Document Sample

A STATISTICAL METHOD

TYPE OF DATA
Type of Analysis                               Numerical                                      Categorical
Describing a group          Ordered array, stem-and-leaf display, frequency        Summary table, bar chart, pie
or several groups           distribution, relative frequency distribution,         chart, Pareto chart (Sections 2.2, 2.4)
percentage distribution, cumulative percentage
distribution, histogram, polygon, cumulative
percentage polygon (Sections 2.3, 2.5)
Mean, median, mode, quartiles, geometric mean,
range, interquartile range, standard deviation,
variance, coefficient of variation, boxplot
(Sections 3.1, 3.2, 3.3)
Index numbers (Online Topic 16.8)

Inference about one group   Confidence interval estimate of the mean               Confidence interval estimate of the
(Sections 8.1 and 8.2)                                 proportion (Section 8.3)
t test for the mean (Section 9.2)                      Z test for the proportion
Chi-square test for a variance (Section 12.5)          (Section 9.4)

Comparing two groups        Tests for the difference in the means of two           Z test for the difference between
independent populations (Section 10.1)                 two proportions (Section 10.3)
Paired t test (Section 10.2)                           Chi-square test for the difference
F test for the difference between two variances        between two proportions
(Section 10.4)                                         (Section 12.1)
Wilcoxon rank sum test (Section 12.6)                  McNemar test for the difference
Wilcoxon signed ranks test (Online Topic 12.8)         between two proportions in related
samples (Section 12.4)

Comparing more              One-way analysis of variance (Section 11.1)            Chi-square test for differences
than two groups             Randomized block design (Section 11.2)                 among more than two proportions
Two-way analysis of variance (Section 11.3)            (Section 12.2)
Kruskal-Wallis test (Section 12.7)
Friedman rank test (Online Topic 12.9)

Analyzing the               Scatter plot, time series plot (Section 2.6)           Contingency table, side-by-side
relationship between        Covariance, coefficient of correlation (Section 3.5)   bar chart, (Sections 2.2, 2.4)
two variables               Simple linear regression (Chapter 13)                  Chi-square test of independence
t test of correlation (Section 13.7)                   (Section 12.3)
Time series forecasting (Chapter 16)

Analyzing the               Multiple regression (Chapters 14 and 15)               Multidimensional contingency
relationship between                                                               tables (Section 2.7)
two or more variables                                                              Logistic regression (Section 14.7)
Statistics: Concepts and
Applications
TWELFTH EDITION
Statistics: Concepts and
Applications
TWELFTH EDITION

Mark L. Berenson
Department of Management and Information Systems

School of Business, Montclair State University

David M. Levine
Department of Statistics and Computer Information Systems

Zicklin School of Business, Baruch College, City University of New York

Timothy C. Krehbiel
Department of Management

Richard T. Farmer School of Business, Miami University

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10 9 8 7 6 5 4 3 2 1
ISBN 10: 0-13-216838-3
ISBN 13: 978-0-13-216838-0
To our wives,
Rhoda B., Marilyn L., and, Patti K.,

and to our children,
Kathy, Lori, Sharyn, Ed, Rudy, and Rhonda

The textbook authors meet to discuss statistics at a Mets
baseball game. Shown left to right: David Levine, Mark
Berenson, and Tim Krehbiel.

Mark L. Berenson is Professor of Management and Information Systems at
Montclair State University (Montclair, New Jersey) and also Professor Emeritus of
Statistics and Computer Information Systems at Bernard M. Baruch College (City
University of New York). He currently teaches graduate and undergraduate courses in sta-
tistics and in operations management in the School of Business and an undergraduate
course in international justice and human rights that he co-developed in the College of
Humanities and Social Sciences.
Berenson received a B.A. in economic statistics and an M.B.A. in business statistics from
City College of New York and a Ph.D. in business from the City University of New York.
Berenson’s research has been published in Decision Sciences Journal of Innovative
Education, Review of Business Research, The American Statistician, Communications in
Statistics, Psychometrika, Educational and Psychological Measurement, Journal of
Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The
New York Statistician, Journal of Health Administration Education, Journal of Behavioral
Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The
Encyclopedia of Measurement & Statistics and Encyclopedia of Statistical Sciences. He is
co-author of 11 statistics texts published by Prentice Hall, including Statistics for
Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications,
and Business Statistics: A First Course.
Over the years, Berenson has received several awards for teaching and for innovative con-
tributions to statistics education. In 2005, he was the first recipient of The Catherine A.
Becker Service for Educational Excellence Award at Montclair State University.

David M. Levine                 is Professor Emeritus of Statistics and Computer Infor-
mation Systems at Baruch College (City University of New York). He received B.B.A. and
M.B.A. degrees in Statistics from City College of New York and a Ph.D. from New York
viii

University in Industrial Engineering and Operations Research. He is nationally recognized
as a leading innovator in statistics education and is the co-author of 14 books, including
such best-selling statistics textbooks as Statistics for Managers Using Microsoft Excel,
and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab.
He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has
Ever Been Afraid of Statistics, currently in its 2nd edition, Six Sigma for Green Belts and
Champions and Design for Six Sigma for Green Belts and Champions, and the author of
Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and
Quality Management, 3rd edition, McGraw-Hill/Irwin. He is also the author of Video
Review of Statistics and Video Review of Probability, both published by Video Aided
Instruction, and the statistics module of the MBA primer published by Cengage Learning.
He has published articles in various journals, including Psychometrika, The American
Statistician, Communications in Statistics, Decision Sciences Journal of Innovative
Education, Multivariate Behavioral Research, Journal of Systems Management, Quality
Progress, and The American Anthropologist, and given numerous talks at the Decision
Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics
More Effective in Schools and Business (MSMESB) conferences. Levine has also received
several awards for outstanding teaching and curriculum development from Baruch College.

Timothy C. Krehbiel                  is Professor of Management and Senior Associate
Dean of the Farmer School of Business at Miami University in Oxford, Ohio. He teaches
gious Instructional Innovation Award from the Decision Sciences Institute. He has also
received the Farmer School of Business Effective Educator Award and has twice been
named MBA professor of the year.
Krehbiel’s research interests span many areas of business and applied statistics. His work
has appeared in numerous journals, including Quality Management Journal, Ecological
Economics, International Journal of Production Research, Journal of Purchasing and
Supply Management, Journal of Applied Business Research, Journal of Marketing
Management, Communications in Statistics, Decision Sciences Journal of Innovative
Education, Journal of Education for Business, Marketing Education Review, Journal of
Accounting Education, and Teaching Statistics. He is a co-author of three statistics text-
Statistics, and Statistics for Managers Using Microsoft Excel. Krehbiel is also a co-author
of the book Sustainability Perspectives in Business and Resources.
Krehbiel graduated summa cum laude with a B.A. in history from McPherson College and
earned an M.S. and a Ph.D. in statistics from the University of Wyoming.
Brief Contents
Preface xxiii
1 Introduction 2
2 Organizing and Visualizing Data 26
3 Numerical Descriptive Measures 94
4 Basic Probability 144
5 Discrete Probability Distributions 180
6 The Normal Distribution and Other Continuous Distributions 216
7 Sampling and Sampling Distributions 248
8 Confidence Interval Estimation 278
9 Fundamentals of Hypothesis Testing: One-Sample Tests 324
10 Two-Sample Tests 364
11 Analysis of Variance 414
12 Chi-Square Tests and Nonparametric Tests 466
13 Simple Linear Regression 520
14 Introduction to Multiple Regression 576
15 Multiple Regression Model Building 628
16 Time-Series Forecasting 664
17 Statistical Applications in Quality Management 716
18 A Road Map for Analyzing Data 762
Online Chapter: 19 Decision Making
Appendices A–G 773
Self-Test Solutions and Answers to Selected Even-Numbered Problems 820
Index 850

xi
Contents
Preface xxiii                                            ORGANIZING DATA 29
2.2   Organizing Categorical Data 30
The Summary Table 30
The Contingency Table 30

