statistics for business economics by anderson 2

CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION

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.

Cumulative
probability

z

0

z

.00

.01

.02

.03

.04

.05

.06

.07

.08

.09

Ϫ3.0

.0013

.0013

.0013

.0012

.0012

.0011

.0011

.0011

.0010

.0010

Ϫ2.9
Ϫ2.8
Ϫ2.7
Ϫ2.6
Ϫ2.5

.0019
.0026
.0035
.0047
.0062

.0018
.0025
.0034
.0045
.0060

.0018
.0024
.0033
.0044
.0059

.0017
.0023
.0032
.0043
.0057

.0016
.0023
.0031
.0041
.0055

.0016
.0022
.0030
.0040
.0054

.0015
.0021
.0029
.0039
.0052

.0015
.0021
.0028
.0038
.0051

.0014
.0020
.0027
.0037
.0049

.0014
.0019
.0026
.0036
.0048

Ϫ2.4
Ϫ2.3
Ϫ2.2
Ϫ2.1
Ϫ2.0

.0082
.0107
.0139
.0179
.0228

.0080
.0104
.0136
.0174
.0222

.0078
.0102
.0132
.0170
.0217

.0075
.0099
.0129
.0166
.0212

.0073
.0096
.0125
.0162
.0207

.0071
.0094
.0122
.0158
.0202

.0069
.0091
.0119
.0154
.0197

.0068
.0089
.0116
.0150
.0192

.0066
.0087
.0113
.0146
.0188

.0064
.0084
.0110
.0143
.0183

Ϫ1.9
Ϫ1.8
Ϫ1.7
Ϫ1.6
Ϫ1.5

.0287
.0359
.0446
.0548
.0668

.0281
.0351
.0436
.0537
.0655

.0274
.0344
.0427
.0526
.0643

.0268
.0336
.0418
.0516
.0630

.0262
.0329
.0409
.0505
.0618

.0256
.0322
.0401
.0495
.0606

.0250
.0314
.0392
.0485
.0594

.0244
.0307
.0384
.0475
.0582

.0239
.0301
.0375
.0465
.0571

.0233
.0294
.0367
.0455
.0559

Ϫ1.4
Ϫ1.3
Ϫ1.2
Ϫ1.1
Ϫ1.0

.0808
.0968
.1151
.1357
.1587

.0793
.0951
.1131
.1335
.1562

.0778
.0934
.1112
.1314
.1539

.0764
.0918
.1093
.1292
.1515

.0749
.0901
.1075
.1271
.1492

.0735
.0885
.1056
.1251
.1469

.0721
.0869
.1038
.1230
.1446

.0708
.0853
.1020
.1210
.1423

.0694
.0838
.1003
.1190
.1401

.0681
.0823
.0985
.1170
.1379

Ϫ.9
Ϫ.8
Ϫ.7
Ϫ.6
Ϫ.5

.1841
.2119
.2420
.2743
.3085

.1814
.2090
.2389
.2709
.3050

.1788
.2061
.2358
.2676
.3015

.1762
.2033
.2327
.2643
.2981

.1736
.2005
.2296
.2611
.2946

.1711
.1977
.2266
.2578
.2912

.1685
.1949
.2236
.2546
.2877

.1660
.1922
.2206
.2514
.2843

.1635
.1894
.2177
.2483
.2810

.1611
.1867
.2148
.2451
.2776

Ϫ.4
Ϫ.3
Ϫ.2
Ϫ.1
Ϫ.0

.3446
.3821
.4207
.4602
.5000

.3409
.3783
.4168
.4562
.4960

.3372
.3745
.4129
.4522
.4920

.3336
.3707
.4090
.4483
.4880

.3300
.3669
.4052
.4443
.4840

.3264
.3632
.4013
.4404
.4801

.3228
.3594
.3974
.4364
.4761

.3192
.3557
.3936
.4325
.4721

.3156
.3520
.3897
.4286
.4681

.3121
.3483
.3859
.4247
.4641


CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION

Cumulative
probability

0

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.

