Decomposition 631

Finding Seasonal Effects with the Computer 634

Winters’ Three-Parameter Exponential Smoothing Method 634

15.5 Autocorrelation and Autoregression 636

Autocorrelation 636

Ways to Overcome the Autocorrelation Problem 639

Addition of Independent Variables 639

Transforming Variables 640

Autoregression 640

15.6 Index Numbers 643

Simple Index Numbers 644

Unweighted Aggregate Price Index Numbers 644

Weighted Aggregate Price Index Numbers 645

Laspeyres Price Index 646

Paasche Price Index 647

Summary 652

Key Terms 653

Formulas 653

Supplementary Problems 653

Analyzing the Databases 658

Case: Debourgh Manufacturing Company 659

Using the Computer 660

UNIT V NONPARAMETRIC STATISTICS AND QUALITY

16 Analysis of Categorical Data 664

Decision Dilemma: Selecting Suppliers in the Electronics Industry 665

16.1 Chi-Square Goodness-of-Fit Test 666

16.2 Contingency Analysis: Chi-Square Test of Independence 674

Summary 683

Key Terms 683

Formulas 683

Supplementary Problems 683

Analyzing the Database 685

Case: Foot Locker in the Shoe Mix 685

Using the Computer 686

17 Nonparametric Statistics 688

Decision Dilemma: How Is the Doughnut Business? 689

17.1 Runs Test 691

Small-Sample Runs Test 692

Large-Sample Runs Test 693

17.2 Mann-Whitney U Test 696

Small-Sample Case 696

Large-Sample Case 698

17.3 Wilcoxon Matched-Pairs Signed Rank Test 704

Small-Sample Case (n ≤ 15) 704

Large-Sample Case (n > 15) 705

17.4 Kruskal-Wallis Test 712

17.5 Friedman Test 717

17.6 Spearman’s Rank Correlation 723

Summary 728

Key Terms 729

Formulas 729

Supplementary Problems 729

Analyzing the Databases 734

Case: Schwinn 735

Using the Computer 736

18 Statistical Quality Control 738

Decision Dilemma: Italy’s Piaggio Makes a Comeback 739

18.1 Introduction to Quality Control 740

What Is Quality Control? 740

Total Quality Management 741

Deming’s 14 Points 742

Quality Gurus 743

Six Sigma 743

Design for Six Sigma 745

Lean Manufacturing 745

Some Important Quality Concepts 745

Benchmarking 746

Just-in-Time Inventory Systems 746

Reengineering 747

Failure Mode and Effects Analysis 748

Poka-Yoke 749

Quality Circles and Six Sigma Teams 749

18.2 Process Analysis 751

Flowcharts 751

Pareto Analysis 752

Cause-and-Effect (Fishbone) Diagrams 753

Control Charts 754

Check Sheets or Checklists 755

Histogram 756

Scatter Chart or Scatter Diagram 756

18.3 Control Charts 757

Variation 758

Types of Control Charts 758

 Chart 758

R Charts 762

p Charts 763

c Charts 766

Interpreting Control Charts 768

Summary 774

Key Terms 775

Formulas 776

Supplementary Problems 776

Analyzing the Databases 779

Case: Robotron-elotherm 780

Using the Computer 781

Appendices

A Tables 783

B Answers to Selected Odd-Numbered

Quantitative Problems 823

Glossary 833

Index 843

  • NEW Topic in Chapter 6: “Using Probabilities to Solve for the Mean, the Standard Deviation, or an x value in a Normal Distribution”, explaining how to solve problems in which the student is required to use their normal curve skills to work problems “backwards” to find an unknown mean, standard deviation, or x value using the standard normal distribution table.
  • NEW Topic in Chapter 9: The more standard and well-known 8-step approach to testing hypotheses is presented, thereby replacing the HTAB system of testing hypotheses.
  • UPDATED Thinking Critically about Business Statistics: this feature provides thought-provoking questions to promote application and analysis of business statistics principles.
  • UPDATED and NEW Visuals: Graphs, tables, and figures have been added throughout the text to illustrate and underscore concepts.
  • NEW Decision Dilemmas: Three new Decision Dilemmas (Chapters 8, 9, and 10) have been added to this edition. New companies featured in these Decision Dilemmas include Valero Energy and L.L. Bean.
  • WileyPLUS -WileyPLUS online learning environment features all end-of-chapter problems, author video tutorials, databases, applets, student solutions and Excel Manual for students.
  • Decision Dilemma & Decision Dilemma Solved - Chapter-opening vignettes are brief business-world scenarios which use the techniques introduced in the chapter to solve a business decision dilemma; at the end of chapters, dilemmas are addressed to reinforce the chapter concept.
  • Ethical Considerations - A feature in each chapter, Ethical Considerations integrate the topic of ethics with applications of business statistics.
  • Tree Taxonomy Diagrams - Further illustrate the connection between topics and techniques and the ability to see the big picture of inferential statistics.