1 Introduction                                            2    2.3   Organizing Numerical Data 33
Stacked and Unstacked Data 33
USING STATISTICS @ Good Tunes & More 3                                 The Ordered Array 34
The Frequency Distribution 35
1.1    Why Learn Statistics 4
The Relative Frequency Distribution and the Percentage
1.2    Statistics in Business 4                                        Distribution 37
1.3    Basic Vocabulary of Statistics 5                                The Cumulative Distribution 38
1.4    Identifying Type of Variables 7                         VISUALIZING DATA 41
Measurement Scales 7                                  2.4   Visualizing Categorical Data 41
1.5    Statistical Applications for Desktop                            The Bar Chart 42
Computing 10                                                    The Pie Chart 43
1.6    How to Use This Book 11                                         The Pareto Chart 44
Checklist for Getting Started 11                              The Side-by-Side Bar Chart 46
USING STATISTICS @ Good Tunes & More Revisited 13              2.5   Visualizing Numerical Data 49
SUMMARY 13                                                      The Stem-and-Leaf Display 49
KEY TERMS 13                                                    The Histogram 50
CHAPTER REVIEW PROBLEMS 13
The Percentage Polygon 51
The Cumulative Percentage Polygon (Ogive) 53
END-OF-CHAPTER CASES 15
LEARNING WITH THE DIGITAL CASES 15
2.6   Visualizing Two Numerical Variables 56
The Scatter Plot 56
REFERENCES 16
The Time-Series Plot 58
CHAPTER 1 EXCEL GUIDE 17
2.7   Organizing Multidimensional Data 60
EG1.1 Getting Started with Excel 17
Multidimensional Contingency Tables 60
EG1.2 Entering Data and Variable Type 18
EG1.3 Opening and Saving Workbooks 18
EG1.4 Creating and Copying Worksheets 19              2.8 Misuses and Common Errors in Visualizing Data 63
EG1.5 Printing Worksheets 19                          USING STATISTICS @ Choice Is Yours, Part I Revisited 66
EG1.6 Worksheet Entries and References 20                   SUMMARY 67
EG1.7 Absolute and Relative Cell References 21              KEY EQUATIONS 67
EG1.8 Entering Formulas into Worksheets 21                  KEY TERMS 68
EG1.9 Using Appendices D and F 21                           CHAPTER REVIEW PROBLEMS 68
CHAPTER 1 MINITAB GUIDE 22                                    MANAGING ASHLAND MULTICOMM SERVICES 74
MG1.1 Getting Started With Minitab 22                       DIGITAL CASE 75
MG1.2 Entering Data and Variable Type 22                    REFERENCES 75
MG1.3 Opening and Saving Worksheets and                     CHAPTER 2 EXCEL GUIDE 76
Projects 23                                                   EG2.2 Organizing Categorical Data 76
MG1.4 Creating and Copying Worksheets 24                      EG2.3 Organizing Numerical Data 78
MG1.5 Printing Parts of a Project 24                          EG2.4 Visualizing Categorical Data 80
MG1.6 Worksheet Entries and References 24                     EG2.5 Visualizing Numerical Data 82
MG1.7 Using Appendices D and F 25                             EG2.6 Visualizing Two Numerical Variables 84
EG2.7 Organizing Multidimensional Data 85
CHAPTER 2 MINITAB GUIDE 87
2 Organizing and Visualizing                                           MG2.2 Organizing Categorical Data 87
MG2.3 Organizing Numerical Data 87
Data                                                26           MG2.4 Visualizing Categorical Data 88
MG2.5 Visualizing Numerical Data 89
USING STATISTICS @ Choice Is Yours, Part I 27                          MG2.6 Visualizing Two Numerical Variables 92
2.1    Data Collection 28                                              MG2.7 Organizing Multidimensional Data 93
xiii
xiv     CONTENTS

3 Numerical Descriptive                                           4 Basic Probability                                        144
Measures                                               94   USING STATISTICS @ M&R Electronics World 145
4.1   Basic Probability Concepts 146
USING STATISTICS @ Choice Is Yours, Part II 95
Events and Sample Spaces 147
3.1   Central Tendency 96                                                 Contingency Tables and Venn Diagrams 148
The Mean 96
Simple Probability 149
The Median 98
Joint Probability 150
The Mode 99
Marginal Probability 150
The Geometric Mean 100
3.2   Variation and Shape 101
4.2   Conditional Probability 155
The Range 102
The Variance and the Standard Deviation 102                         Computing Conditional Probabilities 155
The Coefficient of Variation 106                                    Decision Trees 156
Z Scores 107                                                        Independence 158
Shape 108                                                           Multiplication Rules 159
VISUAL EXPLORATIONS: Exploring Descriptive                                Marginal Probability Using the General Multiplication
Statistics 110                                                        Rule 160
3.3   Exploring Numerical Data 113                                4.3   Bayes’ Theorem 163
The Interquartile Range 115                               4.4 Counting Rules 167
The Five-Number Summary 115                                     Counting Rule 1 167
The Boxplot 117                                                 Counting Rule 2 168
3.4   Numerical Descriptive Measures for a Population 120               Counting Rule 3 168
The Population Mean 121                                         Counting Rule 4 169
The Population Variance and Standard Deviation 121              Counting Rule 5 169
The Empirical Rule 122                                    4.5 Ethical Issues and Probability 171
The Chebyshev Rule 123
USING STATISTICS @ M&R Electronics World Revisited 172
3.5   The Covariance and the Coefficient of                             SUMMARY 172
Correlation 125
KEY EQUATIONS 172
The Covariance 125
KEY TERMS 173
The Coefficient of Correlation 127
CHAPTER REVIEW PROBLEMS 173
3.6 Descriptive Statistics: Pitfalls and Ethical Issues 131
DIGITAL CASE 175
USING STATISTICS @ Choice Is Yours, Part II Revisited 131
REFERENCES 176
SUMMARY 132
CHAPTER 4 EXCEL GUIDE 177
KEY EQUATIONS 132
EG4.1 Basic Probability Concepts 177
KEY TERMS 133                                                       EG4.2 Conditional Probability 177
CHAPTER REVIEW PROBLEMS 133                                         EG4.3 Bayes’ Theorem 177
MANAGING ASHLAND MULTICOMM SERVICES 138                             EG4.4 Counting Rules 178
DIGITAL CASE 138                                                  CHAPTER 4 MINITAB GUIDE 178
REFERENCES 138                                                      MG4.1 Basic Probability Concepts 178
CHAPTER 3 EXCEL GUIDE 139                                           MG4.2 Conditional Probability 178
EG3.1 Central Tendency 139                                        MG4.3 Bayes’ Theorem 178
EG3.2 Variation and Shape 139                                     MG4.4 Counting Rules 178
EG3.3 Exploring Numerical Data 140
EG3.4 Numerical Descriptive Measures for a
Population 140
EG3.5 The Covariance and the Coefficient of
Correlation 141
5 Discrete Probability
CHAPTER 3 MINITAB GUIDE 141                                       Distributions                                        180
MG3.1 Central Tendency 141
MG3.2 Variation and Shape 142                             USING STATISTICS @ Saxon Home Improvement 181
MG3.3 Exploring Numerical Data 142                        5.1   The Probability Distribution for a Discrete Random
MG3.4 Numerical Descriptive Measures for a                      Variable 182
Population 143                                                    Expected Value of a Discrete Random Variable 182
MG3.5 The Covariance and the Coefficient of                       Variance and Standard Deviation of a Discrete Random
Correlation 143                                                   Variable 183
CONTENTS        xv

