## Statistics for Managers Using Microsoft Excel, Global Edition

(2016)

### Abstract

Analyzing the Data Applicable to Business

This text is the gold standard for learning how to use Microsoft Excel® in business statistics, helping students gain the understanding they need to be successful in their careers. The authors present statistics in the context of specific business fields; full chapters on business analytics further prepare students for success in their professions. Current data throughout the text lets students practice analyzing the types of data they will see in their professions. The friendly writing style include tips throughout to encourage learning.

The book also integrates PHStat, an add-in that bolsters the statistical functions of Excel.

MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor.

Section Title Page Action Price
Cover Cover
A Roadmap for Selectinga Statistical Method 1
Title Page 3
Brief Contents 7
Contents 8
Preface 17
Resources for Success 23
First Things First 25
Using Statistics: “The Price of Admission” 25
Now Appearing on Broadway . . . and Everywhere Else 26
FTF.1 Think Differently About Statistics 26
Statistics: A Way of Thinking 26
Analytical Skills More Important than Arithmetic Skills 27
FTF.2 Business Analytics: The Changing Face of Statistics 28
“Big Data” 28
Structured Versus Unstructured Data 28
FTF.3 Getting Started Learning Statistics 29
Statistic 29
Can Statistics (pl., Statistic) Lie? 30
FTF.4 Preparing to Use Microsoft Excel for Statistics 30
Reusability Through Recalculation 31
Practical Matters: Skills You Need 31
Ways of Working with Excel 31
Excel Guides 32
Which Excel Version to Use? 32
Conventions Used 32
References 33
Key Terms 33
Excel Guide 34
EG.1 Entering Data 34
EG.2 Reviewing Worksheets 34
EG.3 If You Plan to Use the Workbook Instructions 35
1 Defining and Collecting Data 36
Using Statistics: Defining Moments 36
1.1 Defining Variables 37
Classifying Variables by Type 38
Measurement Scales 38
1.2 Collecting Data 39
Populations and Samples 40
Data Sources 40
1.3 Types of Sampling Methods 41
Simple Random Sample 42
Systematic Sample 42
Stratified Sample 43
Cluster Sample 43
1.4 Data Preparation 44
Data Cleaning 44
Data Formatting 45
Stacked and Unstacked Variables 45
Recoding Variables 46
1.5 Types of Survey Errors 47
Coverage Error 47
Nonresponse Error 47
Sampling Error 47
Measurement Error 48
Consider This: New Media Surveys/Old Survey Errors 48
Using Statistics: Defining Moments, Revisited 50
Summary 50
References 50
Key Terms 50
Chapter Review Problems 51
Cases For Chapter 1 52
Managing Ashland MultiComm Services 52
CardioGood Fitness 52
Clear Mountain State Student Survey 53
Learning with the Digital Cases 53
Chapter 1 Excel Guide 54
EG1.1 Defining Variables 54
EG1.2 Collecting Data 54
EG1.3 Types of Sampling Methods 54
EG1.4 Data Preparation 55
2 Organizing and Visualizing Variables 56
Using Statistics: “The Choice Is Yours” 56
2.1 Organizing Categorical Variables 57
The Summary Table 57
The Contingency Table 58
2.2 Organizing Numerical Variables 61
The Frequency Distribution 62
Classes and Excel Bins 64
The Relative Frequency Distribution and the Percentage Distribution 65
The Cumulative Distribution 67
2.3 Visualizing Categorical Variables 70
The Bar Chart 70
The Pie Chart and the Doughnut Chart 71
The Pareto Chart 72
Visualizing Two Categorical Variables 74
2.4 Visualizing Numerical Variables 76
The Stem-and-Leaf Display 77
The Histogram 78
The Percentage Polygon 79
The Cumulative Percentage Polygon (Ogive) 80
2.5 Visualizing Two Numerical Variables 83
The Scatter Plot 83
