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Essentials of statistics for business and economics 5th anderson


Entries in this table
give the area under the
curve to the left of the
z value. For example, for
z = –.85, the cumulative
probability is .1977.































































































Entries in the table
give the area under the
curve to the left of the
z value. For example, for
z = 1.25, the cumulative
probability is .8944.



























































































David R. Anderson
University of Cincinnati

Dennis J. Sweeney
University of Cincinnati

Thomas A. Williams
Rochester Institute of Technology

Dedicated to
Marcia, Cherri, and Robbie

Essentials of Statistics for Business and Economics, Fifth Edition
David R. Anderson, Dennis J. Sweeney, Thomas A. Williams

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Brief Contents

Preface xii
About the Authors xvi
Chapter 1 Data and Statistics 1
Chapter 2 Descriptive Statistics: Tabular and Graphical
Presentations 26
Chapter 3 Descriptive Statistics: Numerical Measures 80
Chapter 4 Introduction to Probability 140
Chapter 5 Discrete Probability Distributions 185
Chapter 6 Continuous Probability Distributions 224
Chapter 7 Sampling and Sampling Distributions 256
Chapter 8 Interval Estimation 293
Chapter 9 Hypothesis Tests 332
Chapter 10 Comparisons Involving Means, Experimental Design,
and Analysis of Variance 377
Chapter 11 Comparisons Involving Proportions and a Test
of Independence 430
Chapter 12 Simple Linear Regression 464
Chapter 13 Multiple Regression 532
Appendix A References and Bibliography 580
Appendix B Tables 581
Appendix C Summation Notation 608
Appendix D Self-Test Solutions and Answers to Even-Numbered
Exercises 610
Appendix E Using Excel Functions 640
Appendix F Computing p-Values Using Minitab and Excel 645
Index 649


Preface xii
About the Authors xvi

Chapter 1

Data and Statistics 1

Statistics in Practice: BusinessWeek 2
1.1 Applications in Business and Economics 3
Accounting 3
Finance 4
Marketing 4
Production 4
Economics 4
1.2 Data 5
Elements, Variables, and Observations 6
Scales of Measurement 6
Qualitative and Quantitative Data 7
Cross-Sectional and Time Series Data 7
1.3 Data Sources 10
Existing Sources 10
Statistical Studies 11
Data Acquisition Errors 12
1.4 Descriptive Statistics 13
1.5 Statistical Inference 15
1.6 Computers and Statistical Analysis 17
Summary 17
Glossary 18
Supplementary Exercises 19

Chapter 2

Descriptive Statistics: Tabular and Graphical
Presentations 26

Statistics in Practice: Colgate-Palmolive Company 27
2.1 Summarizing Qualitative Data 28
Frequency Distribution 28
Relative Frequency and Percent Frequency Distributions 29
Bar Graphs and Pie Charts 29
2.2 Summarizing Quantitative Data 34
Frequency Distribution 34
Relative Frequency and Percent Frequency Distributions 35
Dot Plot 36
Histogram 36
Cumulative Distributions 37
Ogive 39



2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 43
2.4 Crosstabulations and Scatter Diagrams 48
Crosstabulation 48
Simpson’s Paradox 51
Scatter Diagram and Trendline 52
Summary 57
Glossary 59
Key Formulas 60
Supplementary Exercises 60
Case Problem 1: Pelican Stores 66
Case Problem 2: Motion Picture Industry 67
Appendix 2.1 Using Minitab for Tabular and Graphical Presentations 68
Appendix 2.2 Using Excel for Tabular and Graphical Presentations 70

Chapter 3

Descriptive Statistics: Numerical Measures 80

Statistics in Practice: Small Fry Design 81
3.1 Measures of Location 82
Mean 82
Median 83
Mode 84
Percentiles 85
Quartiles 86
3.2 Measures of Variability 90
Range 91
Interquartile Range 91
Variance 91
Standard Deviation 94
Coefficient of Variation 94
3.3 Measures of Distribution Shape, Relative Location, and Detecting
Outliers 97
Distribution Shape 97
z-Scores 98
Chebyshev’s Theorem 99
Empirical Rule 100
Detecting Outliers 101
3.4 Exploratory Data Analysis 104
Five-Number Summary 104
Box Plot 105
3.5 Measures of Association Between Two Variables 109
Covariance 109
Interpretation of the Covariance 111
Correlation Coefficient 113
Interpretation of the Correlation Coefficient 114
3.6 The Weighted Mean and Working with Grouped Data 118
Weighted Mean 118
Grouped Data 119
Summary 123
Glossary 124
Key Formulas 125
Supplementary Exercises 127
Case Problem 1: Pelican Stores 130



