Statistics for business and economics 11th edition 2011

Preface xxv About the Authors xxixChapter 1 Data and Statistics 1 Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations 31 Chapter 3 Descriptive Statistics: Numerical Me

z value For example, for

z = – 85, the cumulative probability is 1977.

z

z .00 01 02 03 04 05 06 07 08 09

.0 5000 5040 5080 5120 5160 5199 5239 5279 5319 5359 1 5398 5438 5478 5517 5557 5596 5636 5675 5714 5753 2 5793 5832 5871 5910 5948 5987 6026 6064 6103 6141 3 6179 6217 6255 6293 6331 6368 6406 6443 6480 6517 4 6554 6591 6628 6664 6700 6736 6772 6808 6844 6879 5 6915 6950 6985 7019 7054 7088 7123 7157 7190 7224 6 7257 7291 7324 7357 7389 7422 7454 7486 7517 7549 7 7580 7611 7642 7673 7704 7734 7764 7794 7823 7852 8 7881 7910 7939 7967 7995 8023 8051 8078 8106 8133 9 8159 8186 8212 8238 8264 8289 8315 8340 8365 8389 1.0 8413 8438 8461 8485 8508 8531 8554 8577 8599 8621 1.1 8643 8665 8686 8708 8729 8749 8770 8790 8810 8830 1.2 8849 8869 8888 8907 8925 8944 8962 8980 8997 9015 1.3 9032 9049 9066 9082 9099 9115 9131 9147 9162 9177 1.4 9192 9207 9222 9236 9251 9265 9279 9292 9306 9319 1.5 9332 9345 9357 9370 9382 9394 9406 9418 9429 9441 1.6 9452 9463 9474 9484 9495 9505 9515 9525 9535 9545 1.7 9554 9564 9573 9582 9591 9599 9608 9616 9625 9633 1.8 9641 9649 9656 9664 9671 9678 9686 9693 9699 9706 1.9 9713 9719 9726 9732 9738 9744 9750 9756 9761 9767 2.0 9772 9778 9783 9788 9793 9798 9803 9808 9812 9817 2.1 9821 9826 9830 9834 9838 9842 9846 9850 9854 9857 2.2 9861 9864 9868 9871 9875 9878 9881 9884 9887 9890 2.3 9893 9896 9898 9901 9904 9906 9909 9911 9913 9916 2.4 9918 9920 9922 9925 9927 9929 9931 9932 9934 9936 2.5 9938 9940 9941 9943 9945 9946 9948 9949 9951 9952 2.6 9953 9955 9956 9957 9959 9960 9961 9962 9963 9964 2.7 9965 9966 9967 9968 9969 9970 9971 9972 9973 9974 2.8 9974 9975 9976 9977 9977 9978 9979 9979 9980 9981 2.9 9981 9982 9982 9983 9984 9984 9985 9985 9986 9986 3.0 9987 9987 9987 9988 9988 9989 9989 9989 9990 9990

Cumulative probability 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.

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

David R Anderson University of Cincinnati

Dennis J Sweeney University of Cincinnati

Thomas A Williams Rochester Institute of Technology

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

STATISTICS FOR BUSINESS AND ECONOMICS 11e

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Preface xxv About the Authors xxix

Chapter 1 Data and Statistics 1

Chapter 2 Descriptive Statistics: Tabular and Graphical

Presentations 31

Chapter 3 Descriptive Statistics: Numerical Measures 85

Chapter 4 Introduction to Probability 148

Chapter 5 Discrete Probability Distributions 193

Chapter 6 Continuous Probability Distributions 232

Chapter 7 Sampling and Sampling Distributions 265

Chapter 8 Interval Estimation 308

Chapter 9 Hypothesis Tests 348

Chapter 10 Inference About Means and Proportions

with Two Populations 406

Chapter 11 Inferences About Population Variances 448

Chapter 12 Tests of Goodness of Fit and Independence 472

Chapter 13 Experimental Design and Analysis of Variance 506

Chapter 14 Simple Linear Regression 560

Chapter 15 Multiple Regression 642

Chapter 16 Regression Analysis: Model Building 712

Chapter 17 Index Numbers 763

Chapter 18 Time Series Analysis and Forecasting 784

Chapter 19 Nonparametric Methods 855

Chapter 20 Statistical Methods for Quality Control 903

Chapter 21 Decision Analysis 937

Chapter 22 Sample Survey On Website

Appendix A References and Bibliography 976

Appendix B Tables 978

Appendix C Summation Notation 1005

Appendix D Self-Test Solutions and Answers to Even-Numbered

Exercises 1007

Appendix E Using Excel Functions 1062

Appendix F Computing p-Values Using Minitab and Excel 1067

Index 1071

Preface xxv About the Authors xxix

Chapter 1 Data and Statistics 1

Statistics in Practice: BusinessWeek 2 1.1 Applications in Business and Economics 3

Accounting 3Finance 4Marketing 4Production 4Economics 4

1.4 Descriptive Statistics 13 1.5 Statistical Inference 15 1.6 Computers and Statistical Analysis 17 1.7 Data Mining 17

1.8 Ethical Guidelines for Statistical Practice 18 Summary 20

Glossary 20 Supplementary Exercises 21 Appendix: An Introduction to StatTools 28

Chapter 2 Descriptive Statistics: Tabular and Graphical

Presentations 31

Statistics in Practice: Colgate-Palmolive Company 32 2.1 Summarizing Categorical Data 33

