## Statistics for Business and Economics, 14th Edition

By George Benson, Terry Sincich and James T. Mcclave

**Contents:**

Preface 13

1. Statistics, Data, and Statistical Thinking 19

1.1 The Science of Statistics 22

1.2 Types of Statistical Applications in Business 22

1.3 Fundamental Elements of Statistics 25

1.4 Processes (Optional) 29

1.5 Types of Data 32

1.6 Collecting Data: Sampling and Related Issues 33

1.7 Business Analytics: Critical Thinking with Statistics 40

STATISTICS IN ACTION: A 20/20 View of Surveys and Studies: Facts or Fake News? 19

ACTIVITY 1.1: Keep the Change: Collecting Data 49

ACTIVITY 1.2: Identifying Misleading Statistics 49

USING TECHNOLOGY: Accessing and Listing Data 50

2. Methods for Describing Sets of Data 57

2.1 Describing Qualitative Data 59

2.2 Graphical Methods for Describing Quantitative Data 69

2.3 Numerical Measures of Central Tendency 81

2.4 Numerical Measures of Variability 92

2.5 Using the Mean and Standard Deviation to Describe Data 98

2.6 Numerical Measures of Relative Standing 106

2.7 Methods for Detecting Outliers: Box Plots and z-Scores 111

2.8 Graphing Bivariate Relationships (Optional) 121

2.9 The Time Series Plot (Optional) 126

2.10 Distorting the Truth with Descriptive Techniques 128

STATISTICS IN ACTION: Can Money Buy Love? 57

ACTIVITY 2.1: Real Estate Sales 141

ACTIVITY 2.2: Keep the Change: Measures of Central Tendency and Variability 142

USING TECHNOLOGY: Describing Data 142

MAKING BUSINESS DECISIONS: The Kentucky Milk Case—Part I (Covers Chapters 1 and 2) 148

3. Probability 150

3.1 Events, Sample Spaces, and Probability 152

3.2 Unions and Intersections 166

3.3 Complementary Events 169

3.4 The Additive Rule and Mutually Exclusive Events 171

3.5 Conditional Probability 178

3.6 The Multiplicative Rule and Independent Events 181

3.7 Bayes’s Rule 191

STATISTICS IN ACTION: Lotto Buster! 150

ACTIVITY 3.1: Exit Polls: Conditional Probability 204

ACTIVITY 3.2: Keep the Change: Independent Events 204

USING TECHNOLOGY: Combinations and Permutations 205

4. Random Variables and Probability Distributions 208

4.1 Two Types of Random Variables 209

PART I: DISCRETE RANDOM VARIABLES 212

4.2 Probability Distributions for Discrete Random Variables 212

4.3 The Binomial Distribution 223

4.4 Other Discrete Distributions: Poisson and Hypergeometric 236

PART II: CONTINUOUS RANDOM VARIABLES 243

4.5 Probability Distributions for Continuous Random Variables 243

4.6 The Normal Distribution 244

4.7 Descriptive Methods for Assessing Normality 261

4.8 Other Continuous Distributions: Uniform and Exponential 266

STATISTICS IN ACTION: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? 208

ACTIVITY 4.1: Warehouse Club Memberships: Exploring a Binomial Random Variable 282