The Time-Series Plot 85
2.6 Organizing and Visualizing a Mix of Variables 87
Multidimensional Contingency Table 87
Adding a Numerical Variable to a Multidimensional Contingency Table 88
Drill Down 88
Excel Slicers 89
PivotChart 90
Sparklines 90
2.7 The Challenge in Organizing and Visualizing Variables 92
Obscuring Data 92
Creating False Impressions 93
Chartjunk 94
EXHIBIT: Best Practices for Creating Visualizations 96
Using Statistics: The Choice Is Yours, Revisited 97
Summary 97
References 98
Key Equations 98
Key Terms 99
Chapter Review Problems 99
Cases For Chapter 2 104
Managing Ashland MultiComm Services 104
Digital Case 104
CardioGood Fitness 105
The Choice Is Yours Follow-Up 105
Clear Mountain State Student Survey 105
Chapter 2 Excel Guide 106
EG2.1 Organizing Categorical Variables 106
EG2.2 Organizing Numerical Variables 108
EG2.3 Visualizing Categorical Variables 110
EG2.4 Visualizing Numerical Variables 112
EG2.5 Visualizing Two Numerical Variables 116
EG2.6 Organizing and Visualizing a Set of Variables 116
3 Numerical Descriptive Measures 119
Using Statistics: More Descriptive Choices 119
3.1 Central Tendency 120
The Mean 120
The Median 122
The Mode 123
The Geometric Mean 124
3.2 Variation and Shape 125
The Range 125
The Variance and the Standard Deviation 126
EXHIBIT: Manually Calculating the Sample Variance, S², and Sample Standard Deviation, S 127
The Coefficient of Variation 129
Z Scores 130
Shape: Skewness 132
Shape: Kurtosis 132
3.3 Exploring Numerical Data 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 149
3.6 Statistics: Pitfalls and Ethical Issues 154
Using Statistics: More Descriptive Choices, Revisited 154
Summary 154
References 155
Key Equations 155
Key Terms 156
Chapter Review Problems 157
Cases For Chapter 3 160
Managing Ashland MultiComm Services 160
Digital Case 160
CardioGood Fitness 160
More Descriptive Choices Follow-up 160
Clear Mountain State Student Survey 160
Chapter 3 Excel Guide 161
EG3.1 Central Tendency 161
EG3.2 Variation and Shape 162
EG3.3 Exploring Numerical Data 162
EG3.4 Numerical Descriptive Measures for a Population 163
EG3.5 The Covariance and the Coefficient of Correlation 163
4 Basic Probability 165
Using Statistics: Possibilities at M&R Electronics World 165
4.1 Basic Probability Concepts 166
Events and Sample Spaces 167
Contingency Tables 169
Simple Probability 169
Joint Probability 170
Marginal Probability 171
4.2 Conditional Probability 175
Computing Conditional Probabilities 175
Decision Trees 176
Independence 178
Multiplication Rules 179
Marginal Probability Using the General Multiplication Rule 180
4.3 Ethical Issues and Probability 182
4.4 Bayes’ Theorem 183
Consider This: Divine Providence and Spam 183
4.5 Counting Rules 184
Using Statistics: Possibilities at M&R Electronics World, Revisited 185
Summary 185
References 185
Key Equations 185
Key Terms 186
Chapter Review Problems 186
Cases For Chapter 4 188
Digital Case 188
CardioGood Fitness 188
The Choice Is Yours Follow-Up 188
Clear Mountain State Student Survey 188
Chapter 4 Excel Guide 189
EG4.1 Basic Probability Concepts 189
EG4.4 Bayes’ Theorem 189
5 Discrete Probability Distributions 190
Using Statistics: Events of Interest at Ricknel Home Centers 190
5.1 The Probability Distribution for a Discrete Variable 191
Expected Value of a Discrete Variable 191
Variance and Standard Deviation of a Discrete Variable 192
5.2 Binomial Distribution 195
5.3 Poisson Distribution 202
5.4 Covariance of a Probability Distribution and its Application in Finance 205
5.5 Hypergeometric Distribution 206
Using Statistics: Events of Interest…, Revisited 206
Summary 206