5.2   Covariance and Its Application in Finance 185                USING STATISTICS @ OurCampus! Revisited 240
Covariance 185                                                   SUMMARY 240
Expected Value, Variance, and Standard Deviation of the          KEY EQUATIONS 241
Sum of Two Random Variables 187
KEY TERMS 241
Portfolio Expected Return and Portfolio Risk 187
CHAPTER REVIEW PROBLEMS 241
5.3   Binomial Distribution 190                                          MANAGING ASHLAND MULTICOMM SERVICES 244
5.4   Poisson Distribution 197                                           DIGITAL CASE 244
5.5   Hypergeometric Distribution 201                                    REFERENCES 244
5.6       Online Topic Using the Poisson Distribution                    CHAPTER 6 EXCEL GUIDE 245
to Approximate the Binomial Distribution 204                         EG6.1 Continuous Probability Distributions 245
USING STATISTICS @ Saxon Home Improvement                                  EG6.2 The Normal Distribution 245
Revisited 205                                                        EG6.3 Evaluating Normality 245
SUMMARY 205                                                          EG6.4 The Uniform Distribution 246
KEY EQUATIONS 205                                                    EG6.5 The Exponential Distribution 246
KEY TERMS 206                                                      CHAPTER 6 MINITAB GUIDE 246
CHAPTER REVIEW PROBLEMS 206                                          MG6.1 Continuous Probability Distributions 246
MANAGING ASHLAND MULTICOMM SERVICES 209
MG6.2 The Normal Distribution 246
MG6.3 Evaluating Normality 247
DIGITAL CASE 210
MG6.4 The Uniform Distribution 247
REFERENCES 210
MG6.5 The Exponential Distribution 247
CHAPTER 5 EXCEL GUIDE 211
EG5.1 The Probability Distribution for a Discrete Random
Variable 211
EG5.2 Covariance and Its Application in Finance 211
EG5.3 Binomial Distribution 212
7 Sampling and Sampling
EG5.4 Poisson Distribution 212                                   Distributions                                      248
EG5.5 Hypergeometric Distribution 213
USING STATISTICS @ Oxford Cereals 249
CHAPTER 5 MINITAB GUIDE 214
MG5.1 The Probability Distribution for a Discrete Random   7.1   Types of Sampling Methods 250
Variable 214                                                       Simple Random Samples 251
MG5.2 Covariance and Its Application in Finance 214                Systematic Samples 253
MG5.3 Binomial Distribution 214                                    Stratified Samples 253
MG5.4 Poisson Distribution 214                                     Cluster Samples 254
MG5.5 Hypergeometric Distribution 215                      7.2   Evaluating Survey Worthiness 255
Survey Error 255
Ethical Issues 256
6 The Normal Distribution                                          7.3
Problem 256
Sampling Distributions 258
and Other Continuous                                         7.4   Sampling Distribution of the Mean 258
The Unbiased Property of the Sample Mean 258
Distributions                                        216            Standard Error of the Mean 260
USING STATISTICS @ OurCampus! 217                                         Sampling from Normally Distributed Populations 261
Sampling from Non-Normally Distributed Populations—
6.1   Continuous Probability Distributions 218                            The Central Limit Theorem 264
6.2   The Normal Distribution 218                                  VISUAL EXPLORATIONS: Exploring Sampling Distributions 265
Computing Normal Probabilities 220                          7.5 Sampling Distribution of the Proportion 266
7.6     Online Topic: Sampling from Finite
VISUAL EXPLORATIONS: Exploring the Normal
Populations 269
Distribution 229
6.3   Evaluating Normality 230                                     USING STATISTICS @ Oxford Cereals Revisited 270
Comparing Data Characteristics to Theoretical                    SUMMARY 270
Properties 231                                                   KEY EQUATIONS 270
Constructing the Normal Probability Plot 232                     KEY TERMS 271
6.4   The Uniform Distribution 235                                       CHAPTER REVIEW PROBLEMS 271
6.5   The Exponential Distribution 237                                   MANAGING ASHLAND MULTICOMM SERVICES 273
6.6       Online Topic: The Normal Approximation to the                  DIGITAL CASE 273
Binomial Distribution 240                                          REFERENCES 274
xvi      CONTENTS

CHAPTER 7 EXCEL GUIDE 275                                           CHAPTER 8 MINITAB GUIDE 322
EG7.1 Types of Sampling Methods 275                                 MG8.1 Confidence Interval Estimate for the
EG7.2 Evaluating Survey Worthiness 275                              Mean (s Known) 322
EG7.3 Sampling Distributions 275                                    MG8.2 Confidence Interval Estimate for the
EG7.4 Sampling Distribution of the Mean 275                         Mean (s Unknown) 323
EG7.5 Sampling Distribution of the Proportion 276                   MG8.3 Confidence Interval Estimate for the Proportion 323
MG8.4 Determining Sample Size 323
CHAPTER 7 MINITAB GUIDE 276
MG8.5 Applications of Confidence Interval Estimation
MG7.1 Types of Sampling Methods 276                                 in Auditing 323
MG7.2 Evaluating Survey Worthiness 277
MG7.3 Sampling Distributions 277
MG7.4 Sampling Distribution of the Mean 277                 9 Fundamentals of Hypothesis
Testing: One-Sample Tests 324
8 Confidence Interval                                               USING STATISTICS @ Oxford Cereals, Part II 325
Estimation                                            278     9.1   Fundamentals of Hypothesis-Testing Methodology 326
The Null and Alternative Hypotheses 326
USING STATISTICS @ Saxon Home Improvement 279                               The Critical Value of the Test Statistic 327
8.1   Confidence Interval Estimate for the Mean                             Regions of Rejection and Nonrejection 328
(s Known) 280                                                         Risks in Decision Making Using Hypothesis Testing 328
Can You Ever Know the Population Standard                           Hypothesis Testing Using the Critical Value Approach 331
Deviation? 285                                                      Hypothesis Testing Using the p-Value Approach 333
8.2   Confidence Interval Estimate for the                                  A Connection Between Confidence Interval Estimation and
Mean (s Unknown) 286                                                  Hypothesis Testing 336
Student’s t Distribution 286                                        Can You Ever Know the Population Standard Deviation? 336
Properties of the t Distribution 287                        9.2   t Test of Hypothesis for the Mean (s Unknown) 338
The Concept of Degrees of Freedom 288                                The Critical Value Approach 338
The Confidence Interval Statement 288                                The p-Value Approach 340
8.3   Confidence Interval Estimate for the Proportion 294                    Checking the Normality Assumption 340
8.4   Determining Sample Size 297                                   9.3   One-Tail Tests 344
Sample Size Determination for the Mean 297                           The Critical Value Approach 345
Sample Size Determination for the Proportion 299                     The p-Value Approach 346
8.5   Applications of Confidence Interval Estimation in             9.4   Z Test of Hypothesis for the Proportion 349
Auditing 303                                                           The Critical Value Approach 350
Estimating the Population Total Amount 304                           The p-Value Approach 351
Difference Estimation 305                                   9.5   Potential Hypothesis-Testing Pitfalls and Ethical Issues 353
One-Sided Confidence Interval Estimation of the Rate of             Statistical Significance Versus Practical Significance 353
Noncompliance with Internal Controls 308                            Reporting of Findings 353
8.6 Confidence Interval Estimation and Ethical Issues 310                   Ethical Issues 354
8.7     Online Topic: Estimation and Sample Size                    9.6     Online Topic: The Power of a Test 354
Determination for Finite Populations 311
USING STATISTICS @ Oxford Cereals, Part II Revisited 354
USING STATISTICS @ Saxon Home Improvement                                 SUMMARY 355
Revisited 311
KEY EQUATIONS 355
SUMMARY 311
KEY TERMS 355
KEY EQUATIONS 312
CHAPTER REVIEW PROBLEMS 355
KEY TERMS 313
MANAGING ASHLAND MULTICOMM SERVICES 358
CHAPTER REVIEW PROBLEMS 313
DIGITAL CASE 358
MANAGING ASHLAND MULTICOMM SERVICES 317
REFERENCES 358
DIGITAL CASE 318
CHAPTER 9 EXCEL GUIDE 359
REFERENCES 318
EG9.1 Fundamentals of Hypothesis-Testing Methodology 359
CHAPTER 8 EXCEL GUIDE 319
EG9.2 t Test of Hypothesis for the Mean (s Unknown) 359
EG8.1 Confidence Interval Estimate for the                          EG9.3 One-Tail Tests 360
Mean (s Known) 319
EG9.4 Z Test of Hypothesis for the Proportion 361
EG8.2 Confidence Interval Estimate for the
Mean (s Unknown) 319                                              CHAPTER 9 MINITAB GUIDE 362
EG8.3 Confidence Interval Estimate for the Proportion 320           MG9.1 Fundamentals of Hypothesis-Testing Methodology 362
EG8.4 Determining Sample Size 320                                   MG9.2 t Test of Hypothesis for the Mean (s Unknown) 362
EG8.5 Applications of Confidence Interval Estimation in             MG9.3 One-Tail Tests 362
Auditing 321                                                        MG9.4 Z Test of Hypothesis for the Proportion 363
CONTENTS        xvii