Frequency Distribution 33Relative Frequency and Percent Frequency Distributions 34Bar Charts and Pie Charts 34

2.2 Summarizing Quantitative Data 39

Frequency Distribution 39Relative Frequency and Percent Frequency Distributions 41Dot Plot 41

Histogram 41Cumulative Distributions 43Ogive 44

2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 48 2.4 Crosstabulations and Scatter Diagrams 53

Crosstabulation 53Simpson’s Paradox 56Scatter Diagram and Trendline 57

Summary 63 Glossary 64 Key Formulas 65 Supplementary Exercises 65 Case Problem 1: Pelican Stores 71 Case Problem 2: Motion Picture Industry 72 Appendix 2.1 Using Minitab for Tabular and Graphical Presentations 73 Appendix 2.2 Using Excel for Tabular and Graphical Presentations 75 Appendix 2.3 Using StatTools for Tabular and Graphical Presentations 84

Chapter 3 Descriptive Statistics: Numerical Measures 85

Statistics in Practice: Small Fry Design 86 3.1 Measures of Location 87

Mean 87Median 88Mode 89Percentiles 90Quartiles 91

3.2 Measures of Variability 95

Range 96Interquartile Range 96Variance 97

Standard Deviation 99Coefficient of Variation 99

3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers 102

Distribution Shape 102

z-Scores 103Chebyshev’s Theorem 104Empirical Rule 105Detecting Outliers 106

3.4 Exploratory Data Analysis 109

Interpretation of the Correlation Coefficient 120

3.6 The Weighted Mean and Working with

Case Problem 1: Pelican Stores 137

Case Problem 2: Motion Picture Industry 138

Case Problem 3: Business Schools of Asia-Pacific 139

Case Problem 4: Heavenly Chocolates Website Transactions 139

Appendix 3.1 Descriptive Statistics Using Minitab 142

Appendix 3.2 Descriptive Statistics Using Excel 143

Appendix 3.3 Descriptive Statistics Using StatTools 146

Chapter 4 Introduction to Probability 148

Statistics in Practice: Oceanwide Seafood 149

4.1 Experiments, Counting Rules, and Assigning

Probabilities 150

Counting Rules, Combinations, and

Permutations 151

Assigning Probabilities 155

Probabilities for the KP&L Project 157

4.2 Events and Their Probabilities 160

4.3 Some Basic Relationships of Probability 164

Key Formulas 185 Supplementary Exercises 186 Case Problem: Hamilton County Judges 190

Chapter 5 Discrete Probability Distributions 193

Statistics in Practice: Citibank 194 5.1 Random Variables 194

Discrete Random Variables 195Continuous Random Variables 196

5.2 Discrete Probability Distributions 197 5.3 Expected Value and Variance 202

Expected Value 202Variance 203

5.4 Binomial Probability Distribution 207

A Binomial Experiment 208Martin Clothing Store Problem 209Using Tables of Binomial Probabilities 213Expected Value and Variance for the Binomial Distribution 214

5.5 Poisson Probability Distribution 218

An Example Involving Time Intervals 218

An Example Involving Length or Distance Intervals 220

5.6 Hypergeometric Probability Distribution 221 Summary 225

Glossary 225 Key Formulas 226 Supplementary Exercises 227 Appendix 5.1 Discrete Probability Distributions with Minitab 230 Appendix 5.2 Discrete Probability Distributions with Excel 230

Chapter 6 Continuous Probability Distributions 232

Statistics in Practice: Procter & Gamble 233 6.1 Uniform Probability Distribution 234

Area as a Measure of Probability 235

6.2 Normal Probability Distribution 238

Normal Curve 238Standard Normal Probability Distribution 240Computing Probabilities for Any Normal Probability Distribution 245Grear Tire Company Problem 246

6.3 Normal Approximation of Binomial Probabilities 250 6.4 Exponential Probability Distribution 253

Computing Probabilities for the Exponential Distribution 254Relationship Between the Poisson and Exponential Distributions 255