References 206
Key Equations 206
Key Terms 207
Chapter Review Problems 207
Cases For Chapter 5 209
Managing Ashland MultiComm Services 209
Digital Case 210
Chapter 5 Excel Guide 211
EG5.1 The Probability Distribution for a Discrete Variable 211
EG5.2 Binomial Distribution 211
EG5.3 Poisson Distribution 212
6 The Normal Distribution and Other Continuous Distributions 213
Using Statistics: Normal Load Times at MyTVLab 213
6.1 Continuous Probability Distributions 214
6.2 The Normal Distribution 215
EXHIBIT: Normal Distribution Important Theoretical Properties 215
Computing Normal Probabilities 216
VISUAL EXPLORATIONS: Exploring the Normal Distribution 222
Finding X Values 222
Consider This: What Is Normal? 226
6.3 Evaluating Normality 227
Comparing Data Characteristics to Theoretical Properties 228
Constructing the Normal Probability Plot 229
6.4 The Uniform Distribution 231
6.5 The Exponential Distribution 233
6.6 The Normal Approximation to the Binomial Distribution 233
Using Statistics: Normal Load Times…, Revisited 234
Summary 234
References 234
Key Equations 235
Key Terms 235
Chapter Review Problems 235
Cases For Chapter 6 237
Managing Ashland MultiComm Services 237
CardioGood Fitness 237
More Descriptive Choices Follow-up 237
Clear Mountain State Student Survey 237
Digital Case 237
Chapter 6 Excel Guide 238
EG6.1 Continuous Probability Distributions 238
EG6.2 The Normal Distribution 238
EG6.3 Evaluating Normality 238
7 Sampling Distributions 240
Using Statistics: Sampling Oxford Cereals 240
7.1 Sampling Distributions 241
7.2 Sampling Distribution of the Mean 241
The Unbiased Property of the Sample Mean 241
Standard Error of the Mean 243
Sampling from Normally Distributed Populations 244
Sampling from Non-normally Distributed Populations— The Central Limit Theorem 247
EXHIBIT: Normality and the Sampling Distribution of the Mean 248
VISUAL EXPLORATIONS: Exploring Sampling Distributions 251
7.3 Sampling Distribution of the Proportion 252
Using Statistics: Sampling Oxford Cereals, Revisited 255
Summary 256
References 256
Key Equations 256
Key Terms 256
Chapter Review Problems 257
Cases For Chapter 7 259
Managing Ashland Multicomm Services 259
Digital Case 259
Chapter 7 Excel Guide 260
EG7.2 Sampling Distribution of the Mean 260
8 Confidence Interval Estimation 261
Using Statistics: Getting Estimates at Ricknel Home Centers 261
8.1 Confidence Interval Estimate for the Mean (σ Known) 262
Can You Ever Know the Population Standard Deviation? 267
8.2 Confidence Interval Estimate for the Mean (σ Unknown) 268
Student’s t Distribution 268
Properties of the t Distribution 269
The Concept of Degrees of Freedom 270
The Confidence Interval Statement 271
8.3 Confidence Interval Estimate for the Proportion 276
8.4 Determining Sample Size 279
Sample Size Determination for the Mean 279
Sample Size Determination for the Proportion 281
8.5 Confidence Interval Estimation and Ethical Issues 284
8.6 Application of Confidence Interval Estimation in Auditing 285
8.7 Estimation and Sample Size Estimation for Finite Populations 285
8.8 Bootstrapping 285
Using Statistics: Getting Estimates. . ., Revisited 285
Summary 286
References 286
Key Equations 286
Key Terms 287
Chapter Review Problems 287
Cases For Chapter 8 290
Managing Ashland MultiComm Services 290
Digital Case 291
Sure Value Convenience Stores 291
CardioGood Fitness 291
More Descriptive Choices Follow-Up 291
Clear Mountain State Student Survey 291
Chapter 8 Excel Guide 292
EG8.1 Confidence Interval Estimate for the Mean (σ Known) 292
EG8.2 Confidence Interval Estimate for the Mean (σ Unknown) 292