z

z

.00

.01

.02

.03

.04

.05

.06

.07

.08

.09

.0
.1
.2
.3
.4

.5000
.5398
.5793
.6179
.6554

.5040
.5438
.5832

.6217
.6591

.5080
.5478
.5871
.6255
.6628

.5120
.5517
.5910
.6293
.6664

.5160
.5557
.5948
.6331
.6700

.5199
.5596
.5987
.6368
.6736

.5239
.5636
.6026

.6406
.6772

.5279
.5675
.6064
.6443
.6808

.5319
.5714
.6103
.6480
.6844

.5359
.5753
.6141
.6517
.6879

.5
.6
.7
.8
.9

.6915
.7257
.7580

.7881
.8159

.6950
.7291
.7611
.7910
.8186

.6985
.7324
.7642
.7939
.8212

.7019
.7357
.7673
.7967
.8238

.7054
.7389
.7704
.7995
.8264

.7088
.7422
.7734

.8023
.8289

.7123
.7454
.7764
.8051
.8315

.7157
.7486
.7794
.8078
.8340

.7190
.7517
.7823
.8106
.8365

.7224
.7549
.7852
.8133
.8389

1.0
1.1
1.2

1.3
1.4

.8413
.8643
.8849
.9032
.9192

.8438
.8665
.8869
.9049
.9207

.8461
.8686
.8888
.9066
.9222

.8485
.8708
.8907
.9082
.9236

.8508
.8729
.8925

.9099
.9251

.8531
.8749
.8944
.9115
.9265

.8554
.8770
.8962
.9131
.9279

.8577
.8790
.8980
.9147
.9292

.8599
.8810
.8997
.9162
.9306

.8621
.8830
.9015

.9177
.9319

1.5
1.6
1.7
1.8
1.9

.9332
.9452
.9554
.9641
.9713

.9345
.9463
.9564
.9649
.9719

.9357
.9474
.9573
.9656
.9726

.9370
.9484
.9582

.9664
.9732

.9382
.9495
.9591
.9671
.9738

.9394
.9505
.9599
.9678
.9744

.9406
.9515
.9608
.9686
.9750

.9418
.9525
.9616
.9693
.9756

.9429
.9535
.9625

.9699
.9761

.9441
.9545
.9633
.9706
.9767

2.0
2.1
2.2
2.3
2.4

.9772
.9821
.9861
.9893
.9918

.9778
.9826
.9864
.9896
.9920

.9783
.9830
.9868

.9898
.9922

.9788
.9834
.9871
.9901
.9925

.9793
.9838
.9875
.9904
.9927

.9798
.9842
.9878
.9906
.9929

.9803
.9846
.9881
.9909
.9931

.9808
.9850
.9884

.9911
.9932

.9812
.9854
.9887
.9913
.9934

.9817
.9857
.9890
.9913
.9936

2.5
2.6
2.7
2.8
2.9

.9938
.9953
.9965
.9974
.9981

.9940
.9955
.9966

.9975
.9982

.9941
.9956
.9967
.9976
.9982

.9943
.9957
.9968
.9977
.9983

.9945
.9959
.9969
.9977
.9984

.9946
.9960
.9970
.9978
.9984

.9948
.9961
.9971

.9979
.9985

.9949
.9962
.9972
.9979
.9985

.9951
.9963
.9973
.9980
.9986

.9952
.9964
.9974
.9981
.9986

3.0

.9986

.9987

.9987

.9988

.9988

.9989

.9989

.9989

.9990

.9990


STATISTICS FOR
BUSINESS AND
ECONOMICS 10e


This page intentionally left blank


STATISTICS FOR
BUSINESS AND
ECONOMICS 10e
David R. Anderson
University of Cincinnati

Dennis J. Sweeney
University of Cincinnati

Thomas A. Williams
Rochester Institute of Technology


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

VP/Editorial Director:
Jack W. Calhoun

Manager, Editorial Media:
John Barans

Art Director:
Stacy Jenkins Shirley

Editor-in-Chief:
Alex von Rosenberg

Technology Project Manager:
John Rich

Internal Designers:
Michael Stratton/cmiller design

Senior Acquisitions Editor:
Charles McCormick, Jr.