Summary 257

Glossary 258

Key Formulas 258

Supplementary Exercises 258

Case Problem: Specialty Toys 261

Appendix 6.1 Continuous Probability Distributions with Minitab 262

Appendix 6.2 Continuous Probability Distributions with Excel 263

Chapter 7 Sampling and Sampling Distributions 265

Statistics in Practice: MeadWestvaco Corporation 266

7.1 The Electronics Associates Sampling Problem 267

7.2 Selecting a Sample 268

Sampling from a Finite Population 268

Sampling from an Infinite Population 270

Form of the Sampling Distribution of x_ 281

Sampling Distribution of x_for the EAI Problem 283

Practical Value of the Sampling Distribution of x_ 283

Relationship Between the Sample Size and the Sampling

Distribution of x_ 285

7.6 Sampling Distribution of p_ 289

Expected Value of p_ 289

Standard Deviation of p_ 290

Form of the Sampling Distribution of p_ 291

Practical Value of the Sampling Distribution of p_ 291

7.7 Properties of Point Estimators 295

Unbiased 295

Efficiency 296

Consistency 297

7.8 Other Sampling Methods 297

Stratified Random Sampling 297

Statistics in Practice: Food Lion 309 8.1 Population Mean:␴ Known 310

Margin of Error and the Interval Estimate 310Practical Advice 314

8.2 Population Mean:␴ Unknown 316

Margin of Error and the Interval Estimate 317Practical Advice 320

Using a Small Sample 320Summary of Interval Estimation Procedures 322

8.3 Determining the Sample Size 325 8.4 Population Proportion 328

Determining the Sample Size 330

Summary 333 Glossary 334 Key Formulas 335 Supplementary Exercises 335 Case Problem 1: Young Professional Magazine 338 Case Problem 2: Gulf Real Estate Properties 339 Case Problem 3: Metropolitan Research, Inc 341 Appendix 8.1 Interval Estimation with Minitab 341 Appendix 8.2 Interval Estimation with Excel 343 Appendix 8.3 Interval Estimation with StatTools 346

Statistics in Practice: John Morrell & Company 349 9.1 Developing Null and Alternative Hypotheses 350

The Alternative Hypothesis as a Research Hypothesis 350The Null Hypothesis as an Assumption to Be Challenged 351Summary of Forms for Null and Alternative Hypotheses 352

9.2 Type I and Type II Errors 353 9.3 Population Mean:␴ Known 356

One-Tailed Test 356Two-Tailed Test 362Summary and Practical Advice 365

Relationship Between Interval Estimation and

9.6 Hypothesis Testing and Decision Making 381

9.7 Calculating the Probability of Type II Errors 382

9.8 Determining the Sample Size for a Hypothesis Test About

Case Problem 1: Quality Associates, Inc 396

Case Problem 2: Ethical Behavior of Business Students at

Bayview University 397

Appendix 9.1 Hypothesis Testing with Minitab 398

Appendix 9.2 Hypothesis Testing with Excel 400

Appendix 9.3 Hypothesis Testing with StatTools 404

Chapter 10 Inference About Means and Proportions

with Two Populations 406

Statistics in Practice: U.S Food and Drug Administration 407

10.1 Inferences About the Difference Between Two Population Means:

Glossary 436 Key Formulas 437 Supplementary Exercises 438 Case Problem: Par, Inc 441 Appendix 10.1 Inferences About Two Populations Using Minitab 442 Appendix 10.2 Inferences About Two Populations Using Excel 444 Appendix 10.3 Inferences About Two Populations Using StatTools 446

Chapter 11 Inferences About Population Variances 448

Statistics in Practice: U.S Government Accountability Office 449 11.1 Inferences About a Population Variance 450

Interval Estimation 450Hypothesis Testing 454

11.2 Inferences About Two Population Variances 460 Summary 466

Key Formulas 467 Supplementary Exercises 467 Case Problem: Air Force Training Program 469 Appendix 11.1 Population Variances with Minitab 470 Appendix 11.2 Population Variances with Excel 470 Appendix 11.3 Population Standard Deviation with StatTools 471

Chapter 12 Tests of Goodness of Fit and Independence 472

Statistics in Practice: United Way 473 12.1 Goodness of Fit Test: A Multinomial Population 474 12.2 Test of Independence 479

12.3 Goodness of Fit Test: Poisson and Normal Distributions 487

Poisson Distribution 487Normal Distribution 491

Summary 496 Glossary 497 Key Formulas 497 Supplementary Exercises 497 Case Problem: A Bipartisan Agenda for Change 501 Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab 502 Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel 503

Chapter 13 Experimental Design and Analysis of Variance 506

Statistics in Practice: Burke Marketing Services, Inc 507 13.1 An Introduction to Experimental Design and Analysis of Variance 508

Data Collection 509

Assumptions for Analysis of Variance 510

Analysis of Variance: A Conceptual Overview 510

13.2 Analysis of Variance and the Completely Randomized Design 513

Between-Treatments Estimate of Population Variance 514

Within-Treatments Estimate of Population Variance 515

Comparing the Variance Estimates: The F Test 516

ANOVA Table 518

Computer Results for Analysis of Variance 519

Testing for the Equality of k Population Means:An Observational Study 520

13.3 Multiple Comparison Procedures 524

Fisher’s LSD 524

Type I Error Rates 527

13.4 Randomized Block Design 530

Air Traffic Controller Stress Test 531

Case Problem 1: Wentworth Medical Center 552

Case Problem 2: Compensation for Sales Professionals 553

Appendix 13.1 Analysis of Variance with Minitab 554

Appendix 13.2 Analysis of Variance with Excel 555

Appendix 13.3 Analysis of Variance with StatTools 557

Chapter 14 Simple Linear Regression 560

Statistics in Practice: Alliance Data Systems 561

14.1 Simple Linear Regression Model 562

Regression Model and Regression Equation 562

Estimated Regression Equation 563

14.2 Least Squares Method 565

Confidence Interval for ␤1 587

FTest 588Some Cautions About the Interpretation of Significance Tests 590

14.6 Using the Estimated Regression Equation for Estimation and Prediction 594

Point Estimation 594Interval Estimation 594

Confidence Interval for the Mean Value of y 595 Prediction Interval for an Individual Value of y 596