EG8.3 Confidence Interval Estimate for the Proportion 293
EG8.4 Determining Sample Size 293
9 Fundamentals of Hypothesis Testing: One-Sample Tests 294
Using Statistics: Significant Testing at Oxford Cereals 294
9.1 Fundamentals of Hypothesis-Testing Methodology 295
The Null and Alternative Hypotheses 295
The Critical Value of the Test Statistic 296
Regions of Rejection and Nonrejection 297
Risks in Decision Making Using Hypothesis Testing 297
Z Test for the Mean (σ Known) 300
Hypothesis Testing Using the Critical Value Approach 300
EXHIBIT: The Critical Value Approach to Hypothesis Testing 301
Hypothesis Testing Using the p-Value Approach 303
EXHIBIT: The p-Value Approach to Hypothesis Testing 304
A Connection Between Confidence Interval Estimation and Hypothesis Testing 305
Can You Ever Know the Population Standard Deviation? 306
9.2 t Test of Hypothesis for the Mean (σ Unknown) 308
The Critical Value Approach 308
p-Value Approach 310
Checking the Normality Assumption 310
9.3 One-Tail Tests 314
The Critical Value Approach 314
The p-Value Approach 315
EXHIBIT: The Null and Alternative Hypotheses in One-Tail Tests 317
9.4 Z Test of Hypothesis for the Proportion 318
The Critical Value Approach 319
The p-Value Approach 320
9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues 322
EXHIBIT: Questions for the Planning Stage of Hypothesis Testing 322
Statistical Significance Versus Practical Significance 323
Statistical Insignificance Versus Importance 323
Reporting of Findings 323
Ethical Issues 323
9.6 Power of the Test 324
Using Statistics: Significant Testing. . ., Revisited 324
Summary 324
References 325
Key Equations 325
Key Terms 325
Chapter Review Problems 326
Cases For Chapter 9 328
Managing Ashland MultiComm Services 328
Digital Case 328
Sure Value Convenience Stores 328
Chapter 9 Excel Guide 329
EG9.1 Fundamentals of Hypothesis-Testing Methodology 329
EG9.2 t Test of Hypothesis for the Mean (σ Unknown) 329
EG9.3 One-Tail Tests 330
EG9.4 Z Test of Hypothesis for the Proportion 330
10 Two-Sample Tests 331
Using Statistics: Differing Means for Selling Streaming Media Players at Arlingtons? 331
10.1 Comparing the Means of Two Independent Populations 332
Pooled-Variance t Test for the Difference Between Two Means 332
Confidence Interval Estimate for the Difference Between Two Means 337
t Test for the Difference Between Two Means, Assuming Unequal Variances 338
Consider This: Do People Really Do This? 339
10.2 Comparing the Means of Two Related Populations 341
Paired t Test 342
Confidence Interval Estimate for the Mean Difference 347
10.3 Comparing the Proportions of Two Independent Populations 349
Z Test for the Difference Between Two Proportions 350
Confidence Interval Estimate for the Difference Between Two Proportions 354
10.4 F Test for the Ratio of Two Variances 356
10.5 Effect Size 360
Using Statistics: Differing Means for Selling. . ., Revisited 361
Summary 361
References 362
Key Equations 362
Key Terms 363
Chapter Review Problems 363
Cases For Chapter 10 365
Managing Ashland MultiComm Services 365
Digital Case 366
Sure Value Convenience Stores 366
CardioGood Fitness 366
More Descriptive Choices Follow-Up 366
Clear Mountain State Student Survey 366
Chapter 10 Excel Guide 367
EG10.1 Comparing The Means of Two Independent Populations 367
EG10.2 Comparing the Means of Two Related Populations 369
EG10.3 Comparing the Proportions of Two Independent Populations 370
EG10.4 F Test for the Ratio of Two Variances 371
11 Analysis of Variance 372
Using Statistics: The Means to Find Differences at Arlingtons 372
11.1 The Completely Randomized Design: One-Way ANOVA 373
