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
ABOUT THE AUTHORS xxv
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
Simpson’s Paradox 69
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
(MindTap Reader)
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
Addition Law 188
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
(MindTap Reader)
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
Practical Advice 317
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
Practical Advice 362
8.2 Population Mean: _ Unknown 364
Margin of Error and the Interval Estimate 365
Using Excel 368
Practical Advice 369
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
Summary and Practical Advice 414
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
Summary and Practical Advice 425
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
Practical Advice 454
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
Practical Advice 463
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
Appendix 10.1 Inferences About Two Populations with R (MindTap Reader)
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 D _Answers to Even-Numbered Exercises (MindTap Reader)
Appendix E _Microsoft Excel and Tools for Statistical Analysis 749
Appendix F Microsoft Excel Online and Tools for Statistical Analysis 757
Index 765