Senior Manufacturing Coordinator:

Diane Gibbons

Cover Designer:
Paul Neff

Senior Developmental Editor:
Alice Denny

Production House:
Interactive Composition Corporation

Cover Images:
© Brand X Images/Getty Images

Senior Marketing Manager:
Larry Qualls

Printer:
RR Donnelley
Willard Manufacturing Division

Photography Manager:
John Hill

ALL RIGHTS RESERVED.
No part of this work covered by the copyright hereon may be reproduced or used in
any form or by any means—graphic, electronic, or mechanical, including photocopying, recording, taping, Web distribution or
information storage and retrieval systems, or
in any other manner—without the written
permission of the publisher.

Library of Congress Control Number:
2006927620

Marketing Communications Manager:
Libby Shipp

Photo Researcher:
Darren Wright

Content Project Manager:
Amy Hackett
COPYRIGHT © 2008, 2005
Thomson South-Western, a part of The
Thomson Corporation. Thomson, the Star
logo, and South-Western are trademarks used
herein under license.
Printed in the United States of America
1 2 3 4 5 09 08 07 06
Student Edition:
ISBN 13: 978-0-324-36505-4
ISBN 10: 0-324-36505-5
Student Edition with CD:
ISBN 13: 978-0-324-36068-4
ISBN 10: 0-324-36068-1
Instructor’s Edition:
ISBN 13: 978-0-324-36507-8
ISBN 10: 0-324-36507-1
Instructor’s Edition with CD:
ISBN 13: 978-0-324-36509-2

ISBN 10: 0-324-36509-8

For permission to use material from this text
or product, submit a request online at
.

For more information about our products,
contact us at:
Thomson Learning Academic Resource
Center
1-800-423-0563
Thomson Higher Education
5191 Natorp Boulevard
Mason, OH 45040
USA


Dedicated to
Marcia, Cherri, and Robbie


This page intentionally left blank


Brief Contents

Preface xxv
About the Authors xxix
Chapter 1 Data and Statistics 1
Chapter 2 Descriptive Statistics: Tabular and Graphical

Presentations 26
Chapter 3 Descriptive Statistics: Numerical Measures 81
Chapter 4 Introduction to Probability 141
Chapter 5 Discrete Probability Distributions 186
Chapter 6 Continuous Probability Distributions 225
Chapter 7 Sampling and Sampling Distributions 257
Chapter 8 Interval Estimation 299
Chapter 9 Hypothesis Tests 338
Chapter 10 Statistical Inference About Means and Proportions
with Two Populations 393
Chapter 11 Inferences About Population Variances 434
Chapter 12 Tests of Goodness of Fit and Independence 457
Chapter 13 Experimental Design and Analysis of Variance 490
Chapter 14 Simple Linear Regression 543
Chapter 15 Multiple Regression 624
Chapter 16 Regression Analysis: Model Building 693
Chapter 17 Index Numbers 744
Chapter 18 Forecasting 765
Chapter 19 Nonparametric Methods 812
Chapter 20 Statistical Methods for Quality Control 846
Chapter 21 Decision Analysis 879
Chapter 22 Sample Survey On CD
Appendix A References and Bibliography 916
Appendix B Tables 918
Appendix C Summation Notation 946
Appendix D Self-Test Solutions and Answers to Even-Numbered
Exercises 948
Appendix E Using Excel Functions 995
Appendix F Computing p-Values Using Minitab and Excel 1000
Index 1004

This page intentionally left blank


Contents

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 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


x

Contents

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 81
Statistics in Practice: Small Fry Design 82
3.1 Measures of Location 83
Mean 83
Median 84
Mode 85
Percentiles 86
Quartiles 87
3.2 Measures of Variability 91
Range 92
Interquartile Range 92
Variance 93
Standard Deviation 95
Coefficient of Variation 95
3.3 Measures of Distribution Shape, Relative Location, and Detecting
Outliers 98
Distribution Shape 98
z-Scores 99
Chebyshev’s Theorem 100
Empirical Rule 101
Detecting Outliers 102
3.4 Exploratory Data Analysis 105