Analyzing Variation in One-Way ANOVA 374
F Test for Differences Among More Than Two Means 376
One-Way ANOVA F Test Assumptions 380
Levene Test for Homogeneity of Variance 381
Multiple Comparisons: The Tukey-Kramer Procedure 382
The Analysis of Means (ANOM) 384
11.2 The Factorial Design: Two-Way ANOVA 387
Factor and Interaction Effects 388
Testing for Factor and Interaction Effects 390
Multiple Comparisons: The Tukey Procedure 393
Visualizing Interaction Effects: The Cell Means Plot 395
Interpreting Interaction Effects 395
11.3 The Randomized Block Design 399
11.4 Fixed Effects, Random Effects, and Mixed Effects Models 399
Using Statistics: The Means to Find Differences at Arlingtons Revisited 399
Summary 400
References 400
Key Equations 400
Key Terms 401
Chapter Review Problems 402
Cases For Chapter 11 404
Managing Ashland MultiComm Services 404
PHASE 1 404
PHASE 2 404
Digital Case 405
Sure Value Convenience Stores 405
CardioGood Fitness 405
More Descriptive Choices Follow-Up 405
Clear Mountain State Student Survey 405
Chapter 11 Excel Guide 406
EG11.1 The Completely Randomized Design: One-Way ANOVA 406
EG11.2 The Factorial Design: Two-Way ANOVA 408
12 Chi-Square and Nonparametric Tests 410
Using Statistics: Avoiding Guesswork about Resort Guests 410
12.1 Chi-Square Test for the Difference Between Two Proportions 411
12.2 Chi-Square Test for Differences Among More Than Two Proportions 418
The Marascuilo Procedure 421
The Analysis of Proportions (ANOP) 423
12.3 Chi-Square Test of Independence 424
12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations 430
12.5 Kruskal-Wallis Rank Test: A Nonparametric Method for the One-Way ANOVA 436
Assumptions 439
12.6 McNemar Test for the Difference Between Two Proportions (Related Samples) 441
12.7 Chi-Square Test for the Variance or Standard Deviation 441
Using Statistics: Avoiding Guesswork. . ., Revisited 442
Summary 442
References 443
Key Equations 443
Key Terms 444
Chapter Review Problems 444
Cases For Chapter 12 446
Managing Ashland MultiComm Services 446
PHASE 1 446
PHASE 2 446
Digital Case 447
Sure Value Convenience Stores 447
CardioGood Fitness 447
More Descriptive Choices Follow-Up 447
Clear Mountain State Student Survey 447
Chapter 12 Excel Guide 448
EG12.1 Chi-Square Test for the Difference Between Two Proportions 448
EG12.2 Chi-Square Test for Differences Among More Than Two Proportions 448
EG12.3 Chi-Square Test of Independence 449
EG12.4 Wilcoxon Rank Sum Test: a Nonparametric Method for Two Independent Populations 449
EG12.5 Kruskal-Wallis Rank Test: a Nonparametric Method for the One-Way ANOVA 450
13 Simple Linear Regression 451
Using Statistics: Knowing Customers at Sunflowers Apparel 451
13.1 Types of Regression Models 452
Simple Linear Regression Models 453
13.2 Determining the Simple Linear Regression Equation 454
The Least-Squares Method 454
Predictions in Regression Analysis: Interpolation Versus Extrapolation 457
Computing the Y Intercept, b0 and the Slope, b₁ 457
VISUAL EXPLORATIONS: Exploring Simple Linear Regression Coefficients 460
13.3 Measures of Variation 462
Computing the Sum of Squares 462
The Coefficient of Determination 463
Standard Error of the Estimate 465
13.4 Assumptions of Regression 467
13.5 Residual Analysis 467
Evaluating the Assumptions 467
13.6 Measuring Autocorrelation: The Durbin-Watson Statistic 471
Residual Plots to Detect Autocorrelation 471
The Durbin-Watson Statistic 472
13.7 Inferences About the Slope and Correlation Coefficient 475
t Test for the Slope 475
F Test for the Slope 477
Confidence Interval Estimate for the Slope 478
t Test for the Correlation Coefficient 479