ACTIVITY 4.2: Identifying the Type of Probability Distribution 283

USING TECHNOLOGY: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots 284

5. Sampling Distributions 291

5.1 The Concept of a Sampling Distribution 293

5.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 299

5.3 The Sampling Distribution of the Sample Mean and the Central Limit Theorem 303

5.4 The Sampling Distribution of the Sample Proportion 312

STATISTICS IN ACTION: The Insomnia Pill: Is It Effective? 291

ACTIVITY 5.1: Simulating a Sampling Distribution—Cell Phone Usage 322

USING TECHNOLOGY: Simulating a Sampling Distribution 323

MAKING BUSINESS DECISIONS: The Furniture Fire Case (Covers Chapters 3–5) 326

6. Inferences Based on a Single Sample: Estimation with Confidence Intervals 328

6.1 Identifying and Estimating the Target Parameter 330

6.2 Confidence Interval for a Population Mean: Normal (z) Statistic 331

6.3 Confidence Interval for a Population Mean: Student’s t-Statistic 339

6.4 Large-Sample Confidence Interval for a Population Proportion 349

6.5 Determining the Sample Size 356

6.6 Finite Population Correction for Simple Random Sampling (Optional) 363

6.7 Confidence Interval for a Population Variance (Optional) 366

STATISTICS IN ACTION: Medicare Fraud Investigations 328

ACTIVITY 6.1: Conducting a Pilot Study 378

USING TECHNOLOGY: Confidence Intervals and Sample Size Determination 379

7. Inferences Based on a Single Sample: Tests of Hypotheses 387

7.1 The Elements of a Test of Hypothesis 388

7.2 Formulating Hypotheses and Setting Up the Rejection Region 393

7.3 Observed Significance Levels: p-Values 399

7.4 Test of Hypothesis About a Population Mean: Normal (z) Statistic 403

7.5 Test of Hypothesis About a Population Mean: Student’s t-Statistic 412

7.6 Large-Sample Test of Hypothesis About a Population Proportion 419

7.7 Test of Hypothesis About a Population Variance 427

7.8 Calculating Type II Error Probabilities: More About b (Optional) 432

STATISTICS IN ACTION: Diary of a Kleenex® User—How Many Tissues in a Box? 387

ACTIVITY 7.1: Challenging a Company’s Claim: Tests of Hypotheses 446

ACTIVITY 7.2: Keep the Change: Tests of Hypotheses 446

USING TECHNOLOGY: Tests of Hypotheses 447

8. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 454

8.1 Identifying the Target Parameter 455

8.2 Comparing Two Population Means: Independent Sampling 456

8.3 Comparing Two Population Means: Paired Difference Experiments 472

8.4 Comparing Two Population Proportions: Independent Sampling 483

8.5 Determining the Required Sample Size 491

8.6 Comparing Two Population Variances: Independent Sampling 496

STATISTICS IN ACTION: ZixIt Corp. v. Visa USA Inc.—A Libel Case 454

ACTIVITY 8.1: Box Office Receipts: Comparing Population Means 514

ACTIVITY 8.2: Keep the Change: Inferences Based on Two Samples 514

USING TECHNOLOGY: Two-Sample Inferences 515

MAKING BUSINESS DECISIONS: The Kentucky Milk Case—Part II (Covers Chapters 6–8) 525

9. Design of Experiments and Analysis of Variance 526

9.1 Elements of a Designed Experiment 528

9.2 The Completely Randomized Design: Single Factor 534

9.3 Multiple Comparisons of Means 551

9.4 The Randomized Block Design 558

9.5 Factorial Experiments: Two Factors 572

STATISTICS IN ACTION: Tax Compliance Behavior—Factors That Affect Your Level of

Risk Taking When Filing Your Federal Tax Return 526

ACTIVITY 9.1: Designed vs. Observational Experiments 598

USING TECHNOLOGY: Analysis of Variance 599

10. Categorical Data Analysis 603

10.1 Categorical Data and the Multinomial Experiment 604

10.2 Testing Category Probabilities: One-Way Table 606

10.3 Testing Category Probabilities: Two-Way (Contingency) Table 613

10.4 A Word of Caution About Chi-Square Tests 629

STATISTICS IN ACTION: The Illegal Transplant Tissue Trade—Who Is Responsible for Paying Damages? 603

ACTIVITY 10.1: Binomial vs. Multinomial Experiments 635

ACTIVITY 10.2: Contingency Tables 636

USING TECHNOLOGY: Chi-Square Analyses 636

Making Business Decision: Discrimination in the Workplace (Covers Chapters 9–10) 641