10 Two-Sample Tests                                        364    11.2 The Randomized Block Design 430
Testing for Factor and Block Effects 430
Multiple Comparisons: The Tukey Procedure 436
USING STATISTICS @ BLK Beverages 365
11.3 The Factorial Design: Two-Way Analysis of Variance 438
10.1 Comparing the Means of Two Independent
Testing for Factor and Interaction Effects 439
Populations 366
Multiple Comparisons: The Tukey Procedure 444
Pooled-Variance t Test for the Difference Between Two
Means 366                                                          Visualizing Interaction Effects: The Cell Means Plot 445
Confidence Interval Estimate for the Difference Between            Interpreting Interaction Effects 446
Two Means 371                                               USING STATISTICS @ Perfect Parachutes Revisited 451
t Test for the Difference Between Two Means Assuming              SUMMARY 451
Unequal Variances 372
KEY EQUATIONS 451
KEY TERMS 453
10.2 Comparing the Means of Two Related Populations 377
CHAPTER REVIEW PROBLEMS 453
Paired t Test 378
MANAGING ASHLAND MULTICOMM SERVICES 457
Confidence Interval Estimate for the Mean
Difference 383                                                  DIGITAL CASE 458

10.3 Comparing the Proportions of Two Independent                       REFERENCES 458
Populations 385                                                    CHAPTER 11 EXCEL GUIDE 459
Z Test for the Difference Between Two Proportions 386             EG11.1 The Completely Randomized Design: One-Way
Confidence Interval Estimate for the Difference Between           Analysis of Variance 459
Two Proportions 390                                               EG11.2 The Randomized Block Design 461
10.4 F Test for the Ratio of Two Variances 392                            EG11.3 The Factorial Design: Two-Way Analysis of
Variance 462
USING STATISTICS @ BLK Beverages Revisited 397
CHAPTER 11 MINITAB GUIDE 464
SUMMARY 398                                                          MG11.1 The Completely Randomized Design: One-Way
KEY EQUATIONS 399                                                    Analysis of Variance 464
KEY TERMS 400                                                        MG11.2 The Randomized Block Design 465
CHAPTER REVIEW PROBLEMS 400                                          MG11.3 The Factorial Design: Two-Way Analysis of
MANAGING ASHLAND MULTICOMM SERVICES 404
Variance 465
DIGITAL CASE 405
REFERENCES 405
CHAPTER 10 EXCEL GUIDE 406
12 Chi-Square Tests and
EG10.1 Comparing the Means of Two Independent                    Nonparametric Tests                                 466
Populations 406
EG10.2 Comparing the Means of Two Related                 USING STATISTICS @ T.C. Resort Properties 467
Populations 408                                           12.1 Chi-Square Test for the Difference Between Two
EG10.3 Comparing the Proportions of Two Independent            Proportions 468
Populations 409                                           12.2 Chi-Square Test for Differences Among More Than Two
EG10.4 F Test for the Ratio of Two Variances 410               Proportions 475
CHAPTER 10 MINITAB GUIDE 411                                         The Marascuilo Procedure 478
MG10.1 Comparing the Means of Two Independent                         Online Topic: The Analysis of Proportions (ANOP) 480
Populations 411
12.3 Chi-Square Test of Independence 481
MG10.2 Comparing the Means of Two Related
Populations 411                                           12.4 McNemar Test for the Difference Between Two Proportions
MG10.3 Comparing the Proportions of Two Independent            (Related Samples) 487
Populations 412                                           12.5 Chi-Square Test for the Variance or Standard
MG10.4 F Test for the Ratio of Two Variances 412               Deviation 490
12.6 Wilcoxon Rank Sum Test: Nonparametric Analysis
for Two Independent Populations 494
11 Analysis of Variance                                    414    12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis
for the One-Way ANOVA 500
USING STATISTICS @ Perfect Parachutes 415
12.8      Online Topic: Wilcoxon Signed Ranks test:
11.1 The Completely Randomized Design: One-Way Analysis                Nonparametric Analysis for Two Related Populations 505
of Variance 416                                              12.9      Online Topic: Friedman Rank Test: Nonparametric
One-Way ANOVA F Test for Differences Among More Than           Analysis for the Randomized Block Design 506
Two Means 416
Multiple Comparisons: The Tukey-Kramer Procedure 422      USING STATISTICS @ T.C. Resort Properties Revisited 506
Online Topic: The Analysis of Means (ANOM) 424              SUMMARY 506
ANOVA Assumptions 424                                           KEY EQUATIONS 507
Levene Test for Homogeneity of Variance 425                     KEY TERMS 508
xviii     CONTENTS

CHAPTER REVIEW PROBLEMS 508                                         Confidence Interval Estimate for the Slope 550
MANAGING ASHLAND MULTICOMM SERVICES 511                             t Test for the Correlation Coefficient 551
DIGITAL CASE 512                                            13.8 Estimation of Mean Values and Prediction of Individual
REFERENCES 513                                                   Values 554
CHAPTER 12 EXCEL GUIDE 514                                         The Confidence Interval Estimate 554
EG12.1 Chi-Square Test for the Difference Between                The Prediction Interval 556
Two Proportions 514                                       13.9 Pitfalls in Regression 558
Two Proportions 514
USING STATISTICS @ Sunflowers Apparel
EG12.3 Chi-Square Test of Independence 515                     Revisited 561
EG12.4 McNemar Test for the Difference Between Two             SUMMARY 562
Proportions (Related Samples) 515
KEY EQUATIONS 563
EG12.5 Chi-Square Test for the Variance or Standard
Deviation 516                                                  KEY TERMS 564
EG12.6 Wilcoxon Rank Sum Test: Nonparametric Analysis          CHAPTER REVIEW PROBLEMS 564
for Two Independent Populations 516                            MANAGING ASHLAND MULTICOMM SERVICES 569
EG12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis        DIGITAL CASE 569
for the One-Way ANOVA 517                                      REFERENCES 570
CHAPTER 12 MINITAB GUIDE 518                                     CHAPTER 13 EXCEL GUIDE 571
MG12.1 Chi-Square Test for the Difference Between Two             EG13.1 Types of Regression Models 571
Proportions 518                                                   EG13.2 Determining the Simple Linear Regression
MG12.2 Chi-Square Test for Differences Among More                 Equation 571
Than Two Proportions 518                                          EG13.3 Measures of Variation 572
MG12.3 Chi-Square Test of Independence 518                        EG13.4 Assumptions 572
MG12.4 McNemar Test for the Difference Between Two                EG13.5 Residual Analysis 572
Proportions (Related Samples) 518                                 EG13.6 Measuring Autocorrelation: The Durbin-Watson
MG12.5 Chi-Square Test for the Variance or Standard               Statistic 572
Deviation 518                                                     EG13.7 Inferences About the Slope and Correlation
MG12.6 Wilcoxon Rank Sum Test: Nonparametric                      Coefficient 573
Analysis for Two Independent Populations 519                      EG13.8 Estimation of Mean Values and Prediction
EG12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis           of Individual Values 573
for the One-Way ANOVA 519                                      CHAPTER 13 MINITAB GUIDE 574
MG13.1 Types of Regression Models 574
MG13.2 Determining the Simple Linear Regression
13 Simple Linear                                                            Equation 574
MG13.3 Measures of Variation 574
Regression                                           520           MG13.4 Assumptions 574
MG13.5 Residual Analysis 574
USING STATISTICS @ Sunflowers Apparel 521
MG13.6 Measuring Autocorrelation: The Durbin-Watson
13.1 Types of Regression Models 522                                         Statistic 575
13.2 Determining the Simple Linear Regression Equation 524                  MG13.7 Inferences About the Slope and Correlation
The Least-Squares Method 525                                         Coefficient 575
Predictions in Regression Analysis: Interpolation Versus             MG13.8 Estimation of Mean Values and Prediction
Extrapolation 527                                                    of Individual Values 575
Computing the Y Intercept, b0 and the Slope, b1 528
VISUAL EXPLORATIONS: Exploring Simple Linear Regression
Coefficients 530                                               14 Introduction to Multiple
13.3 Measures of Variation 533
Computing the Sum of Squares 533
Regression                                           576
The Coefficient of Determination 534                      USING STATISTICS @ OmniFoods 577
Standard Error of the Estimate 536
14.1 Developing a Multiple Regression Model 578
13.4 Assumptions 538                                                       Visualizing Multiple Regression Data 578
13.5 Residual Analysis 539                                                 Interpreting the Regression Coefficients 578
Evaluating the Assumptions 539                                      Predicting the Dependent Variable Y 581
13.6 Measuring Autocorrelation: The Durbin-Watson                   14.2 r2, Adjusted r2, and the Overall F Test 584
Statistic 543                                                          Coefficient of Multiple Determination 584
Residual Plots to Detect Autocorrelation 543                      Adjusted r2 585
The Durbin-Watson Statistic 544                                   Test for the Significance of the Overall Multiple
13.7 Inferences About the Slope and Correlation Coefficient 547             Regression Model 585
t Test for the Slope 548                                     14.3 Residual Analysis for the Multiple Regression
F Test for the Slope 549                                          Model 588
CONTENTS          xix