14.7 Computer Solution 600 14.8 Residual Analysis: Validating Model Assumptions 605

Residual Plot Against x 606 Residual Plot Against yˆ 607

Standardized Residuals 607Normal Probability Plot 610

14.9 Residual Analysis: Outliers and Influential Observations 614

Detecting Outliers 614Detecting Influential Observations 616

Summary 621 Glossary 622 Key Formulas 623 Supplementary Exercises 625 Case Problem 1: Measuring Stock Market Risk 631 Case Problem 2: U.S Department of Transportation 632 Case Problem 3: Alumni Giving 633

Case Problem 4: PGA Tour Statistics 633 Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 635 Appendix 14.2 A Test for Significance Using Correlation 636

Appendix 14.3 Regression Analysis with Minitab 637 Appendix 14.4 Regression Analysis with Excel 638 Appendix 14.5 Regression Analysis with StatTools 640

Chapter 15 Multiple Regression 642

Statistics in Practice: dunnhumby 643 15.1 Multiple Regression Model 644

Regression Model and Regression Equation 644Estimated Multiple Regression Equation 644

15.2 Least Squares Method 645

An Example: Butler Trucking Company 646Note on Interpretation of Coefficients 648

15.3 Multiple Coefficient of Determination 654 15.4 Model Assumptions 657

15.5 Testing for Significance 658

15.7 Categorical Independent Variables 668

An Example: Johnson Filtration, Inc 668

Interpreting the Parameters 670

More Complex Categorical Variables 672

Logistic Regression Equation 684

Estimating the Logistic Regression Equation 685

Testing for Significance 687

Case Problem 1: Consumer Research, Inc 704

Case Problem 2: Alumni Giving 705

Case Problem 3: PGA Tour Statistics 705

Case Problem 4: Predicting Winning Percentage for the NFL 708

Appendix 15.1 Multiple Regression with Minitab 708

Appendix 15.2 Multiple Regression with Excel 709

Appendix 15.3 Logistic Regression with Minitab 710

Appendix 15.4 Multiple Regression with StatTools 711

Chapter 16 Regression Analysis: Model Building 712

Statistics in Practice: Monsanto Company 713

16.1 General Linear Model 714

Modeling Curvilinear Relationships 714

Interaction 718

Transformations Involving the Dependent Variable 720Nonlinear Models That Are Intrinsically Linear 724

16.2 Determining When to Add or Delete Variables 729

16.5 Multiple Regression Approach to Experimental Design 745 16.6 Autocorrelation and the Durbin-Watson Test 750

Summary 754 Glossary 754 Key Formulas 754 Supplementary Exercises 755 Case Problem 1: Analysis of PGA Tour Statistics 758 Case Problem 2: Fuel Economy for Cars 759

Appendix 16.1 Variable Selection Procedures with Minitab 760 Appendix 16.2 Variable Selection Procedures with StatTools 761

Statistics in Practice: U.S Department of Labor, Bureau of Labor Statistics 764

17.1 Price Relatives 765 17.2 Aggregate Price Indexes 765 17.3 Computing an Aggregate Price Index from Price Relatives 769

17.4 Some Important Price Indexes 771

Consumer Price Index 771Producer Price Index 771Dow Jones Averages 772

17.5 Deflating a Series by Price Indexes 773 17.6 Price Indexes: Other Considerations 777

Selection of Items 777Selection of a Base Period 777Quality Changes 777

17.7 Quantity Indexes 778 Summary 780

Glossary 780

Key Formulas 780

Supplementary Exercises 781

Chapter 18 Time Series Analysis and Forecasting 784

Statistics in Practice: Nevada Occupational Health Clinic 785

18.1 Time Series Patterns 786

Linear Trend Regression 807

Holt’s Linear Exponential Smoothing 812

Nonlinear Trend Regression 814

18.5 Seasonality and Trend 820

Seasonality Without Trend 820

Seasonality and Trend 823

Models Based on Monthly Data 825

18.6 Time Series Decomposition 829

Calculating the Seasonal Indexes 830

Deseasonalizing the Time Series 834

Using the Deseasonalized Time Series to Identify Trend 834

Case Problem 1: Forecasting Food and Beverage Sales 846

Case Problem 2: Forecasting Lost Sales 847

Appendix 18.1 Forecasting with Minitab 848

Appendix 18.2 Forecasting with Excel 851

Appendix 18.3 Forecasting with StatTools 852

Chapter 19 Nonparametric Methods 855

Statistics in Practice: West Shell Realtors 856 19.1 Sign Test 857

Hypothesis Test About a Population Median 857Hypothesis Test with Matched Samples 862

19.2 Wilcoxon Signed-Rank Test 865 19.3 Mann-Whitney-Wilcoxon Test 871 19.4 Kruskal-Wallis Test 882

19.5 Rank Correlation 887 Summary 891

Glossary 892 Key Formulas 893 Supplementary Exercises 893 Appendix 19.1 Nonparametric Methods with Minitab 896 Appendix 19.2 Nonparametric Methods with Excel 899 Appendix 19.3 Nonparametric Methods with StatTools 901Chapter 20 Statistical Methods for Quality Control 903

Statistics in Practice: Dow Chemical Company 904 20.1 Philosophies and Frameworks 905