11. Simple Linear Regression 644

11.1 Probabilistic Models 646

11.2 Fitting the Model: The Least Squares Approach 650

11.3 Model Assumptions 662

11.4 Assessing the Utility of the Model: Making Inferences About the Slope b1 667

11.5 The Coefficients of Correlation and Determination 675

11.6 Using the Model for Estimation and Prediction 684

11.7 A Complete Example 693

STATISTICS IN ACTION: Legal Advertising—Does It Pay? 644

ACTIVITY 11.1: Applying Simple Linear Regression to Your Favorite Data 707

USING TECHNOLOGY: Simple Linear Regression 707

12. Multiple Regression and Model Building 711

12.1 Multiple Regression Models 712

PART I: FIRST-ORDER MODELS WITH QUANTITATIVE INDEPENDENT VARIABLES 714

12.2 Estimating and Making Inferences About the b Parameters 714

12.3 Evaluating Overall Model Utility 720

12.4 Using the Model for Estimation and Prediction 731

PART II: MODEL BUILDING IN MULTIPLE REGRESSION 737

12.5 Interaction Models 737

12.6 Quadratic and Other Higher-Order Models 744

12.7 Qualitative (Dummy) Variable Models 754

12.8 Models with Both Quantitative and Qualitative Variables 762

12.9 Comparing Nested Models 771

12.10 Stepwise Regression 778

PART III: MULTIPLE REGRESSION DIAGNOSTICS 787

12.11 Residual Analysis: Checking the Regression Assumptions 787

12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 800

STATISTICS IN ACTION: Bid Rigging in the Highway Construction Industry 711

ACTIVITY 12.1: Insurance Premiums: Collecting Data for Several Variables 821

ACTIVITY 12.2: Collecting Data and Fitting a Multiple Regression Model 822

USING TECHNOLOGY: Multiple Regression 822

MAKING BUSINESS DECISIONS: The Condo Sales Case (Covers Chapters 11–12) 828

13. Methods for Quality Improvement: Statistical Process Control (Available Online) 13-1

13.1 Quality, Processes, and Systems 13-3

13.2 Statistical Control 13-6

13.3 The Logic of Control Charts 13-13

13.4 A Control Chart for Monitoring the Mean of a Process: The x-Chart 13-17

13.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart 13-33

13.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart 13-43

13.7 Diagnosing the Causes of Variation 13-52

13.8 Capability Analysis 13-55

STATISTICS IN ACTION: Testing Jet Fuel Additive for Safety 13-1

ACTIVITY 13.1: Quality Control: Consistency 13-66

USING TECHNOLOGY: Control Charts 13-67

MAKING BUSINESS DECISIONS: The Gasket Manufacturing Case (Covers Chapter 13) 13-70

14. Time Series: Descriptive Analyses, Models, and Forecasting (Available Online) 14-1

14.1 Descriptive Analysis: Index Numbers 14-2

14.2 Descriptive Analysis: Exponential Smoothing 14-12

14.3 Time Series Components 14-16

14.4 Forecasting: Exponential Smoothing 14-17

14.5 Forecasting Trends: Holt’s Method 14-20

14.6 Measuring Forecast Accuracy: MAD and RMSE 14-25

14.7 Forecasting Trends: Simple Linear Regression 14-29

14.8 Seasonal Regression Models 14-32

14.9 Autocorrelation and the Durbin-Watson Test 14-39

STATISTICS IN ACTION: Forecasting the Monthly Sales of a New Cold Medicine 14-1

ACTIVITY 14.1: Time Series 14-49

USING TECHNOLOGY: Forecasting 14-50

15. Nonparametric Statistics (Available Online) 15-1

15.1 Introduction: Distribution-Free Tests 15-2

15.2 Single Population Inferences 15-3

15.3 Comparing Two Populations: Independent Samples 15-8

15.4 Comparing Two Populations: Paired Difference Experiment 15-19

15.5 Comparing Three or More Populations: Completely Randomized Design 15-27

15.6 Comparing Three or More Populations: Randomized **Block Design** 15-34

15.7 Rank Correlation 15-40

STATISTICS IN ACTION: Pollutants at a Housing Development—A Case of Mishandling Small Samples 15-1

ACTIVITY 15.1: Keep the Change: Nonparametric Statistics 15-54

USING TECHNOLOGY: Nonparametric Tests 15-55

MAKING BUSINESS DECISIONS: Detecting “Sales Chasing” (Covers Chapters 10 and 15) 15-62

Appendix A: Summation Notation 830

Appendix B: Basic Counting Rules 832

Appendix C: Calculation Formulas for Analysis of Variance 835

C.1 Formulas for the Calculations in the Completely Randomized Design 835

C.2 Formulas for the Calculations in the Randomized Block Design 836

C.3 Formulas for the Calculations for a Two-Factor Factorial Experiment 837

C.4 Tukey’s Multiple Comparisons Procedure (Equal Sample Sizes) 838

C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons) 839

C.6 Scheffé’s Multiple Comparisons Procedure (Pairwise Comparisons) 839

Appendix D: Tables 840

Table I Binomial Probabilities 841

Table II Normal Curve Areas 844

Table III Critical Values of t 845

Table IV Critical Values of x2 846

Table V Percentage Points of the F-Distribution, a = .10 848

Table VI Percentage Points of the F-Distribution, a = .05 850

Table VII Percentage Points of the F-Distribution, a = .025 852

Table VIII Percentage Points of the F-Distribution, a = .01 854

Table IX Control Chart Constants 856

Table X Critical Values for the Durbin-Watson d-Statistic, a = .05 857

Table XI Critical Values for the Durbin-Watson d-Statistic, a = .01 858

Table XII Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples 859

Table XIII Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test 860

Table XIV Critical Values of Spearman’s Rank Correlation Coefficient 861

Table XV Critical Values of the Studentized Range, a = .05 862

Answers to Selected Exercises 863

Index 875

Credits 885