14.4 Inferences Concerning the Population Regression              15.3 Collinearity 642
Coefficients 590                                             15.4 Model Building 644
Tests of Hypothesis 590                                             The Stepwise Regression Approach to Model Building 646
Confidence Interval Estimation 591                                  The Best-Subsets Approach to Model Building 647
14.5 Testing Portions of the Multiple Regression                           Model Validation 652
Model 593                                                    15.5 Pitfalls in Multiple Regression and Ethical Issues 653
Coefficients of Partial Determination 597                         Pitfalls in Multiple Regression 653
14.6 Using Dummy Variables and Interaction Terms in                      Ethical Issues 654
Regression Models 599                                        15.6 (     Online Topic) Influence Analysis 654
Dummy variables 599
Interactions 602                                           15.7 (     Online Topic) Analytics and Data Mining 654
14.7 Logistic Regression 609                                      USING STATISTICS @ WHIT-DT Revisited 654
SUMMARY 655
USING STATISTICS @ OmniFoods Revisited 614
KEY EQUATIONS 656
SUMMARY 614
KEY TERMS 656
KEY EQUATIONS 616
CHAPTER REVIEW PROBLEMS 656
KEY TERMS 617
THE MOUNTAIN STATES POTATO COMPANY 658
CHAPTER REVIEW PROBLEMS 617
DIGITAL CASE 659
MANAGING ASHLAND MULTICOMM SERVICES 620
REFERENCES 659
DIGITAL CASE 620
CHAPTER 15 EXCEL GUIDE 660
REFERENCES 621
EG15.1 The Quadratic Regression Model 660
CHAPTER 14 EXCEL GUIDE 622
EG15.2 Using Transformations in Regression Models 660
EG14.1 Developing a Multiple Regression Model 622
EG15.3 Collinearity 660
EG14.2 r2, Adjusted r2, and the Overall F Test 623
EG15.4 Model Building 660
EG14.3 Residual Analysis for the Multiple Regression
Model 623                                                         CHAPTER 15 MINITAB GUIDE 661
EG14.4 Inferences Concerning the Population Regression              MG15.1 The Quadratic Regression Model 661
Coefficients 624                                                    MG15.2 Using Transformations in Regression Models 662
EG14.5 Testing Portions of the Multiple Regression                  MG15.3 Collinearity 662
Model 624                                                           MG15.4 Model Building 662
EG14.6 Using Dummy Variables and Interaction Terms
in Regression Models 624
EG14.7 Logistic Regression 624
CHAPTER 14 MINITAB GUIDE 625
MG14.1 Developing a Multiple Regression Model 625
16 Time-Series Forecasting 664
MG14.2 r2, Adjusted r2, and the Overall F Test 626         USING STATISTICS @ The Principled 665
MG14.3 Residual Analysis for the Multiple Regression       16.1 The Importance of Business Forecasting 666
Model 626
16.2 Component Factors of Time-Series Models 666
MG14.4 Inferences Concerning the Population Regression
Coefficients 626                                           16.3 Smoothing an Annual Time Series 667
MG14.5 Testing Portions of the Multiple Regression                  Moving Averages 668
Model 626                                                           Exponential Smoothing 670
MG14.6 Using Dummy Variables and Interaction Terms         16.4 Least-Squares Trend Fitting and Forecasting 673
in Regression Models 626                                          The Linear Trend Model 673
MG14.7 Logistic Regression 627                                    The Quadratic Trend Model 675
The Exponential Trend Model 676
Model Selection Using First, Second, and Percentage
15 Multiple Regression Model                                               Differences 678
16.5 Autoregressive Modeling for Trend Fitting and
Building                                           628          Forecasting 684
16.6 Choosing an Appropriate Forecasting Model 692
USING STATISTICS @ WHIT-DT 629                                           Performing a Residual Analysis 693
15.1 The Quadratic Regression Model 630                                    Measuring the Magnitude of the Residuals Through Squared
Finding the Regression Coefficients and Predicting Y 630            or Absolute Differences 693
Testing for the Significance of the Quadratic Model 633             Using the Principle of Parsimony 694
Testing the Quadratic Effect 633                                    A Comparison of Four Forecasting Methods 694
The Coefficient of Multiple Determination 635              16.7 Time-Series Forecasting of Seasonal Data 696
15.2 Using Transformations in Regression Models 638                        Least-Squares Forecasting with Monthly or Quarterly Data 697
The Square-Root Transformation 638                          16.8       Online Topic: Index Numbers 703
xx      CONTENTS

USING STATISTICS @ The Principled Revisited 703                        MANAGING ASHLAND MULTICOMM SERVICES 753
SUMMARY 704                                                       REFERENCES 754
KEY EQUATIONS 704                                                 CHAPTER 17 EXCEL GUIDE 755
KEY TERMS 705                                                       EG17.1 The Theory of Control Charts 755
CHAPTER REVIEW PROBLEMS 706                                         EG17.2 Control Chart for the Proportion: The p Chart 755
MANAGING ASHLAND MULTICOMM SERVICES 707                             EG17.3 The Red Bead Experiment: Understanding Process
Variability 756
DIGITAL CASE 708
EG17.4 Control Chart for an Area of Opportunity: The c
REFERENCES 708                                                      Chart 756
CHAPTER 16 EXCEL GUIDE 709                                          EG17.5 Control Charts for the Range and the Mean 757
EG16.1 The Importance of Business Forecasting 709                 EG17.6 Process Capability 758
EG16.2 Component Factors of Time-Series Models 709                EG17.7 Total Quality Management 759
EG16.3 Smoothing an Annual Time Series 709                        EG17.8 Six Sigma 759
EG16.4 Least-Squares Trend Fitting and Forecasting 710          CHAPTER 17 MINITAB GUIDE 759
EG16.5 Autoregressive Modeling for Trend Fitting and              MG17.1 The Theory of Control Charts 759
Forecasting 711                                                   MG17.2 Control Chart for the Proportion:
EG16.6 Choosing an Appropriate Forecasting Model 711              The p Chart 759
EG16.7 Time-Series Forecasting of Seasonal Data 712               MG17.3 The Red Bead Experiment: Understanding
CHAPTER 16 MINITAB GUIDE 713                                        Process Variability 759
MG16.1 The Importance of Business Forecasting 713                 MG17.4 Control Chart for an Area of Opportunity: The c
MG16.2 Component Factors of Time-Series Models 713                Chart 756
MG16.3 Smoothing an Annual Time Series 713                        MG17.5 Control Charts for the Range and the Mean 760
MG16.4 Least-Squares Trend Fitting and Forecasting 713            MG17.6 Process Capability 761
MG16.5 Autoregressive Modeling for Trend Fitting and              MG17.7 Total Quality Management 761
Forecasting 714                                                   MG17.8 Six Sigma 761
MG16.6 Choosing an Appropriate Forecasting Model 714
MG16.7 Time-Series Forecasting of Seasonal Data 714

17 Statistical Applications in                                          Data                                                 762
Quality Management                                   716   USING STATISTICS @ YourBusiness 763
USING STATISTICS @ Beachcomber Hotel 717                          18.1 Analyzing Numerical Variables 765
How to Describe the Characteristics of a Numerical
17.1 The Theory of Control Charts 718                                    Variable 766
17.2 Control Chart for the Proportion: The p Chart 720                   How to Draw Conclusions About the Population Mean
17.3 The Red Bead Experiment: Understanding Process                      or Standard Deviation 766
Variability 726                                                     How to Determine Whether the Mean or Standard Deviation
Differs Depending on the Group 766
17.4 Control Chart for an Area of Opportunity: The c
Chart 728                                                           How to Determine Which Factors Affect the Value of
a Variable 767
17.5 Control Charts for the Range and the Mean 732                       How to Predict the Value of a Variable Based on the Value
The R Chart 732                                                   of Other Variables 767
The X Chart 734                                                   How to Determine Whether the Values of a Variable Are
17.6 Process Capability 737                                              Stable over Time 767
Customer Satisfaction and Specification Limits 737         18.2 Analyzing Categorical Variables 767
Capability Indices 739                                            How to Describe the Proportion of Items of Interest in Each
CPL, CPU, and Cpk 740                                             Category 768
17.7 Total Quality Management 742                                        How to Reach Conclusions About the Proportion of Items
of Interest 768
17.8 Six Sigma 744                                                       How to Determine Whether the Proportion of Items
The DMAIC Model 744                                               of Interest Differs Depending on the Group 768
Roles in a Six Sigma Organization 745                             How to Predict the Proportion of Items of Interest Based
USING STATISTICS @ Beachcomber Hotel Revisited 746                       on the Value of Other Variables 768
SUMMARY 747                                                         How to Determine Whether the Proportion of Items
of Interest Is Stable over Time 769
KEY EQUATIONS 747
KEY TERMS 748                                                USING STATISTICS @ YourBusiness Revisited 769
CHAPTER REVIEW PROBLEMS 748                                       DIGITAL CASE 769