13.8 Estimation of Mean Values and Prediction of Individual Values 482
The Confidence Interval Estimate for the Mean Response 482
The Prediction Interval for an Individual Response 483
13.9 Potential Pitfalls in Regression 486
EXHIBIT: Six Steps for Avoiding the Potential Pitfalls 486
Using Statistics: Knowing Customers. . ., Revisited 488
Summary 488
References 489
Key Equations 490
Key Terms 491
Chapter Review Problems 491
Cases For Chapter 13 495
Managing Ashland MultiComm Services 495
Digital Case 495
Brynne Packaging 495
Chapter 13 Excel Guide 496
EG13.2 Determining the Simple Linear Regression Equation 496
EG13.3 Measures of Variation 497
EG13.4 Assumptions of Regression 497
EG13.5 Residual Analysis 497
EG13.6 Measuring Autocorrelation: The Durbin-Watson Statistic 498
EG13.7 Inferences about the Slope and Correlation Coefficient 498
EG13.8 Estimation of Mean Values and Prediction of Individual Values 498
14 Introduction to Multiple Regression 499
Using Statistics: The Multiple Effects of OmniPower Bars 499
14.1 Developing a Multiple Regression Model 500
Interpreting the Regression Coefficients 500
Predicting the Dependent Variable Y 503
14.2 r², Adjusted r², and the Overall F Test 505
Coefficient of Multiple Determination 505
Test for the Significance of the Overall Multiple Regression Model 506
14.3 Residual Analysis for the Multiple Regression Model 508
14.4 Inferences Concerning the Population Regression Coefficients 510
Tests of Hypothesis 510
Confidence Interval Estimation 511
14.5 Testing Portions of the Multiple Regression Model 513
Coefficients of Partial Determination 517
14.6 Using Dummy Variables and Interaction Terms in Regression Models 519
Interactions 521
14.7 Logistic Regression 528
Using Statistics: The Multiple Effects . . ., Revisited 533
Summary 533
References 535
Key Equations 535
Key Terms 536
Chapter Review Problems 536
Cases For Chapter 14 539
Managing Ashland MultiComm Services 539
Digital Case 539
Chapter 14 Excel Guide 541
EG14.1 Developing a Multiple Regression Model 541
EG14.2 r², Adjusted r², and the Overall F Test 542
EG14.3 Residual Analysis for the Multiple Regression Model 542
EG14.4 Inferences Concerning the Population Regression Coefficients 543
EG14.5 Testing Portions of the Multiple Regression Model 543
EG14.6 Using Dummy Variables and Interaction Terms in Regression Models 543
EG14.7 Logistic Regression 544
15 Multiple Regression Model Building 545
Using Statistics: Valuing Parsimony at WSTA-TV 545
Finding the Regression Coefficients and Predicting Y 546
Testing for the Significance of the Quadratic Model 549
The Coefficient of Multiple Determination 551
15.2 Using Transformations in Regression Models 553
The Square-Root Transformation 553
The Log Transformation 555
15.3 Collinearity 558
15.4 Model Building 559
The Stepwise Regression Approach to Model Building 561
The Best Subsets Approach to Model Building 562
Model Validation 565
EXHIBIT: Steps for Successful Model Building 566
15.5 Pitfalls in Multiple Regression and Ethical Issues 568
Pitfalls in Multiple Regression 568
Ethical Issues 568
Using Statistics: Valuing Parsimony…, Revisited 568
Summary 569
References 570
Key Equations 570
Key Terms 570
Chapter Review Problems 570
Cases For Chapter 15 572
The Mountain States Potato Company 572
Sure Value Convenience Stores 573
Digital Case 573
The Craybill Instrumentation Company Case 573
More Descriptive Choices Follow-Up 574
Chapter 15 Excel Guide 575
Eg15.1 The Quadratic Regression Model 575
Eg15.2 Using Transformations In Regression Models 575
Eg15.3 Collinearity 576
Eg15.4 Model Building 576