Case Problem 2: Motion Picture Industry 132
Case Problem 3: Business Schools of Asia-Pacific 132
Appendix 3.1 Descriptive Statistics Using Minitab 134
Appendix 3.2 Descriptive Statistics Using Excel 136

Chapter 4

Introduction to Probability 140

Statistics in Practice: Rohm and Hass Company 141
4.1 Experiments, Counting Rules, and Assigning Probabilities 142
Counting Rules, Combinations, and Permutations 143
Assigning Probabilities 147
Probabilities for the KP&L Project 149
4.2 Events and Their Probabilities 152
4.3 Some Basic Relationships of Probability 156
Complement of an Event 156
Addition Law 157
4.4 Conditional Probability 162
Independent Events 166
Multiplication Law 166
4.5 Bayes’ Theorem 170
Tabular Approach 174
Summary 176
Glossary 176
Key Formulas 177
Supplementary Exercises 178
Case Problem: Hamilton County Judges 182

Chapter 5

Discrete Probability Distributions 185

Statistics in Practice: Citibank 186
5.1 Random Variables 186
Discrete Random Variables 187
Continuous Random Variables 188
5.2 Discrete Probability Distributions 189
5.3 Expected Value and Variance 195
Expected Value 195
Variance 195
5.4 Binomial Probability Distribution 199
A Binomial Experiment 200
Martin Clothing Store Problem 201
Using Tables of Binomial Probabilities 205
Expected Value and Variance for the Binomial Distribution 206
5.5 Poisson Probability Distribution 210
An Example Involving Time Intervals 210
An Example Involving Length or Distance Intervals 212
5.6 Hypergeometric Probability Distribution 213
Summary 216
Glossary 217
Key Formulas 218
Supplementary Exercises 219
Appendix 5.1 Discrete Probability Distributions with Minitab 221
Appendix 5.2 Discrete Probability Distributions with Excel 222



Chapter 6

Continuous Probability Distributions 224

Statistics in Practice: Procter & Gamble 225
6.1 Uniform Probability Distribution 226
Area as a Measure of Probability 227
6.2 Normal Probability Distribution 230
Normal Curve 230
Standard Normal Probability Distribution 232
Computing Probabilities for Any Normal Probability Distribution 237
Grear Tire Company Problem 238
6.3 Normal Approximation of Binomial Probabilities 242
6.4 Exponential Probability Distribution 245
Computing Probabilities for the Exponential Distribution 246
Relationship Between the Poisson and Exponential Distributions 247
Summary 249
Glossary 249
Key Formulas 250
Supplementary Exercises 250
Case Problem: Specialty Toys 253
Appendix 6.1 Continuous Probability Distributions with Minitab 254
Appendix 6.2 Continuous Probability Distributions with Excel 255

Chapter 7

Sampling and Sampling Distributions 256

Statistics in Practice: Meadwestvaco Corporation 257
7.1 The Electronics Associates Sampling Problem 258
7.2 Selecting a Sample 259
Sampling from a Finite Population 259
Sampling from a Process 261
7.3 Point Estimation 263
Practical Advice 264
7.4 Introduction to Sampling _Distributions 266
7.5 Sampling Distribution
_ of x 269
Expected Value of x 269
Standard Deviation of x 270
Form of the Sampling Distribution
of x 271
Sampling Distribution of x for the EAI Problem_ 272
Practical Value of the Sampling Distribution of x 273
Relationship Between
the Sample Size and the Sampling
Distribution of x 274 _
7.6 Sampling Distribution
_ of p 278
Expected Value of p 279
Standard Deviation of p 279
Form of the Sampling Distribution of p 280 _
Practical Value of the Sampling Distribution of p 281
7.7 Other Sampling Methods 284
Stratified Random Sampling 284
Cluster Sampling 285
Systematic Sampling 285
Convenience Sampling 286
Judgment Sampling 286



Summary 287
Glossary 287
Key Formulas 288
Supplementary Exercises 288
Appendix 7.1 Random Sampling with Minitab 290
Appendix 7.2 Random Sampling with Excel 291

Chapter 8

Interval Estimation 293

Statistics in Practice: Food Lion 294
8.1 Population Mean: ␴ Known 295
Margin of Error and the Interval Estimate 295
Practical Advice 299
8.2 Population Mean: ␴ Unknown 301
Margin of Error and the Interval Estimate 302
Practical Advice 305
Using a Small Sample 305
Summary of Interval Estimation Procedures 307
8.3 Determining the Sample Size 310
8.4 Population Proportion 313
Determining the Sample Size 315
Summary 318
Glossary 319
Key Formulas 320
Supplementary Exercises 320
Case Problem 1: Young Professional Magazine 323
Case Problem 2: Gulf Real Estate Properties 324
Case Problem 3: Metropolitan Research, Inc. 326
Appendix 8.1 Interval Estimation with Minitab 326
Appendix 8.2 Interval Estimation Using Excel 328