THE HARNSWELL SEWING MACHINE COMPANY CASE 751                     CHAPTER REVIEW PROBLEMS 769
CONTENTS       xxi

Online Chapter:                                             D.3 Configuring Excel for PHStat2 Usage 793
D.4 Using the Visual Explorations Add-in
19 Decision Making                                                  Workbook 795
D.5 Checking for the Presence of the Analysis
USING STATISTICS @ Reliable Fund                                    ToolPak 795
19.1 Payoff Tables and Decision Trees                      E. Tables 796
19.2 Criteria for Decision Making                             E.1 Table of Random Numbers 796
Maximax Payoff                                         E.2 The Cumulative Standardized Normal
Maximin Payoff                                               Distribution 798
Expected Monetary Value
E.3 Critical Values of t 800
Expected Opportunity Loss
Return-to-Risk Ratio
E.4 Critical Values of x2 802
19.3 Decision Making with Sample Information                  E.5 Critical values of F 803
19.4 Utility                                                  E.6 Lower and Upper Critical Values, T1, of Wilcoxon
Rank Sum Test 807
E.7 Critical Values of the Studentized Range, Q 808
USING STATISTICS @ Reliable Fund Revisited
E.8 Critical Values dL and du of the Durbin-Watson
CHAPTER 19 EXCEL GUIDE
Statistic, D 810
EG19.1 Payoff Tables and Decision Trees
EG19.2 Criteria for Decision Making                    E.9 Control Chart Factors 811
E.10 The Standardized Normal Distribution 812
Appendices 773                                             F. Additional Excel Procedures 813
A. Basic Math Concepts and Symbols 774                        F.1 Enhancing Workbook Presentation 813
A.1 Rules for Arithmetic Operations 774                    F.2 Useful Keyboard Shortcuts 814
A.2 Rules for Algebra: Exponents and Square Roots 774      F.3 Verifying Formulas and Worksheets 815
A.3 Rules for Logarithms 775                               F.4 Chart Formatting 815
A.4 Summation Notation 776                                 F.5 Creating Histograms for Discrete Probability
Distributions 816
A.5 Statistical Symbols 779
F.6 Pasting with Paste Special 816
A.6 Greek Alphabet 779
G. PHStat2, Excel, and Minitab FAQs 818
B. Basic Computing Skills 780
G.1 PHStat2 FAQs 818
B.1 Objects in a Window 780
G.2 Excel FAQs 818
B.2 Basic Mouse Operations 781
G.3 FAQs for Minitab 819
B.3 Dialog Box Interactions 781
B.4 Unique Features 783
C. Companion Website Resources 784
C.1 Visiting the Companion Website for This Book 784    Self-Test Solutions and Answers to Selected
C.3 Accessing the Online Topics Files 784
D. Software Configuration Details 792
D.1 Checking for and Applying Excel Updates 792
D.2 Concise Instructions for Installing PHStat2 792
Preface
Educational Philosophy
Seeking ways to continuously improve the teaching of business statistics is the core value that
guides our works. We actively participate in Decision Sciences Institute (DSI), American Statistical
Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB)
conferences. We use the Guidelines for Assessment and Instruction (GAISE) reports as well as our
reflections on teaching business statistics to a diverse student body at several large universities.
These experiences have helped us identify the following key principles:

1. Show students the relevance of statistics Students need a frame of reference when learning
statistics, especially when statistics is not their major. That frame of reference for business
students should be the functional areas of business, such as accounting, finance, information
systems, management, and marketing. Each statistics topic needs to be presented in an
applied context related to at least one of these functional areas. The focus in teaching each
topic should be on its application in business, the interpretation of results, the evaluation of
the assumptions, and the discussion of what should be done if the assumptions are violated.
2. Familiarize students with the statistical applications used in the business world
Integrating these programs into all aspects of an introductory statistics course allows the
course to focus on interpretation of results instead of computations. Introductory business
statistics courses should recognize that programs with statistical functions are commonly
found on a business decision maker’s desktop computer, therefore making the interpretation
of results more important than the tedious hand calculations required to produce them.
3. Provide clear instructions to students for using statistical applications Books should
explain clearly how to use programs such as Excel and Minitab with the study of statistics,
without having those instructions dominate the book or distract from the learning of statisti-
cal concepts.
4. Give students ample practice in understanding how to apply statistics to business Both
classroom examples and homework exercises should involve actual or realistic data as much as
possible. Students should work with data sets, both small and large, and be encouraged to look
beyond the statistical analysis of data to the interpretation of results in a managerial context.

New to This Edition: MyStatLab
Custom MyStatLab course materials designed for specific use with this book are available.
MyStatLab is Pearson Education’s online learning, homework, and assessment tool that provides a
rich and flexible set of course materials, including free-response exercises that are algorithmically
generated for unlimited practice and mastery. MyStatLab provides students with a personalized,
interactive learning environment that helps them to independently improve their understanding and
performance in a course. MyStatLab allows instructors to deliver portions of a course online, to per-
form course management functions, and to create a supportive online community. In addition,
instructors can use the MyStatLab homework and test manager to select and assign their own online
exercises as well as import TestGen tests.
The MyStatLab for Basic Business Statistics features several improvements over earlier versions
including a more intuitive user design that presents a simpler interface with fewer pop-up windows.
This MyStatLab also provides mobile device access through free apps that can be downloaded for
Pearson eTexts, seeing their instructors annotations and gaining links to Do Homework, Take a Test,
and Study Plan functions.)

xxiii
xxiv   PREFACE

New to This Edition: Enhanced Statistical Coverage
This 12th edition of Basic Business Statistics builds on previous editions with these new and
enhanced features:
• New chapter-ending “Using Statistics ... Revisited” sections that reinforce the statistical
methods and applications discussed in each chapter.
• The use of the DCOVA (Define, Collect, Organize, Visualize, and Analyze) framework as an
integrated approach for applying statistics to help solve business problems.
• Many new applied examples and exercises, with data from The Wall Street Journal, USA
Today, and other sources.
• “Managing Ashland MultiComm Services,” a new integrated case that appears at the ends of
chapters throughout the book (replacing the Springville Herald case).
• “Digital Cases,” interactive PDF files that create a new series of cases that appear at the
ends of chapters throughout the book (replacing the Web Cases).
• An expanded discussion of using Excel and Minitab to summarize and explore multidimen-
sional data.
Desktop”) that provide greater insight into what has just been learned and raise important
issues about the application of statistical knowledge.
• Additional in-chapter Excel and Minitab results.
• A new online section that discusses analytics and data mining.

