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