Chapter 9

Hypothesis Tests 332

Statistics in Practice: John Morrell & Company 333
9.1 Developing Null and Alternative Hypotheses 334
Testing Research Hypotheses 334
Testing the Validity of a Claim 334
Testing in Decision-Making Situations 335
Summary of Forms for Null and Alternative Hypotheses 335
9.2 Type I and Type II Errors 336
9.3 Population Mean: ␴ Known 339
One-Tailed Tests 339
Two-Tailed Test 345
Summary and Practical Advice 348
Relationship Between Interval Estimation and Hypothesis Testing 349
9.4 Population Mean: ␴ Unknown 353
One-Tailed Tests 354
Two-Tailed Test 355
Summary and Practical Advice 356



9.5 Population Proportion 359
Summary 362
Summary 364
Glossary 365
Key Formulas 366
Supplementary Exercises 366
Case Problem 1: Quality Associates, Inc. 368
Case Problem 2: Unemployment Study 370
Appendix 9.1 Hypothesis Testing with Minitab 370
Appendix 9.2 Hypothesis Testing with Excel 372

Chapter 10

Comparisons Involving Means, Experimental Design,
and Analysis of Variance 377

Statistics in Practice: U.S. Food and Drug Administration 378
10.1 Inferences About the Difference Between Two Population Means:
␴1 and ␴2 Known 379
Interval Estimation of ␮1 Ϫ ␮2 379
Hypothesis Tests About ␮1 Ϫ ␮2 381
Practical Advice 383
10.2 Inferences About the Difference Between Two Population Means:
␴1 and ␴2 Unknown 386
Interval Estimation of ␮1 Ϫ ␮2 386
Hypothesis Tests About ␮1 Ϫ ␮2 387
Practical Advice 390
10.3 Inferences About the Difference Between Two Population Means:
Matched Samples 394
10.4 An Introduction to Experimental Design and Analysis of Variance 400
Data Collection 401
Assumptions for Analysis of Variance 402
Analysis of Variance: A Conceptual Overview 403
10.5 Analysis of Variance and the Completely Randomized Design 405
Between-Treatments Estimate of Population Variance 406
Within-Treatments Estimate of Population Variance 407
Comparing the Variance Estimates: The F Test 408
ANOVA Table 410
Computer Results for Analysis of Variance 411
Testing for the Equality of k Population Means: An Observational
Study 412
Summary 416
Glossary 416
Key Formulas 417
Supplementary Exercises 419
Case Problem 1: Par, Inc. 423
Case Problem 2: Wentworth Medical Center 423
Case Problem 3: Compensation for Sales Professionals 424
Appendix 10.1 Inferences About Two Populations Using Minitab 425
Appendix 10.2 Inferences About Two Populations Using Excel 427
Appendix 10.3 Analysis of Variance with Minitab 428
Appendix 10.4 Analysis of Variance with Excel 429



Chapter 11

Comparisons Involving Proportions and a Test
of Independence 430

Statistics in Practice: United Way 431
11.1 Inferences About the Difference Between Two Population Proportions 432
Interval Estimation of p1 Ϫ p2 432
Hypothesis Tests About p1 Ϫ p2 434
11.2 Hypothesis Test for Proportions of a Multinomial Population 438
11.3 Test of Independence 445
Summary 452
Glossary 453
Key Formulas 453
Supplementary Exercises 454
Case Problem: A Bipartisan Agenda for Change 459
Appendix 11.1 Inferences About Two Population Proportions Using Minitab 459
Appendix 11.2 Tests of Goodness of Fit and Independence Using Minitab 460
Appendix 11.3 Tests of Goodness of Fit and Independence Using Excel 461

Chapter 12

Simple Linear Regression 464

Statistics in Practice: Alliance Data Systems 465
12.1 Simple Linear Regression Model 466
Regression Model and Regression Equation 466
Estimated Regression Equation 467
12.2 Least Squares Method 469
12.3 Coefficient of Determination 480
Correlation Coefficient 483
12.4 Model Assumptions 487
12.5 Testing for Significance 489
Estimate of ␴ 2 489
t Test 490
Confidence Interval for ␤1 491
F Test 492
Some Cautions About the Interpretation of Significance Tests 494
12.6 Using the Estimated Regression Equation for Estimation and Prediction 498
Point Estimation 498
Interval Estimation 498
Confidence Interval for the Mean Value of y 499
Prediction Interval for an Individual Value of y 500
12.7 Computer Solution 504
12.8 Residual Analysis: Validating Model Assumptions 509
Residual Plot Against x 510
Residual Plot Against yˆ 512
Summary 515
Glossary 515
Key Formulas 516
Supplementary Exercises 518
Case Problem 1: Measuring Stock Market Risk 524
Case Problem 2: U.S. Department of Transportation 525
Case Problem 3: Alumni Giving 526
Case Problem 4: Major League Baseball Team Values 526
Appendix 12.1 Regression Analysis with Minitab 528
Appendix 12.2 Regression Analysis with Excel 529