New to This Edition: Expanded Excel and Minitab Guides
In this 12th edition of Basic Business Statistics, the instructions for using Excel and Minitab have
been revised, reorganized, and enhanced in new end-of-chapter guides and back-of-the book appen-
dices. These sections support students by:
• Providing a readiness checklist and orientation that guide students through the process of get-
ting ready to use Excel or Minitab (see Chapter 1 and the Chapter 1 Excel and Minitab Guides).
• Incorporating Excel Guide workbooks that serve as models and templates for using Excel for
statistical problem solving. These free and reusable workbooks, annotated examples of which
appear throughout the chapters of this book, can be used by students in their other courses or
in their jobs.
• Allowing students to use Excel with or without PHStat2 and with or without the Analysis
ToolPak (an Excel component that is not available in Mac Excel 2008).
• Expanding the scope of Minitab Guide instructions.
• Reviewing common operations, such as opening, saving, and printing results (see Chapter 1
Excel and Minitab Guides).
• Explaining the different types of files available online that support this book and how to
• Providing a separate appendix that discusses software configuration issues, including how to
check for Excel and Minitab updates and how to configure Excel for use with PHStat2 or the
Analysis ToolPak (Appendix D).
• An appendix that discusses formatting and other intermediate-level Excel operations
(Appendix F).
for Microsoft Windows–based Excel versions, and Minitab (the new Appendix G).
• In Appendix Section C.4, offering a complete list of all downloadable files and programs for
this book. (See “Student Resources” on page xxvi for more details about the files and programs

Chapter-by-Chapter Changes in the 12th Edition
Chapters begin with a redesigned opening page that displays the chapter sections and subsections
and conclude with the new Excel and Minitab Guides that discuss how to apply Excel and Minitab
to the statistical methods discussed in a chapter. Minitab Guides have been expanded to better match
the scope of the Excel Guides. End-of-chapter Digital Cases that use interactive documents, in lieu
PREFACE       xxv

of simulated web pages, update the former Web Cases. There is a new integrated case, “Managing
Ashland MultiComm Services,” that replaces the “Managing the Springville Herald” case (see
Chapters 2, 3, 5 through 7, 9 through 14, 16, and 17). Appendices B through D and F and G have
been revised, reorganized, and updated.
Highlights of changes to individual chapters follow.
Chapter 1 The 11th edition’s Section 1.4 has been moved to Chapter 2. Section 1.6 has been
rewritten and retitled “How to Use This Book” and now includes the “Checklist for Getting
Started” (with Excel or Minitab). There are new undergraduate and graduate surveys.
Chapter 2 This chapter has been completely reorganized. Sections 1.4 of the previous edition,
concerning data collection, has been moved to this chapter. The Define, Collect, Organize,
Visualize, and Analyze approach to solving business problems has been incorporated. The
material on tables and charts has been reorganized so that the sections on organizing data into
tables is presented first, in Sections 2.2 and 2.3, followed by sections on visualizing data in
graphs in Sections 2.4–2.7. There is a new section on organizing multidimensional data
(Section 2.7). There are new Excel and Minitab Guide sections that discuss multidimensional
data. The Minitab Guide that replaces the Minitab Appendix has been greatly expanded. In
addition, there are new examples throughout the chapter, and a new data set on bond funds has
been created.
Chapter 3 A new data set on bond funds has been created. The section “Numerical Measures
for a Population” has been moved after the section on quartiles and boxplots. “Numerical
Descriptive Measures from a Population” has been deleted.
Chapter 4 The chapter example has been updated. There are new problems throughout the
updated. In combinations and permutations, x is used instead of X to be consistent with bino-
mial notation in Chapter 5.
Chapter 5 This chapter has revised notation for the binomial, Poisson, and hypergeometric
distributions. It uses lower-case x and includes the parameter after an | sign in the equa-
tion. To reduce the size of the book, the tables of the binomial and Poisson distributions
(Tables E.6 and E.7) have been placed online. There are new problems throughout the
chapter.
essay on the importance of the normal distribution has been revised. The discussion of the
exponential distribution has been revised.
Chapter 7 A new “Think About This” essay replaces and expands on the pros and cons of web-
based surveys, using a famous historical example. “Sampling from Finite Populations” is
now an online topic.
Chapter 8 This chapter includes problems on sigma known in Section 8.1.
Chapter 9 This chapter includes problems on sigma known in Section 9.1. “Power of a Test” is
now an online topic.
Chapter 10 This chapter has a new example on the paired t-test on textbook prices.
Chapter 11 This chapter has an “Online Topic” subsection titled “The Analysis of Means
(ANOM).”
Chapter 12 This chapter has an “Online Topic” subsection titled “The Analysis of Proportions
(ANOP).” The Wilcoxon signed ranks test and the Friedman test are now online topicss.
out the chapter.
Chapter 14 This chapter has various new problems.
Chapter 15 This chapter has a new “Online Topic” section titled “Analytics and Data Mining.”
There are new problems throughout the chapter.
Chapter 16 This chapter has updated examples throughout the chapter. “Index Numbers” is
now an online topic.
Chapter 17 This chapter has been edited for conciseness without any loss of concepts or clarity.
Chapter 18 This chapter now includes an interactive roadmap for analyzing data as part of a
new Digital Case. There are many new problems in the chapter.
Chapter 19 This chapter (formerly Chapter 17) has become an online chapter and is available
xxvi   PREFACE

Hallmark Features
We have continued many of the traditions of past editions and have highlighted some of these
features below.
Using Statistics Business Scenarios—Each chapter begins with a Using Statistics example that
shows how statistics is used in the functional areas of business—accounting, finance, infor-
mation systems, management, and marketing. Each scenario is used throughout the chapter
to provide an applied context for the concepts.
Emphasis on Data Analysis and Interpretation of Software Results—We believe that
the use of computer software is an integral part of learning statistics. Our focus empha-
sizes analyzing data by interpreting results while reducing emphasis on doing compu-
tations. For example, in the coverage of tables and charts in Chapter 2, the focus is on
the interpretation of various charts and on when to use each chart. In our coverage of
hypothesis testing in Chapters 9 through 12, and regression and time series forecasting in
Chapters 13–16, extensive computer results have been included so that the p-value
approach can be emphasized.
Pedagogical Aids—An active writing style is used, with boxed numbered equations, set-off
examples to provide reinforcement for learning concepts, problems divided into “Learning
the Basics” and “Applying the Concepts,” key equations, and key terms.
Answers—Many answers to the even-numbered exercises are included at the end of the book.
Flexibility Using Excel—For almost every statistical method discussed, this book presents
more than one way of using Excel. Students can use In-Depth Excel instructions to directly
work with the worksheet cell-level details or they can use the PHStat2 instructions or use the
Analysis ToolPak instructions to automate the creation of those same details.
Digital Cases—An end-of-chapter Digital Case is included for each of the first 16 chapters.
Most Digital Cases extend a Using Statistics business scenario by posing additional ques-
tions and raising issues about the scenario. Students examine interactive documents to sift
through claims and assorted information in order to discover the data most relevant to a sce-
nario. Students then determine whether the conclusions and claims are supported by the data.
In doing so, students discover and learn how to identify common misuses of statistical infor-
mation. (Instructional tips for using the Digital Cases and solutions to the Digital Cases are
included in the Instructor’s Solutions Manual.)
Case Studies and Team Projects—Detailed case studies are included in numerous chapters. A
“Managing Ashland MultiComm Services” continuing case, a team project related to bond
funds, and undergraduate and graduate student surveys are included at the end of most chap-
ters, and these serve to integrate learning across the chapters.
Visual Explorations—The Excel add-in workbook allows students to interactively explore
important statistical concepts in descriptive statistics, the normal distribution, sampling dis-
tributions, and regression analysis. For example, in descriptive statistics, students observe the
effect of changes in the data on the mean, median, quartiles, and standard deviation. With the
normal distribution, students see the effect of changes in the mean and standard deviation on
the areas under the normal curve. In sampling distributions, students use simulation to
explore the effect of sample size on a sampling distribution. In regression analysis, students
have the opportunity to fit a line and observe how changes in the slope and intercept affect
the goodness of fit.

Student Resources
Student Solutions Manual—Created by Professor Pin Tian Ng of Northern Arizona University,
this manual provides detailed solutions to virtually all the even-numbered exercises and
worked-out solutions to the self-test problems.
Companion website—This book comes with a companion website from which the following
details about these resources, including how to visit the companion website):
PREFACE      xxvii

• Data files Excel and Minitab data files used by in-chapter examples and problems (in .xls
and .mtw formats).
• Online Chapter The electronic-only Chapter 19: Decision Making in PDF format.
• Online Topics Online topics are PDF files that discuss additional topics for Chapters 5, 6, 7,
8, 9, 11, 12, 15, and 16.
• Excel Guide workbooks Self-documenting Excel Guide workbooks illustrate solutions for
more than 60 statistical topics that serve as freely reusable templates for future problem solv-
ing.
• Case files Supporting files are provided for the Digital Cases and the Managing Ashland
MultiComm Services Case.
• Visual Explorations The files needed to use the Visual Explorations Excel add-in work-
book.
• Using Excel 2003 Guide This guide presents, where necessary, alternate Excel Guide
instructions for users of this older version of Excel.
Excel, version 2003 and later. This version eliminates the use of the Excel Analysis ToolPak
add-ins, thereby simplifying installation and setup.