Five-Number Summary 105
Box Plot 106


Contents

3.5 Measures of Association Between Two Variables 110
Covariance 110
Interpretation of the Covariance 112
Correlation Coefficient 114
Interpretation of the Correlation Coefficient 115
3.6 The Weighted Mean and Working with
Grouped Data 119
Weighted Mean 119
Grouped Data 120
Summary 124
Glossary 125
Key Formulas 126
Supplementary Exercises 128
Case Problem 1: Pelican Stores 132
Case Problem 2: Motion Picture Industry 133
Case Problem 3: Business Schools of Asia-Pacific 133
Appendix 3.1 Descriptive Statistics Using Minitab 135
Appendix 3.2 Descriptive Statistics Using Excel 137

Chapter 4 Introduction to Probability 141
Statistics in Practice: Rohm and Hass Company 142
4.1 Experiments, Counting Rules, and Assigning
Probabilities 143
Counting Rules, Combinations, and

Permutations 144
Assigning Probabilities 148
Probabilities for the KP&L Project 150
4.2 Events and Their Probabilities 153
4.3 Some Basic Relationships of Probability 157
Complement of an Event 157
Addition Law 158
4.4 Conditional Probability 163
Independent Events 167
Multiplication Law 167
4.5 Bayes’ Theorem 171
Tabular Approach 175
Summary 177
Glossary 177
Key Formulas 178
Supplementary Exercises 179
Case Problem: Hamilton County Judges 183

xi


xii

Contents

Chapter 5 Discrete Probability Distributions 186
Statistics in Practice: Citibank 187
5.1 Random Variables 187
Discrete Random Variables 188
Continuous Random Variables 189

5.2 Discrete Probability Distributions 190
5.3 Expected Value and Variance 196
Expected Value 196
Variance 196
5.4 Binomial Probability Distribution 200
A Binomial Experiment 201
Martin Clothing Store Problem 202
Using Tables of Binomial Probabilities 206
Expected Value and Variance for the Binomial Distribution 207
5.5 Poisson Probability Distribution 210
An Example Involving Time Intervals 211
An Example Involving Length or Distance Intervals 213
5.6 Hypergeometric Probability Distribution 214
Summary 217
Glossary 218
Key Formulas 219
Supplementary Exercises 220
Appendix 5.1 Discrete Probability Distributions with Minitab 222
Appendix 5.2 Discrete Probability Distributions with Excel 223

Chapter 6 Continuous Probability Distributions 225
Statistics in Practice: Procter & Gamble 226
6.1 Uniform Probability Distribution 227
Area as a Measure of Probability 228
6.2 Normal Probability Distribution 231
Normal Curve 231
Standard Normal Probability Distribution 233
Computing Probabilities for Any Normal Probability Distribution 238
Grear Tire Company Problem 239
6.3 Normal Approximation of Binomial Probabilities 243

6.4 Exponential Probability Distribution 246
Computing Probabilities for the Exponential Distribution 247
Relationship Between the Poisson and Exponential Distributions 248
Summary 250
Glossary 250
Key Formulas 251
Supplementary Exercises 251


Contents

Case Problem: Specialty Toys 254
Appendix 6.1 Continuous Probability Distributions with Minitab 255
Appendix 6.2 Continuous Probability Distributions with Excel 256

Chapter 7 Sampling and Sampling Distributions 257
Statistics in Practice: MeadWestvaco Corporation 258
7.1 The Electronics Associates Sampling Problem 259
7.2 Simple Random Sampling 260
Sampling from a Finite Population 260
Sampling from an Infinite Population 261
7.3 Point Estimation 264
7.4 Introduction to Sampling Distributions 267
_
7.5 Sampling Distribution of x 270
_
Expected Value of x 270
_
Standard Deviation of x 271
_