Chapter 13

Multiple Regression 532

Statistics in Practice: International Paper 533
13.1 Multiple Regression Model 534
Regression Model and Regression Equation 534
Estimated Multiple Regression Equation 534
13.2 Least Squares Method 535
An Example: Butler Trucking Company 536
Note on Interpretation of Coefficients 538
13.3 Multiple Coefficient of Determination 544
13.4 Model Assumptions 547
13.5 Testing for Significance 548
F Test 548
t Test 551
Multicollinearity 552
13.6 Using the Estimated Regression Equation for Estimation and Prediction 555
13.7 Qualitative Independent Variables 558
An Example: Johnson Filtration, Inc. 558
Interpreting the Parameters 560
More Complex Qualitative Variables 562
Summary 566
Glossary 566
Key Formulas 567
Supplementary Exercises 568
Case Problem 1: Consumer Research, Inc. 573
Case Problem 2: Predicting Student Proficiency Test Scores 574
Case Problem 3: Alumni Giving 574
Case Problem 4: Predicting Winning Percentage for the NFL 576
Appendix 13.1 Multiple Regression with Minitab 577
Appendix 13.2 Multiple Regression with Excel 577

Appendix A

References and Bibliography 580

Appendix B

Tables 581

Appendix C

Summation Notation 608

Appendix D

Self-Test Solutions and Answers to Even-Numbered
Exercises 610

Appendix E

Using Excel Functions 640

Appendix F

Computing p-Values Using Minitab and Excel 645

Index 649


to give students, primarily those in the fields of business administration and economics,
a conceptual introduction to the field of statistics and its many applications. The text is
applications-oriented and written with the needs of the nonmathematician in mind; the mathematical prerequisite is knowledge of algebra.
Applications of data analysis and statistical methodology are an integral part of the organization and presentation of the text material. The discussion and development of each
technique is presented in an application setting, with the statistical results providing insights
to decisions and solutions to problems.
Although the book is applications-oriented, we have taken care to provide sound
methodological development and to use notation that is generally accepted for the topic being covered. Hence, students will find that this text provides good preparation for the study
of more advanced statistical material. A bibliography to guide further study is included as
an appendix.
The text introduces the student to the statistical software packages of Minitab® 15 and
Microsoft® Office Excel® 2007 and emphasizes the role of computer software in the application of statistical analysis. Minitab is illustrated as it is one of the leading statistical software packages for both education and statistical practice. Excel is not a statistical software
package, but the wide availability and use of Excel makes it important for students to understand the statistical capabilities of this package. Minitab and Excel procedures are provided in appendices so that instructors have the flexibility of using as much computer
emphasis as desired for the course.

Changes in the Fifth Edition
We appreciate the acceptance and positive response to the previous editions of ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS. Accordingly, in making modifications for this new edition, we have maintained the presentation style and readability of
those editions. The significant changes in the new edition are summarized here.

Content Revisions
The following list summarizes selected content revisions for the new edition.

• p-Values. In the previous edition, we emphasized the use of p-values as the preferred
approach to hypothesis testing. We continue this approach in the new edition. However, we have eased the introduction to p-values by simplifying the conceptual
definition for the student. We now say, “A p-value is a probability that provides a
measure of the evidence against the null hypothesis provided by the sample. The
smaller the p-value, the more evidence there is against H0.” After this conceptual
definition, we provide operational definitions that make it clear how the p-value is
computed for a lower tail test, an upper tail test, and a two-tail test. Based on our
experience, we have found that separating the conceptual definition from the operational definitions is helpful to the novice student trying to digest difficult new