Malcolm Baldrige National Quality Award 906ISO 9000 906

Six Sigma 906

20.2 Statistical Process Control 908

Control Charts 909

x_Chart: Process Mean and Standard Deviation Known 910

x_Chart: Process Mean and Standard Deviation Unknown 912

RChart 915

pChart 917

npChart 919Interpretation of Control Charts 920

20.3 Acceptance Sampling 922

KALI, Inc.: An Example of Acceptance Sampling 924Computing the Probability of Accepting a Lot 924Selecting an Acceptance Sampling Plan 928Multiple Sampling Plans 930

Summary 931 Glossary 931 Key Formulas 932 Supplementary Exercises 933 Appendix 20.1 Control Charts with Minitab 935 Appendix 20.2 Control Charts with StatTools 935

Chapter 21 Decision Analysis 937

Statistics in Practice: Ohio Edison Company 938

21.1 Problem Formulation 939

Payoff Tables 940

Decision Trees 940

21.2 Decision Making with Probabilities 941

Expected Value Approach 941

Expected Value of Perfect Information 943

21.3 Decision Analysis with Sample Information 949

Decision Tree 950

Decision Strategy 951

Expected Value of Sample Information 954

21.4 Computing Branch Probabilities Using Bayes’ Theorem 960

Summary 964

Glossary 965

Key Formulas 966

Supplementary Exercises 966

Case Problem: Lawsuit Defense Strategy 969

Appendix: An Introduction to PrecisionTree 970

Statistics in Practice: Duke Energy 22-2

22.1 Terminology Used in Sample Surveys 22-2

22.2 Types of Surveys and Sampling Methods 22-3

Determining the Sample Size 22-9

22.5 Stratified Simple Random Sampling 22-12

Glossary 22-30 Key Formulas 22-30 Supplementary Exercises 22-34 Appendix: Self-Test Solutions and Answers to Even-Numbered Exercises 22-37

Appendix D Self-Test Solutions and Answers to Even-Numbered

Appendix F Computing p-Values Using Minitab and Excel 1067

Index 1071

The purpose of STATISTICS FOR BUSINESS AND ECONOMICS is to give students,

pri-marily those in the fields of business administration and economics, a conceptual tion to the field of statistics and its many applications The text is applications oriented andwritten with the needs of the nonmathematician in mind; the mathematical prerequisite isknowledge of algebra

introduc-Applications of data analysis and statistical methodology are an integral part of the nization and presentation of the text material The discussion and development of each tech-nique is presented in an application setting, with the statistical results providing insights todecisions and solutions to problems

orga-Although the book is applications oriented, we have taken care to provide sound ological 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 ad-vanced statistical material A bibliography to guide further study is included as an appendix.The text introduces the student to the software packages of Minitab 15 and Microsoft®

method-Office Excel 2007 and emphasizes the role of computer software in the application ofstatistical analysis Minitab is illustrated as it is one of the leading statistical software pack-ages for both education and statistical practice Excel is not a statistical software package,but the wide availability and use of Excel make it important for students to understand thestatistical capabilities of this package Minitab and Excel procedures are provided inappendixes so that instructors have the flexibility of using as much computer emphasis asdesired for the course

Changes in the Eleventh Edition

We appreciate the acceptance and positive response to the previous editions of STATISTICS

FOR BUSINESS AND ECONOMICS Accordingly, in making modifications for this newedition, we have maintained the presentation style and readability of those editions The sig-nificant changes in the new edition are summarized here

Content Revisions

• Revised Chapter 18 — “Time Series Analysis and Forecasting.”The chapter hasbeen completely rewritten to focus more on using the pattern in a time series plot toselect an appropriate forecasting method We begin with a new Section 18.1 on timeseries patterns, followed by a new Section 18.2 on methods for measuring forecast ac-curacy Section 18.3 discusses moving averages and exponential smoothing Section18.4 introduces methods appropriate for a time series that exhibits a trend Here we illustrate how regression analysis and Holt’s linear exponential smoothing can be usedfor linear trend projection, and then discuss how regression analysis can be used tomodel nonlinear relationships involving a quadratic trend and an exponential growth.Section 18.5 then shows how dummy variables can be used to model seasonality in aforecasting equation Section 18.6 discusses classical time series decomposition,including the concept of deseasonalizing a time series There is a new appendix onforecasting using the Excel add-in StatTools and most exercises are new or updated

• Revised Chapter 19 — “Nonparametric Methods.”The treatment of ric methods has been revised and updated We contrast each nonparametric method

nonparamet-with its parametric counterpart and describe how fewer assumptions are required forthe nonparametric procedure The sign test emphasizes the test for a population median, which is important in skewed populations where the median is often the pre-ferred measure of central location The Wilcoxon Rank-Sum test is used for bothmatched samples tests and tests about a median of a symmetric population A newsmall-sample application of the Mann-Whitney-Wilcoxon test shows the exact sam-pling distribution of the test statistic and is used to explain why the sum of the signedranks can be used to test the hypothesis that the two populations are identical The chap-ter concludes with the Kruskal-Wallis test and rank correlation New chapter endingappendixes describe how Minitab, Excel, and StatTools can be used to implement non-parametric methods Twenty-seven data sets are now available to facilitate computersolution of the exercises.