resources as well as the following exclusive online features and tools:
• Interactive tutorial exercises A comprehensive set of exercises have been written espe-
cially for use with this book that are algorithmically generated for unlimited practice and
mastery. Most exercises are free-response exercises and provide guided solutions, sample
problems, and learning aids for extra help at point of use.
• Personalized study plan A plan indicates which topics have been mastered and creates
direct links to tutorial exercises for topics that have not been mastered. MyStatLab manages
the study plan, updating its content based on the results of future online assessments.
• Pearson Tutor Center (www.pearsontutorservices.com) The MyStatlab student access
code grants access to this online resource, staffed by qualified instructors who provide book-
specific tutoring via phone, fax, e-mail, and interactive web sessions.
all their Pearson eTexts. The iPad app also allows access to the Do Homework, Take a Test,
and Study Plan pages of their MyStatLab course.
• Mobile Dashboard Allows students to use their mobile devices to log in and review informa-
tion from the dashboard of their courses: announcements, assignments, results, and progress
bars for completed work. This app is available for iPhones, iPads, and Android phones, and is
designed to promote effective study habits rather than to allow students to complete assign-
ments on their mobile devices.
@RISK trial Palisade Corporation, the maker of the market-leading risk and decision analysis
Excel add-ins, @RISK and the DecisionTools® Suite, provides special academic versions of its
software to students (and faculty). Its flagship product, @RISK, debuted in 1987 and performs risk
analysis using Monte Carlo simulation.
programs worldwide. Thanks to the company’s generous academic sales program, more than 40,000
students learn to make better decisions using @RISK and the DecisionTools Suite each year.

Instructor Resources
Instructor’s Resource Center—Reached through a link at www.pearsonhighered.com/levine,
the Instructor’s Resource Center contains the electronic files for the complete Instructor’s Solutions
Manual, the Test Item File, and PowerPoint lecture presentations.
• Register, redeem, log in At www.pearsonhighered.com/irc, instructors can access a vari-
ety of print, media, and presentation resources that are available with this book in downloadable
xxviii   PREFACE

digital format. Resources are also available for course management platforms such as
Blackboard, WebCT, and CourseCompass.
• Need help? Pearson Education’s dedicated technical support team is ready to assist instruc-
tors with questions about the media supplements that accompany this text. Visit http://
phone numbers. The supplements are available to adopting instructors. Detailed descriptions
are provided at the Instructor’s Resource Center.
Instructor’s Solutions Manual—Created by Professor Pin Tian Ng of Northern Arizona University,
this manual includes solutions for end-of-section and end-of-chapter problems, answers to case
questions, where applicable, and teaching tips for each chapter. Electronic solutions are provided in
PDF and Word formats.
Lecture PowerPoint Presentations—A PowerPoint presentation, created by Professor Patrick
Schur of Miami University, is available for each chapter. The PowerPoint slides provide an instruc-
tor with individual lecture outlines to accompany the text. The slides include many of the figures
and tables from the text. Instructors can use these lecture notes as is or can easily modify the notes
to reflect specific presentation needs.
Test Item File—Created by Professor Pin Tian Ng of Northern Arizona University, the Test Item
File contains true/false, multiple-choice, fill-in, and problem-solving questions based on the defini-
tions, concepts, and ideas developed in each chapter of the text.
TestGen—The computerized TestGen package allows instructors to customize, save, and generate
classroom tests. The test program permits instructors to edit, add, and delete questions from the test
bank; edit existing graphics and create new graphics; analyze test results; and organize a database of
test and student results. This software provides ease of use and extensive flexibility, and it provides
many options for organizing and displaying tests, along with search and sort features. The software
and the test banks can be downloaded from the Instructor’s Resource Center.
MathXL for Statistics—MathXL for Statistics is a powerful online homework, tutorial, and assess-
ment system that accompanies Pearson Education statistics textbooks. With MathXL for Statistics,
instructors can create, edit, and assign online homework and tests using algorithmically generated
exercises correlated at the objective level to the textbook. They can also create and assign their own
online exercises and import TestGen tests for added flexibility. All student work is tracked in
’s
MathXL online grade book. Students can take chapter tests in MathXL and receive personalized
study plans based on their test results. Each study plan diagnoses weaknesses and links the student
directly to tutorial exercises for the objectives he or she needs to study and retest. Students can also
access supplemental animations and video clips directly from selected exercises. MathXL for

MyStatLab—Part of the MyMathLab and MathXL product family, MyStatLab is a text-specific,
easily customizable online course that integrates interactive multimedia instruction with textbook
content. MyStatLab gives you the tools you need to deliver all or a portion of your course online,
whether your students are in a lab setting or working from home. The latest version of MyStatLab
offers a new, intuitive design that features more direct access to MathXL for Statistics pages
tionality for communicating with students and customizing courses. Other key features include:
• Assessment manager An easy-to-use assessment manager lets instructors create online
homework, quizzes, and tests that are automatically graded and correlated directly to your
textbook. Assignments can be created using a mix of questions from the MyStatLab exercise
bank, instructor-created custom exercises, and/or TestGen test items.
• Grade book Designed specifically for mathematics and statistics, the MyStatLab grade
book automatically tracks students’ results and gives you control over how to calculate final
• MathXL Exercise Builder You can use the MathXL Exercise Builder to create static and
algorithmic exercises for your online assignments. A library of sample exercises provides an
easy starting point for creating questions, and you can also create questions from scratch.
• eText-MathXL for Statistics Full Integration Students using appropriate mobile devices
PREFACE      xxix

a free app that allows them access to the Do Homework, Take a Test, and Study Plan pages of
their course.
tent team of any irregularities with specific questions by using the “Ask the Publisher” func-
• Tracking Time Spent on Media Because the latest version of MyStatLab requires students
to explicitly click a “Submit” button after viewing the media for their assignments, you will
be able to track how long students are spending on each media file.
decision analysis Excel add-ins, @RISK and the DecisionTools® Suite, provides special academic
versions of its software. Its flagship product, @RISK, debuted in 1987 and performs risk analysis
using Monte Carlo simulation. With an estimated 150,000 users, Palisade software can be found in
more than 100 countries and has been translated into five languages.
programs worldwide and can be bundled with this textbook. To download a trial version of @RISK

Acknowledgments
We are extremely grateful to the Biometrika Trustees, American Cyanamid Company, the RAND
Corporation, and the American Society for Testing and Materials for their kind permission to pub-
lish various tables in Appendix E, and the American Statistical Association for its permission to
publish diagrams from the American Statistician. Also, we are grateful to Professors George A.
Johnson and Joanne Tokle of Idaho State University and Ed Conn, Mountain States Potato
Company, for their kind permission to incorporate parts of their work as our Mountain States Potato
Company case in Chapter 15.

A Note of Thanks
We would like to thank Kevin Caskey, SUNY–New Paltz; Zhi Min Huang, Adelphi University;
David Huff, Wayne State University; Eugene Jones, Ohio State University; Glen Miller, Piedmont
College; Angela Mitchell, Wilmington College; Daniel Montgomery, Delta State University;
Patricia Mullins, University of Wisconsin–Madison; Robert Pred, Temple University; Gary Smith,
Florida State University; and Robert Wharton, Fordham University for their comments, which have
We would especially like to thank Chuck Synovec, Mary Kate Murray, Jason Calcano, Judy
Leale, Anne Fahlgren, Melinda Jensen, and Kerri Tomasso of the editorial, marketing, and produc-
tion teams at Prentice Hall. We would like to thank our statistical reader and accuracy checker Annie
Puciloski for her diligence in checking our work; Susan Pariseau, Merrimack College, for assisting
in the reading of the page proofs; Kitty Wilson for her copyediting; Lori Cavanaugh for her proof-
reading; and Jen Carley of PreMediaGlobal for her work in the production of this text.
Finally, we would like to thank our families for their patience, understanding, love, and assis-
tance in making this book a reality. It is to them that we dedicate this book.

Concluding Remarks
We have gone to great lengths to make this text both pedagogically sound and error free. If you have
any suggestions or require clarification about any of the material, or if you find any errors, please
contact us at davidlevine@davidlevinestatistics.com. Include the phrase “BBS edition 12” in the
subject line of your e-mail. For technical support for PHStat2 beyond what is presented in the
appendices and in the PHStat2 readme file that accompanies PHStat2, visit the PHStat2 website,
www.pearsonhighered.com/phstat and click on the Contact Pearson Technical Support link.

Mark L. Berenson
David M. Levine
Timothy C. Krehbiel