• Minitab and Excel Procedures for Computing p-Values. New to this edition is

an appendix showing how Minitab and Excel can be used to compute p-values associated with z, t, ␹2, and F test statistics. Students who use hand calculations to
compute the value of test statistics will be shown how statistical tables can be used
to provide a range for the p-value. Appendix F provides a means for these students
to compute the exact p-value using Minitab or Excel. This appendix will be helpful
for the coverage of hypothesis testing in Chapters 9 through 13.
Cumulative Standard Normal Distribution Table. It may be a surprise to many
of our users, but in the new edition we use the cumulative standard normal distribution table. We are making this change because of what we believe is the growing
trend for more and more students and practitioners alike to use statistics in an environment that emphasizes modern computer software. Historically, a table was used
by everyone because a table was the only source of information about the normal
distribution. However, many of today’s students are ready and willing to learn about
the use of computer software in statistics. Students will find that virtually every
computer software package uses the cumulative standard normal distribution. Thus,
it is becoming more and more important for introductory statistical texts to use a
normal probability table that is consistent with what the student will see when working with statistical software. It is no longer desirable to use one form of the standard
normal distribution table in the text and then use a different type of standard normal
distribution calculation when using a software package. Those who are using the cumulative normal distribution table for the first time will find that, in general, it eases
the normal probability calculations. In particular, a cumulative normal probability
table makes it easier to compute p-values for hypothesis testing.
Other Content Revisions. The following additional content revisions appear in the
new edition.
• Statistical routines covered in the chapter-ending appendices feature Minitab 15
and Excel 2007 procedures.
• New examples of time series data are provided in Chapter 1.
• The Excel appendix to Chapter 2 now provides more complete instructions on
how to develop a frequency distribution and a histogram for quantitative data.
• The introduction of sampling in Chapter 7 covers simple random sampling from
finite populations and random sampling from a process.
• Revised guidelines on the sample size necessary to use the t distribution now provide a consistency for the use of the t distribution in Chapters 8, 9, and 10.
• Step-by-step summary boxes for computing p-values for one-tailed and twotailed hypothesis tests are included in Chapter 9.
• Sections 10.4 and 10.5 have been revised to include an introduction to experimental design concepts. We show how analysis of variance (ANOVA) can be
used to analyze data from a completely randomized design as well as continue
to show how ANOVA can be used for the comparison of k means in an observational study.
• The Solutions Manual now shows the exercise solution steps using the cumulative normal distribution and more details in the explanations about how to compute p-values for hypothesis testing.

New Examples and Exercises Based on Real Data
We have added approximately 150 new examples and exercises based on real data and recent reference sources of statistical information. Using data pulled from sources also used
by the Wall Street Journal, USA Today, Fortune, Barron’s, and a variety of other sources,
we have drawn actual studies to develop explanations and to create exercises that demonstrate
many uses of statistics in business and economics. We believe that the use of real data helps



generate more student interest in the material and enables the student to learn about both
the statistical methodology and its application. The fifth edition of the text contains
approximately 300 examples and exercises based on real data.

New Case Problems
We have added five new case problems to this edition, bringing the total number of case
problems in the text to twenty-three. The new case problems appear in the chapters on descriptive statistics, interval estimation, and regression. These case problems provide students with the opportunity to analyze somewhat larger data sets and prepare managerial
reports based on the results of the analysis.

Features and Pedagogy
We have continued many of the features that appeared in previous editions. Some of the important ones are noted here.

Statistics in Practice
Each chapter begins with a Statistics in Practice article that describes an application of the
statistical methodology to be covered in the chapter. New to this edition are Statistics in
Practice articles for Rohm and Hass Company in Chapter 4 and the U.S. Food and Drug
Administration in Chapter 10.

Methods Exercises and Applications Exercises
The end-of-section exercises are split into two parts, Methods and Applications. The Methods exercises require students to use the formulas and make the necessary computations.
The Applications exercises require students to use the chapter material in real-world situations. Thus, students first focus on the computational “nuts and bolts,” then move on to the
subtleties of statistical application and interpretation.

Self-Test Exercises
Certain exercises are identified as self-test exercises. Completely worked-out solutions for
those exercises are provided in Appendix D at the back of the book. Students can attempt
the self-test exercises and immediately check the solution to evaluate their understanding
of the concepts presented in the chapter.

Margin Annotations and Notes and Comments
Margin annotations that highlight key points and provide additional insights for the student
are a key feature of this text. These annotations are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text.
At the end of many sections, we provide Notes and Comments designed to give the student additional insights about the statistical methodology and its application. Notes and
Comments include warnings about or limitations of the methodology, recommendations for
application, brief descriptions of additional technical considerations, and other matters.

Minitab and Excel® Appendices
Optional Minitab and Excel appendices appear at the end of most chapters. These appendices provide step-by-step instructions that make it easy for students to use Minitab or Excel



to conduct the statistical analysis presented in the chapter. The appendices in this edition
provide instructions for twenty-eight statistical routines and feature Minitab 15 and Excel
2007 procedures.

Data Sets Accompany the Text
Over 160 data sets are now available on the CD-ROM that is packaged with the text. The
data sets are available in both Minitab and Excel formats. Data set logos are used in the text
to identify the data sets that are available on the CD. Data sets for all case problems as well
as data sets for larger exercises are also included on the CD.

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are available in both Minitab and Excel formats. Data sets for all case problems, as well as
data sets for larger exercises, are included.