• StatTools Add-In for Excel Excel 2007 does not contain statistical functions ordata analysis tools to perform all the statistical procedures discussed in the text Stat-Tools is a commercial Excel 2007 add-in, developed by Palisades Corporation, thatextends the range of statistical options for Excel users In an appendix to Chapter 1

we show how to download and install StatTools, and most chapters include a ter appendix that shows the steps required to accomplish a statistical procedure us-ing StatTools

chap-We have been very careful to make the use of StatTools completely optional sothat instructors who want to teach using the standard tools available in Excel 2007can continue to do so But users who want additional statistical capabilities notavailable in standard Excel 2007 now have access to an industry standard statisticsadd-in that students will be able to continue to use in the workplace

• Change in Terminology for Data In the previous edition, nominal and ordinal datawere classified as qualitative; interval and ratio data were classified as quantitative

In this edition, nominal and ordinal data are referred to as categorical data nal and ordinal data use labels or names to identify categories of like items Thus,

Nomi-we believe that the term categorical is more descriptive of this type of data

• Introducing Data Mining A new section in Chapter 1 introduces the relatively newfield of data mining We provide a brief overview of data mining and the concept of

a data warehouse We also describe how the fields of statistics and computer sciencejoin to make data mining operational and valuable

• Ethical Issues in Statistics Another new section in Chapter 1 provides a sion of ethical issues when presenting and interpreting statistical information

discus-• Updated Excel Appendix for Tabular and Graphical Descriptive Statistics Thechapter-ending Excel appendix for Chapter 2 shows how the Chart Tools, PivotTableReport, and PivotChart Report can be used to enhance the capabilities for displayingtabular and graphical descriptive statistics

• Comparative Analysis with Box Plots The treatment of box plots in Chapter 2 hasbeen expanded to include relatively quick and easy comparisons of two or more datasets Typical starting salary data for accounting, finance, management, and market-ing majors are used to illustrate box plot multigroup comparisons

• Revised Sampling Material The introduction of Chapter 7 has been revised and nowincludes the concepts of a sampled population and a frame The distinction betweensampling from a finite population and an infinite population has been clarified, withsampling from a process used to illustrate the selection of a random sample from aninfinite population A practical advice section stresses the importance of obtainingclose correspondence between the sampled population and the target population

• Revised Introduction to Hypothesis Testing Section 9.1, Developing Null and Alternative Hypotheses, has been revised A better set of guidelines has been de-veloped for identifying the null and alternative hypotheses The context of the sit-uation and the purpose for taking the sample are key In situations in which the

focus is on finding evidence to support a research finding, the research hypothesis

is the alternative hypothesis In situations where the focus is on challenging an sumption, the assumption is the null hypothesis

as-• New PrecisionTree Software for Decision Analysis PrecisionTree is another Excel add-in developed by Palisades Corporation that is very helpful in decision analy-sis Chapter 21 has a new appendix which shows how to use the PrecisionTree add-in

• New Case Problems We have added 5 new case problems to this edition, bringingthe total number of case problems to 31 A new case problem on descriptive statis-tics appears in Chapter 3 and a new case problem on hypothesis testing appears inChapter 9 Three new case problems have been added to regression in Chapters 14,

15, and 16 These case problems provide students with the opportunity to analyzelarger data sets and prepare managerial reports based on the results of the analysis

• New Statistics in Practice Applications Each chapter begins with a Statistics inPractice vignette that describes an application of the statistical methodology to becovered in the chapter New to this edition are Statistics in Practice articles forOceanwide Seafood in Chapter 4 and the London-based marketing services com-pany dunnhumby in Chapter 15

• New Examples and Exercises Based on Real Data We continue to make a nificant effort to update our text examples and exercises with the most current realdata and referenced sources of statistical information In this edition, we have addedapproximately 150 new examples and exercises based on real data and referenced

sig-sources Using data from sources also used by The Wall Street Journal, USA Today,

Barron’s, and others, we have drawn from actual studies to develop explanationsand to create exercises that demonstrate the many uses of statistics in business andeconomics 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 ogy and its application The eleventh edition of the text contains over 350 examplesand exercises based on real data

methodol-Features and Pedagogy

Authors Anderson, Sweeney, and Williams have continued many of the features that peared in previous editions Important ones for students are noted here

ap-Methods Exercises and Applications Exercises

The end-of-section exercises are split into two parts, Methods and Applications The ods 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 situa-tions Thus, students first focus on the computational “nuts and bolts” and then move on tothe subtleties of statistical application and interpretation

Meth-Self-Test Exercises

Certain exercises are identified as “Self-Test Exercises.” Completely worked-out solutionsfor these exercises are provided in Appendix D at the back of the book Students can attemptthe 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, which appear in the margins, are designed to vide emphasis and enhance understanding of the terms and concepts being presented in the text

pro-At the end of many sections, we provide Notes and Comments designed to give the dent additional insights about the statistical methodology and its application Notes andComments include warnings about or limitations of the methodology, recommendations forapplication, brief descriptions of additional technical considerations, and other matters.

stu-Data Files Accompany the Text

Over 200 data files are available on the website that accompanies the text The data sets areavailable in both Minitab and Excel formats File logos are used in the text to identify thedata sets that are available on the website Data sets for all case problems as well as datasets for larger exercises are included

Acknowledgments

A special thank you goes to Jeffrey D Camm, University of Cincinnati, and James J