16 Time-Series Forecasting 577
Using Statistics: Principled Forecasting 577
16.1 The Importance of Business Forecasting 578
16.2 Component Factors of Time-Series Models 578
16.3 Smoothing an Annual Time Series 579
Moving Averages 580
Exponential Smoothing 582
16.4 Least-Squares Trend Fitting and Forecasting 585
The Linear Trend Model 585
The Exponential Trend Model 588
Model Selection Using First, Second, and Percentage Differences 590
16.5 Autoregressive Modeling for Trend Fitting and Forecasting 595
Selecting an Appropriate Autoregressive Model 596
Determining the Appropriateness of a Selected Model 597
EXHIBIT: Autoregressive Modeling Steps 599
16.6 Choosing an Appropriate Forecasting Model 604
Performing a Residual Analysis 604
Measuring the Magnitude of the Residuals Through Squared or Absolute Differences 605
Using the Principle of Parsimony 605
A Comparison of Four Forecasting Methods 605
16.7 Time-Series Forecasting of Seasonal Data 607
Least-Squares Forecasting with Monthly or Quarterly Data 608
16.8 Index Numbers 613
CONSIDER THIS: Let the Model User Beware 613
Using Statistics: Principled Forecasting, Revisited 613
Summary 614
References 615
Key Equations 615
Key Terms 616
Chapter Review Problems 616
Cases For Chapter 16 617
Managing Ashland MultiComm Services 617
Digital Case 617
Chapter 16 Excel Guide 618
Eg16.3 Smoothing an Annual Time Series 618
Eg16.4 Least-Squares Trend Fitting and Forecasting 619
Eg16.5 Autoregressive Modeling for Trend Fitting and Forecasting 620
Eg16.6 Choosing an Appropriate Forecasting Model 620
Eg16.7 Time-Series Forecasting of Seasonal Data 621
17 Getting Ready to Analyze Data in the Future 622
Using Statistics: Mounting Future Analyses 622
17.1 Analyzing Numerical Variables 623
EXHIBIT: Questions to Ask When Analyzing Numerical Variables 623
Describe the Characteristics of a Numerical Variable? 623
Reach Conclusions about the Population Mean or the Standard Deviation? 623
Determine Whether the Mean and/or Standard Deviation Differs Depending on the Group? 624
Determine Which Factors Affect the Value of a Variable? 624
Predict the Value of a Variable Based on the Values of Other Variables? 625
Determine Whether the Values of a Variable Are Stable Over Time? 625
17.2 Analyzing Categorical Variables 625
EXHIBIT: Questions to Ask When Analyzing Categorical Variables 625
Describe the Proportion of Items of Interest in Each Category? 625
Reach Conclusions about the Proportion of Items of Interest? 625
Determine Whether the Proportion of Items of Interest Differs Depending on the Group? 626
Predict the Proportion of Items of Interest Based on the Values of Other Variables? 626
Determine Whether the Proportion of Items of Interest Is Stable Over Time? 626
Using Statistics: Back to Arlingtons for the Future 626
17.3 Introduction to Business Analytics 627
Data Mining 627
Power Pivot 627
17.4 Descriptive Analytics 628
Dashboards 629
Dashboard Elements 629
17.5 Predictive Analytics 630
Classification and Regression Trees 631
Using Statistics: The Future to be Visited 632
References 632
Chapter Review Problems 632
Chapter 17 Excel Guide 635
EG17.3 Introduction to Business Analytics 635
EG17.4 Descriptive Analytics 635
18 Statistical Applications in Quality Management (online) 18-1
Using Statistics: Finding Quality at the Beachcomber 18-1
18.1 The Theory of Control Charts 18-2
18.2 Control Chart for the Proportion: The p Chart 18-4
18.3 The Red Bead Experiment: Understanding Process Variability 18-10
18.4 Control Chart for an Area of Opportunity: The c Chart 18-12
18.5 Control Charts for the Range and the Mean 18-15
The R Chart 18-16