Form of the Sampling Distribution of x 272
_
Sampling Distribution of x for the EAI Problem 274
_
Practical Value of the Sampling Distribution of x 274
Relationship Between
the Sample Size and the Sampling
_
Distribution of x 276
_
7.6 Sampling Distribution of p 280
_
Expected Value of p 280
_
Standard Deviation of p 281
_
Form of the Sampling Distribution of p 281
_
Practical Value of the Sampling Distribution of p 282
7.7 Properties of Point Estimators 285
Unbiased 286
Efficiency 287
Consistency 287
7.8 Other Sampling Methods 288
Stratified Random Sampling 288
Cluster Sampling 289
Systematic Sampling 289
Convenience Sampling 290
Judgment Sampling 290
Summary 291

Glossary 291
Key Formulas 292
Supplementary Exercises 292
_
Appendix 7.1 The Expected Value and Standard Deviation of x 295
Appendix 7.2 Random Sampling with Minitab 296
Appendix 7.3 Random Sampling with Excel 297

xiii


xiv

Contents

Chapter 8 Interval Estimation 299
Statistics in Practice: Food Lion 300
8.1 Population Mean: ␴ Known 301
Margin of Error and the Interval Estimate 301
Practical Advice 305
8.2 Population Mean: ␴ Unknown 307
Margin of Error and the Interval Estimate 308
Practical Advice 311
Using a Small Sample 311
Summary of Interval Estimation Procedures 313
8.3 Determining the Sample Size 316
8.4 Population Proportion 319
Determining the Sample Size 321
Summary 324
Glossary 325

Key Formulas 326
Supplementary Exercises 326
Case Problem 1: Young Professional Magazine 329
Case Problem 2: Gulf Real Estate Properties 330
Case Problem 3: Metropolitan Research, Inc. 332
Appendix 8.1 Interval Estimation with Minitab 332
Appendix 8.2 Interval Estimation Using Excel 334

Chapter 9 Hypothesis Tests 338
Statistics in Practice: John Morrell & Company 339
9.1 Developing Null and Alternative Hypotheses 340
Testing Research Hypotheses 340
Testing the Validity of a Claim 340
Testing in Decision-Making Situations 341
Summary of Forms for Null and Alternative Hypotheses 341
9.2 Type I and Type II Errors 342
9.3 Population Mean: ␴ Known 345
One-Tailed Test 345
Two-Tailed Test 351
Summary and Practical Advice 354
Relationship Between Interval Estimation and
Hypothesis Testing 355
9.4 Population Mean: ␴ Unknown 359
One-Tailed Test 360
Two-Tailed Test 361
Summary and Practical Advice 362


xv

Contents

9.5 Population Proportion 365
Summary 368
9.6 Hypothesis Testing and Decision Making 370
9.7 Calculating the Probability of Type II Errors 371
9.8 Determining the Sample Size for a Hypothesis Test About
a Population Mean 376
Summary 380
Glossary 381
Key Formulas 381
Supplementary Exercises 382
Case Problem 1: Quality Associates, Inc. 385
Case Problem 2: Unemployment Study 386
Appendix 9.1 Hypothesis Testing with Minitab 386
Appendix 9.2 Hypothesis Testing with Excel 388

Chapter 10 Statistical Inference About Means and Proportions
with Two Populations 393
Statistics in Practice: U.S. Food and Drug Administration 394
10.1 Inferences About the Difference Between Two Population Means:
␴1 and ␴2 Known 395
Interval Estimation of ␮1 – ␮2 395
Hypothesis Tests About ␮1 – ␮2 397
Practical Advice 399
10.2 Inferences About the Difference Between Two Population Means:
␴1 and ␴2 Unknown 402
Interval Estimation of ␮1 – ␮2 402
Hypothesis Tests About ␮1 – ␮2 403
Practical Advice 406

10.3 Inferences About the Difference Between Two Population Means:
Matched Samples 410
10.4 Inferences About the Difference Between Two Population
Proportions 416
Interval Estimation of p1 – p2 416
Hypothesis Tests About p1 – p2 418
Summary 423
Glossary 423
Key Formulas 424
Supplementary Exercises 425
Case Problem: Par, Inc. 428
Appendix 10.1 Inferences About Two Populations Using Minitab 429
Appendix 10.2 Inferences About Two Populations Using Excel 431


xvi

Contents

Chapter 11 Inferences About Population Variances 434
Statistics in Practice: U.S. General Accounting Office 435
11.1 Inferences About a Population Variance 436
Interval Estimation 436
Hypothesis Testing 440
11.2 Inferences About Two Populations Variances 445
Summary 452
Key Formulas 452
Supplementary Exercises 453
Case Problem: Air Force Training Program 454
Appendix 11.1 Population Variances with Minitab 455