Cochran, Louisiana Tech University, for their contributions to this eleventh edition of

Sta-tistics for Business and Economics Professors Camm and Cochran provided extensive put for the new chapters on forecasting and nonparametric methods In addition, theyprovided helpful input and suggestions for new case problems, exercises, and Statistics inPractice articles We would also like to thank our associates from business and industry whosupplied the Statistics in Practice features We recognize them individually by a credit line

in-in each of the articles Fin-inally, we are also in-indebted to our senior acquisitions editor CharlesMcCormick, Jr., our developmental editor Maggie Kubale, our content project manager,Jacquelyn K Featherly, our marketing manager Bryant T Chrzan, and others at CengageSouth-Western for their editorial counsel and support during the preparation of this text

David R Anderson Dennis J Sweeney Thomas A Williams

David R Anderson. David R Anderson is Professor of Quantitative Analysis in the lege of Business Administration at the University of Cincinnati Born in Grand Forks, NorthDakota, he earned his B.S., M.S., and Ph.D degrees from Purdue University ProfessorAnderson has served as Head of the Department of Quantitative Analysis and OperationsManagement and as Associate Dean of the College of Business Administration at the University of Cincinnati In addition, he was the coordinator of the College’s first ExecutiveProgram.

Col-At the University of Cincinnati, Professor Anderson has taught introductory statisticsfor business students as well as graduate-level courses in regression analysis, multivariateanalysis, 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 forexcellence in teaching and excellence in service to student organizations

Professor Anderson has coauthored 10 textbooks in the areas of statistics, managementscience, linear programming, and production and operations management He is an activeconsultant 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 DesMoines, Iowa, he earned a B.S.B.A degree from Drake University and his M.B.A andD.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; during1981–82, 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 ofBusiness Administration at the University of Cincinnati

Professor Sweeney has published more than 30 articles and monographs in the area ofmanagement 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 10 textbooks in the areas of statistics, managementscience, linear programming, and production and operations management

Thomas A Williams. Thomas A Williams is Professor of Management Science in theCollege of Business at Rochester Institute of Technology Born in Elmira, New York, heearned his B.S degree at Clarkson University He did his graduate work at RensselaerPolytechnic Institute, where he received his M.S and Ph.D degrees

Before joining the College of Business at RIT, Professor Williams served for sevenyears as a faculty member in the College of Business Administration at the University ofCincinnati, where he developed the undergraduate program in Information Systems andthen served as its coordinator At RIT he was the first chairman of the Decision SciencesDepartment He teaches courses in management science and statistics, as well as graduatecourses in regression and decision analysis

Professor Williams is the coauthor of 11 textbooks in the areas of management science,statistics, production and operations management, and mathematics He has been a consul-

tant 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

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Data and Statistics

Categorical and Quantitative Data

Cross-Sectional and Time

Series Data

Existing SourcesStatistical StudiesData Acquisition Errors

With a global circulation of more than 1 million,

Busi-nessWeekis the most widely read business magazine in

the world More than 200 dedicated reporters and editors

in 26 bureaus worldwide deliver a variety of articles of

interest to the business and economic community Along

with feature articles on current topics, the magazine

contains regular sections on International Business,

Eco-nomic Analysis, Information Processing, and Science &

Technology Information in the feature articles and the

regular sections helps readers stay abreast of current

de-velopments and assess the impact of those dede-velopments

on business and economic conditions

Most issues of BusinessWeek provide an in-depth

report on a topic of current interest Often, the in-depth

re-ports contain statistical facts and summaries that help the

reader understand the business and economic information

For example, the February 23, 2009 issue contained a

fea-ture article about the home foreclosure crisis, the March

17, 2009 issue included a discussion of when the stock

market would begin to recover, and the May 4, 2009 issue

had a special report on how to make pay cuts less painful

In addition, the weekly BusinessWeek Investor provides

statistics about the state of the economy, including

produc-tion indexes, stock prices, mutual funds, and interest rates

BusinessWeek also uses statistics and statistical

in-formation 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 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 Theresults of the survey are also made available to potentialadvertisers The high percentage of subscribers using per-sonal computers at home and the high percentage of sub-scribers 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 availablefor statistical analysis and describe how the data are ob-tained We introduce descriptive statistics and statisticalinference as ways of converting data into meaningful andeasily interpreted statistical information

BusinessWeekuses statistical facts and summaries

in many of its articles © Terri Miller/E-VisualCommunications, Inc

BUSINESSWEEK*

NEW YORK, NEW YORK

STATISTICS in PRACTICE

*The authors are indebted to Charlene Trentham, Research Manager at

BusinessWeek, for providing this Statistics in Practice.

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

• The National Association of Realtors reported that the median price paid by

first-time home buyers is $165,000 (The Wall Street Journal, February 11, 2009).

• NCAA president Myles Brand reported that college athletes are earning degrees atrecord rates Latest figures show that 79% of all men and women student-athletesgraduate (Associated Press, October 15, 2008)

• The average one-way travel time to work is 25.3 minutes (U.S Census Bureau,March 2009)

• A record high 11% of U.S homes are vacant, a glut created by the housing boom

and subsequent collapse (USA Today, February 13, 2009).