The X Chart 18-18
18.6 Process Capability 18-21
Customer Satisfaction and Specification Limits 18-21
Capability Indices 18-23
CPL, CPU, and Cpk 18-24
18.7 Total Quality Management 18-26
18.8 Six Sigma 18-28
The DMAIC Model 18-29
Roles in a Six Sigma Organization 18-30
Lean Six Sigma 18-30
Using Statistics: Finding Quality at the Beachcomber, Revisited 18-31
Summary 18-31
References 18-32
Key Equations 18-32
Key Terms 18-33
Chapter Review Problems 18-34
Cases For Chapter 18 18-35
The Harnswell Sewing Machine Company Case 18-35
Managing Ashland Multicomm Services 18-38
Chapter 18 Excel Guide 18-39
EG18.1 The Theory of Control Charts 18-39
EG18.2 Control Chart for the Proportion: The p Chart 18-39
EG18.3 The Red Bead Experiment: Understanding Process Variability 18-40
EG18.4 Control Chart for an Area of Opportunity: The c Chart 18-40
EG18.5 Control Charts for the Range and the Mean 18-41
EG18.6 Process Capability 18-42
19 Decision Making (online) 19-1
Using Statistics: Reliable Decision Making 19-1
19.1 Payoff Tables and Decision Trees 19-2
19.2 Criteria for Decision Making 19-6
Maximax Payoff 19-6
Maximin Payoff 19-7
Expected Monetary Value 19-7
Expected Opportunity Loss 19-9
Return-to-Risk Ratio 19-11
19.3 Decision Making with Sample Information 19-16
19.4 Utility 19-21
Using Statistics: Reliable Decision-Making, Revisited 19-22
Summary 19-23
References 19-23
Key Equations 19-23
Key Terms 19-23
Chapter Review Problems 19-23
Cases For Chapter 19 19-26
Digital Case 19-26
Chapter 19 Excel Guide 19-27
EG19.1 Payoff Tables and Decision Trees 19-27
EG19.2 Criteria for Decision Making 19-27
Appendices 637
A. Basic Math Concepts and Symbols 638
A.1 Rules for Arithmetic Operations 638
A.2 Rules for Algebra: Exponents and Square Roots 638
A.3 Rules for Logarithms 639
A.4 Summation Notation 640
A.5 Statistical Symbols 643
A.6 Greek Alphabet 643
B Important Excel Skills and Concepts 644
B.1 Which Excel Do You Use? 644
B.2 Basic Operations 645
B.3 Formulas and Cell References 645
B.4 Entering a Formula 647
B.5 Formatting Cell Contents 648
B.6 Formatting Charts 649
B.7 Selecting Cell Ranges for Charts 650
B.8 Deleting the “Extra” Histogram Bar 651
B.9 Creating Histograms for Discrete Probability Distributions 651
C. Online Resources 652
C.1 About the Online Resources for This Book 652
C.2 Accessing the Online Resources 652
C.3 Details of Online Resources 652
C.4 PHStat 659
D. Configuring Microsoft Excel 660
D.1 Getting Microsoft Excel Ready for Use 660
D.2 Checking for the Presence of the Analysis ToolPak or Solver Add-Ins 660
D.3 Configuring Microsoft Windows Excel Security Settings 660
E. Tables 662
E.1 Table of Random Numbers 662
E.2 The Cumulative Standardized Normal Distribution 664
E.3 Critical Values of t 666
E.4 Critical Values of X² 668
E.5 Critical Values of F 669
E.6 Lower and Upper Critical Values, T₁, of the Wilcoxon Rank Sum Test 673
E.7 Critical Values of the Studentized Range, Q 674
E.8 Critical Values, dL and dU, of the Durbin–Watson Statistic, D (Critical Values Are One-Sided) 676
E.9 Control Chart Factors 677
E.10 The Standardized Normal Distribution 678
F. Useful Excel Knowledge 679
F.1 Useful Keyboard Shortcuts 679
F.2 Verifying Formulas and Worksheets 679
F.3 New Function Names 679
F.4 Understanding the Nonstatistical Functions 681
G. Software FAQs 683
G.1 PHStat FAQs 683
G.2 Microsoft Excel FAQs 683
Self-Test Solutions and Answers to Selected Even-Numbered Problems 685
Index 714
A 714
B 714
C 714
D 715
E 715
F 715
G 715
H 715
I 715
J 715
K 715
L 716
M 716
N 717
O 717
P 717
Q 718
R 718
S 718
T 719
U 719
V 719
W 720
X 720
Y 720
Z 720
Credits 721