A special thanks goes to our associates from business and industry who supplied the Statistics in Practice features. We recognize them individually by a credit line in each of the
articles. Finally, we are also indebted to our senior acquisitions editor Charles McCormick,
Jr., our senior developmental editor Alice Denny and developmental editor Maggie
Kubale, our content project managers Patrick Cosgrove and Amy Hackett, our senior marketing manager Larry Qualls, our technology project manager John Rich, and others at
Thomson/South-Western for their editorial counsel and support during the preparation of
this text.
David R. Anderson
Dennis J. Sweeney
Thomas A. Williams

About the Authors

David R. Anderson. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North
Dakota, he earned his B.S., M.S., and Ph.D. degrees from Purdue University. Professor
Anderson has served as Head of the Department of Quantitative Analysis and Operations
Management and as Associate Dean of the College of Business Administration. In addition, he
was the coordinator of the College’s first Executive Program.
At the University of Cincinnati, Professor Anderson has taught introductory statistics
for business students as well as graduate-level courses in regression analysis, multivariate
analysis, and management science. He has also taught statistical courses at the Department
of Labor in Washington, D.C. He has been honored with nominations and awards for
excellence in teaching and excellence in service to student organizations.
Professor Anderson has coauthored ten textbooks in the areas of statistics, management
science, linear programming, and production and operations management. He is an active
consultant in the field of sampling and statistical methods.
Dennis J. Sweeney. Dennis J. Sweeney is Professor of Quantitative Analysis and Founder
of the Center for Productivity Improvement at the University of Cincinnati. Born in Des
Moines, Iowa, he earned a B.S.B.A. degree from Drake University and his M.B.A. and
D.B.A. degrees from Indiana University, where he was an NDEA Fellow. During 1978–79,
Professor Sweeney worked in the management science group at Procter & Gamble; during
1981–82, he was a visiting professor at Duke University. Professor Sweeney served as Head
of the Department of Quantitative Analysis and as Associate Dean of the College of
Business Administration at the University of Cincinnati.
Professor Sweeney has published more than thirty articles and monographs in the area
of management science and statistics. The National Science Foundation, IBM, Procter &
Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded
his research, which has been published in Management Science, Operations Research,
Mathematical Programming, Decision Sciences, and other journals.
Professor Sweeney has coauthored ten textbooks in the areas of statistics, management
science, linear programming, and production and operations management.
Thomas A. Williams. Thomas A. Williams is Professor of Management Science in the
College of Business at Rochester Institute of Technology. Born in Elmira, New York, he
earned his B.S. degree at Clarkson University. He did his graduate work at Rensselaer
Polytechnic Institute, where he received his M.S. and Ph.D. degrees.
Before joining the College of Business at RIT, Professor Williams served for seven
years as a faculty member in the College of Business Administration at the University of
Cincinnati, where he developed the undergraduate program in Information Systems and
then served as its coordinator. At RIT he was the first chairman of the Decision Sciences
Department. He teaches courses in management science and statistics, as well as graduate
courses in regression and decision analysis.
Professor Williams is the coauthor of eleven textbooks in the areas of management
science, statistics, production and operations management, and mathematics. He has been
a consultant for numerous Fortune 500 companies and has worked on projects ranging from
the use of data analysis to the development of large-scale regression models.

Data and Statistics

Scales of Measurement
Qualitative and Quantitative Data
Cross-Sectional and Time
Series Data



Elements, Variables, and


Existing Sources
Statistical Studies
Data Acquisition Errors









Chapter 1


Data and Statistics



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contained a feature article about business travel. In
addition, the weekly BusinessWeek Investor provides
statistics about the state of the economy, including production indexes, stock prices, mutual funds, and interest
BusinessWeek also uses statistics and statistical information in managing its own business. For example,
an annual survey of subscribers helps the company learn
about subscriber demographics, reading habits, likely
purchases, lifestyles, and so on. BusinessWeek managers
use statistical summaries from the survey to provide
*The authors are indebted to Charlene Trentham, Research Manager at
BusinessWeek, for providing this Statistics in Practice.

BusinessWeek uses statistical facts and summaries
in many of its articles. © Terri Miller/E-Visual
Communications, Inc.
better services to subscribers and advertisers. One recent
North American subscriber survey indicated that 90% of
BusinessWeek subscribers use a personal computer at
home and that 64% of BusinessWeek subscribers are
involved with computer purchases at work. Such statistics alert BusinessWeek managers to subscriber interest
in articles about new developments in computers. The
results of the survey are also made available to potential
advertisers. The high percentage of subscribers using
personal computers at home and the high percentage of
subscribers involved with computer purchases at work
would be an incentive for a computer manufacturer to
consider advertising in BusinessWeek.
In this chapter, we discuss the types of data available
for statistical analysis and describe how the data are obtained. We introduce descriptive statistics and statistical
inference as ways of converting data into meaningful
and easily interpreted statistical information.