• The national average price for regular gasoline reached $4.00 per gallon for the first

time in history (Cable News Network website, June 8, 2008).

• The New York Yankees have the highest salaries in major league baseball The

to-tal payroll is $201,449,289 with a median salary of $5,000,000 (USA Today Salary

Data Base,April 2009)

• The Dow Jones Industrial Average closed at 8721 (The Wall Street Journal, June 2,

2009)

The numerical facts in the preceding statements ($165,000, 79%, 25.3, 11%, $4.00,

$201,449,289, $5,000,000 and 8721) 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 situations However, as you will see, thefield, or subject, of statistics involves much more than numerical facts In a broader sense,

statisticsis defined as the art and science of collecting, analyzing, presenting, and preting data Particularly in business and economics, the information provided by collect-ing, analyzing, presenting, and interpreting data gives managers and decision makers abetter understanding of the business and economic environment and thus enables them tomake more informed and better decisions In this text, we emphasize the use of statistics for business and economic decision making

inter-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 dif-

ference between quantitative and categorical data, and illustrates the uses of cross-sectionaland time series data Section 1.3 discusses how data can be obtained from existing sources

or through survey and experimental studies designed to obtain new data The important rolethat the Internet now plays in obtaining data is also highlighted The uses of data in devel-oping descriptive statistics and in making statistical inferences are described in Sections 1.4and 1.5 The last three sections of Chapter 1 provide the role of the computer in statisticalanalysis, an introduction to the relative new field of data mining, and a discussion of ethi-cal guidelines for statistical practice A chapter-ending appendix includes an introduction

to the add-in StatTools which can be used to extend the statistical options for users of Microsoft Excel

In today’s global business and economic environment, anyone can access vast amounts ofstatistical information The most successful managers and decision makers understand theinformation 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

Accounting

Public accounting firms use statistical sampling procedures when conducting audits for theirclients For instance, suppose an accounting firm wants to determine whether the amount ofaccounts receivable shown on a client’s balance sheet fairly represents the actual amount

of accounts receivable Usually the large number of individual accounts receivable makesreviewing and validating every account too time-consuming and expensive As commonpractice 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 towhether the accounts receivable amount shown on the client’s balance sheet is acceptable

Financial analysts use a variety of statistical information to guide their investment mendations In the case of stocks, the analysts review a variety of financial data includingprice/earnings ratios and dividend yields By comparing the information for an individualstock with information about the stock market averages, a financial analyst can begin todraw a conclusion as to whether an individual stock is over- or underpriced For example,

recom-Barron’s(February 18, 2008) reported that the average dividend yield for the 30 stocks inthe Dow Jones Industrial Average was 2.45% Altria Group showed a dividend yield of3.05% In this case, the statistical information on dividend yield indicates a higher dividendyield for Altria Group than the average for the Dow Jones stocks Therefore, a financial an-alyst might conclude that Altria Group was underpriced This and other information aboutAltria Group would help the analyst make a buy, sell, or hold recommendation for the stock

Marketing

Electronic scanners at retail checkout counters collect data for a variety of marketing search applications For example, data suppliers such as ACNielsen and Information Re-sources, Inc., purchase point-of-sale scanner data from grocery stores, process the data, andthen sell statistical summaries of the data to manufacturers Manufacturers spend hundreds

re-of thousands re-of dollars per product category to obtain this type re-of scanner data turers also purchase data and statistical summaries on promotional activities such as spe-cial pricing and the use of in-store displays Brand managers can review the scannerstatistics and the promotional activity statistics to gain a better understanding of the rela-tionship between promotional activities and sales Such analyses often prove helpful inestablishing future marketing strategies for the various products

Manufac-Production

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

out-put 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 valuebelow the chart’s lower control limit indicates underfilling The process is termed “in con-

trol” 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

Economics

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 fore-casting inflation rates, economists use statistical information on such indicators as the ProducerPrice Index, the unemployment rate, and manufacturing capacity utilization Often these sta-tistical 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 ofthis text Such examples provide an overview of the breadth of statistical applications Tosupplement these examples, practitioners in the fields of business and economics providedchapter-opening Statistics in Practice articles that introduce the material covered in eachchapter The Statistics in Practice applications show the importance of statistics in a widevariety of business and economic situations

1.2 Data

Dataare the facts and figures collected, analyzed, and summarized for presentation and terpretation All the data collected in a particular study are referred to as the data setfor thestudy Table 1.1 shows a data set containing information for 25 mutual funds that are part

in-of the Morningstar Funds500 for 2008 Morningstar is a company that tracks over 7000

mutual funds and prepares in-depth analyses of 2000 of these Their recommendations arefollowed closely by financial analysts and individual investors

Elements, Variables, and ObservationsElementsare the entities on which data are collected For the data set in Table 1.1 each in-dividual mutual fund is an element: the element names appear in the first column With 25mutual funds, the data set contains 25 elements

Avariableis a characteristic of interest for the elements The data set in Table 1.1 cludes the following five variables:

in-• Fund Type: The type of mutual fund, labeled DE (Domestic Equity), IE tional Equity), and FI (Fixed Income)

(Interna-• Net Asset Value ($): The closing price per share on December 31, 2007

5-Year Expense Fund Net Asset Average Ratio Morningstar

Source: Morningstar Funds500(2008).