Frequently, we see the following types of statements in newspapers and magazines:

• The National Association of Realtors reported that the median selling price for

a house in the United States was $222,600 (The Wall Street Journal, January 2,
The average cost of a 30-second television commercial during the 2006 Super Bowl
game was $2.5 million (USA Today, January 27, 2006).


Applications in Business and Economics


• A Jupiter Media survey found 31% of adult males watch television 10 or more hours
a week. For adult women it was 26% (The Wall Street Journal, January 26, 2004).

• General Motors, a leader in automotive cash rebates, provided an average cash
incentive of $4300 per vehicle (USA Today, January 27, 2006).

• More than 40% of Marriott International managers work their way up through the
ranks (Fortune, January 20, 2003).

• The New York Yankees have the highest payroll in major league baseball. In 2005, the

team payroll was $208,306,817, with a median of $5,833,334 per player (USA Today
Salary Database, February 2006).
The Dow Jones Industrial Average closed at 13,265 (Barron’s, May 5, 2007).

The numerical facts in the preceding statements ($222,600; $2.5 million; 31%; 26%;
$4300; 40%; $5,833,334; and 13,265) are called statistics. In this usage, the term statistics
refers to numerical facts such as averages, medians, percents, and index numbers that help
us understand a variety of business and economic conditions. However, as you will see, the
field, or subject, of statistics involves much more than numerical facts. In a broader sense,
statistics is defined as the art and science of collecting, analyzing, presenting, and interpreting data. Particularly in business and economics, the information provided by collecting, analyzing, presenting, and interpreting data gives managers and decision makers a
better understanding of the business and economic environment and thus enables them to
make more informed and better decisions. In this text, we emphasize the use of statistics
for business and economic decision making.
Chapter 1 begins with some illustrations of the applications of statistics in business and
economics. In Section 1.2 we define the term data and introduce the concept of a data set.
This section also introduces key terms such as variables and observations, discusses the
difference between quantitative and qualitative data, and illustrates the uses of crosssectional and time series data. Section 1.3 discusses how data can be obtained from existing sources or through surveys and experimental studies designed to obtain new data. The
important role that the Internet now plays in obtaining data is also highlighted. The uses of
data in developing descriptive statistics and in making statistical inferences are described
in Sections 1.4 and 1.5.


Applications in Business and Economics
In today’s global business and economic environment, anyone can access vast amounts of
statistical information. The most successful managers and decision makers understand the
information and know how to use it effectively. In this section, we provide examples that
illustrate some of the uses of statistics in business and economics.

Public accounting firms use statistical sampling procedures when conducting audits for
their clients. For instance, suppose an accounting firm wants to determine whether the
amount of accounts receivable shown on a client’s balance sheet fairly represents the actual amount of accounts receivable. Usually the large number of individual accounts receivable makes reviewing and validating every account too time-consuming and expensive.
As common practice in such situations, the audit staff selects a subset of the accounts
called a sample. After reviewing the accuracy of the sampled accounts, the auditors draw a
conclusion as to whether the accounts receivable amount shown on the client’s balance
sheet is acceptable.


Chapter 1

Data and Statistics

Financial analysts use a variety of statistical information to guide their investment recommendations. In the case of stocks, the analysts review a variety of financial data including
price/earnings ratios and dividend yields. By comparing the information for an individual
stock with information about the stock market averages, a financial analyst can begin to
draw a conclusion as to whether an individual stock is over- or underpriced. For example,
Barron’s (September 12, 2005) reported that the average price/earnings ratio for the 30 stocks
in the Dow Jones Industrial Average was 16.5. JPMorgan showed a price/earnings ratio of
11.8. In this case, the statistical information on price/earnings ratios indicated a lower price
in comparison to earnings for JPMorgan than the average for the Dow Jones stocks. Therefore, a financial analyst might conclude that JPMorgan was underpriced. This and other
information about JPMorgan would help the analyst make a buy, sell, or hold recommendation for the stock.

Electronic scanners at retail checkout counters collect data for a variety of marketing research applications. For example, data suppliers such as ACNielsen and Information Resources, Inc., purchase point-of-sale scanner data from grocery stores, process the data, and
then sell statistical summaries of the data to manufacturers. Manufacturers spend hundreds
of thousands of dollars per product category to obtain this type of scanner data. Manufacturers also purchase data and statistical summaries on promotional activities such as special pricing and the use of in-store displays. Brand managers can review the scanner
statistics and the promotional activity statistics to gain a better understanding of the relationship between promotional activities and sales. Such analyses often prove helpful in
establishing future marketing strategies for the various products.

Today’s emphasis on quality makes quality control an important application of statistics
in production. A variety of statistical quality control charts are used to monitor the output of a production process. In particular, an x-bar chart can be used to monitor the average
output. Suppose, for example, that a machine fills containers with 12 ounces of a soft drink.
Periodically, a production worker selects a sample of containers and computes the average
number of ounces in the sample. This average, or x-bar value, is plotted on an x-bar chart. A
plotted value above the chart’s upper control limit indicates overfilling, and a plotted value
below the chart’s lower control limit indicates underfilling. The process is termed “in control” and allowed to continue as long as the plotted x-bar values fall between the chart’s
upper and lower control limits. Properly interpreted, an x-bar chart can help determine when
adjustments are necessary to correct a production process.

Economists frequently provide forecasts about the future of the economy or some aspect of
it. They use a variety of statistical information in making such forecasts. For instance, in
forecasting inflation rates, economists use statistical information on such indicators as
the Producer Price Index, the unemployment rate, and manufacturing capacity utilization.
Often these statistical indicators are entered into computerized forecasting models that
predict inflation rates.




Applications of statistics such as those described in this section are an integral part of
this text. Such examples provide an overview of the breadth of statistical applications. To
supplement these examples, practitioners in the fields of business and economics provided
chapter-opening Statistics in Practice articles that introduce the material covered in each
chapter. The Statistics in Practice applications show the importance of statistics in a wide
variety of business and economic situations.


Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation. All the data collected in a particular study are referred to as the data set for the
study. Table 1.1 shows a data set containing information for 25 companies that are part of
the S&P 500. The S&P 500 is made up of 500 companies selected by Standard & Poor’s.
These companies account for 76% of the market capitalization of all U.S. stocks. S&P 500
stocks are closely followed by investors and Wall Street analysts.






Abbott Laboratories
Altria Group
Apollo Group
Bank of New York
Bristol-Myers Squibb
Cincinnati Financial
Federated Dept. Stores
International Paper
Manor Care
National Semiconductor
Novellus Systems
Pitney Bowes
Pulte Homes
SBC Communications
St. Paul Travelers
UnitedHealth Group
Wells Fargo









Source: BusinessWeek (April 4, 2005).



Chapter 1

Data and Statistics

Elements, Variables, and Observations
Elements are the entities on which data are collected. For the data set in Table 1.1, each individual company’s stock is an element; the element names appear in the first column. With
25 stocks, the data set contains 25 elements.
A variable is a characteristic of interest for the elements. The data set in Table 1.1
includes the following five variables:

• Exchange: Where the stock is traded— N (New York Stock Exchange) and


(Nasdaq National Market)
Ticker Symbol: The abbreviation used to identify the stock on the exchange
BusinessWeek Rank: A number from 1 to 500 that is a measure of company strength
Share Price ($): The closing price (February 28, 2005)
Earnings per Share ($): The earnings per share for the most recent 12 months

Measurements collected on each variable for every element in a study provide the data.
The set of measurements obtained for a particular element is called an observation. Referring to Table 1.1, we see that the set of measurements for the first observation (Abbott Laboratories) is N, ABT, 90, 46, and 2.02. The set of measurements for the second observation
(Altria Group) is N, MO, 148, 66, and 4.57, and so on. A data set with 25 elements contains
25 observations.

Scales of Measurement
Data collection requires one of the following scales of measurement: nominal, ordinal,
interval, or ratio. The scale of measurement determines the amount of information contained in the data and indicates the most appropriate data summarization and statistical
When the data for a variable consist of labels or names used to identify an attribute of
the element, the scale of measurement is considered a nominal scale. For example, referring to the data in Table 1.1, we see that the scale of measurement for the exchange variable
is nominal because N and NQ are labels used to identify where the company’s stock is traded.
In cases where the scale of measurement is nominal, a numeric code as well as nonnumeric
labels may be used. For example, to facilitate data collection and to prepare the data for
entry into a computer database, we might use a numeric code by letting 1 denote the New
York Stock Exchange and 2 denote the Nasdaq National Market. In this case the numeric
values 1 and 2 provide the labels used to identify where the stock is traded. The scale of measurement is nominal even though the data appear as numeric values.
The scale of measurement for a variable is called an ordinal scale if the data exhibit the properties of nominal data and the order or rank of the data is meaningful. For
example, Eastside Automotive sends customers a questionnaire designed to obtain data
on the quality of its automotive repair service. Each customer provides a repair service
rating of excellent, good, or poor. Because the data obtained are the labels—excellent,
good, or poor—the data have the properties of nominal data. In addition, the data can be
ranked, or ordered, with respect to the service quality. Data recorded as excellent indicate the best service, followed by good and then poor. Thus, the scale of measurement
is ordinal. Note that the ordinal data can also be recorded using a numeric code. For
example, the BusinessWeek rank for the data in Table 1.1 is ordinal data. It provides a rank
from 1 to 500 based on BusinessWeek’s assessment of the company’s strength.
The scale of measurement for a variable becomes an interval scale if the data show the
properties of ordinal data and the interval between values is expressed in terms of a fixed

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