Appendix 11.2 Population Variances with Excel 456

Chapter 12 Tests of Goodness of Fit and Independence 457
Statistics in Practice: United Way 458
12.1 Goodness of Fit Test: A Multinomial Population 459
12.2 Test of Independence 464
12.3 Goodness of Fit Test: Poisson and Normal Distributions 472
Poisson Distribution 472
Normal Distribution 476
Summary 481
Glossary 481
Key Formulas 481
Supplementary Exercises 482
Case Problem: A Bipartisan Agenda for Change 485
Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab 486
Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel 487

Chapter 13 Experimental Design and Analysis of Variance 490
Statistics in Practice: Burke Marketing Services, Inc. 491
13.1 An Introduction to Experimental Design and Analysis of Variance 492
Data Collection 493
Assumptions for Analysis of Variance 494
Analysis of Variance: A Conceptual Overview 494
13.2 Analysis of Variance and the Completely Randomized Design 497
Between-Treatments Estimate of Population Variance 498
Within-Treatments Estimate of Population Variance 499
Comparing the Variance Estimates: The F Test 500
ANOVA Table 502
Computer Results for the Analysis of Variance 503
Testing for the Equality of k Population Means: An Observational Study 504

Contents

13.3 Multiple Comparison Procedures 508
Fisher’s LSD 508
Type I Error Rates 511
13.4 Randomized Block Design 514
Air Traffic Controller Stress Test 515
ANOVA Procedure 516
Computations and Conclusions 517
13.5 Factorial Experiment 521
ANOVA Procedure 523
Computations and Conclusions 523
Summary 529
Glossary 529
Key Formulas 530
Supplementary Exercises 532
Case Problem 1: Wentworth Medical Center 536
Case Problem 2: Compensation for Sales Professionals 537
Appendix 13.1 Analysis of Variance with Minitab 538
Appendix 13.2 Analysis of Variance with Excel 539

Chapter 14 Simple Linear Regression 543
Statistics in Practice: Alliance Data Systems 544
14.1 Simple Linear Regression Model 545
Regression Model and Regression Equation 545
Estimated Regression Equation 546
14.2 Least Squares Method 548
14.3 Coefficient of Determination 559

Correlation Coefficient 562
14.4 Model Assumptions 566
14.5 Testing for Significance 568
Estimate of ␴2 568
t Test 569
Confidence Interval for ␤1 570
F Test 571
Some Cautions About the Interpretation of Significance Tests 573
14.6 Using the Estimated Regression Equation for Estimation
and Prediction 577
Point Estimation 577
Interval Estimation 577
Confidence Interval for the Mean Value of y 578
Prediction Interval for an Individual Value of y 579
14.7 Computer Solution 583
14.8 Residual Analysis: Validating Model Assumptions 588
Residual Plot Against x 589

xvii


xviii

Contents

Residual Plot Against yˆ 590
Standardized Residuals 590
Normal Probability Plot 593
14.9 Residual Analysis: Outliers and Influential Observations 597
Detecting Outliers 597

Detecting Influential Observations 599
Summary 604
Glossary 605
Key Formulas 606
Supplementary Exercises 608
Case Problem 1: Measuring Stock Market Risk 614
Case Problem 2: U.S. Department of Transportation 615
Case Problem 3: Alumni Giving 616
Case Problem 4: Major League Baseball Team Values 616
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 618
Appendix 14.2 A Test for Significance Using Correlation 619
Appendix 14.3 Regression Analysis with Minitab 620
Appendix 14.4 Regression Analysis with Excel 621

Chapter 15 Multiple Regression 624
Statistics in Practice: International Paper 625
15.1 Multiple Regression Model 626
Regression Model and Regression Equation 626
Estimated Multiple Regression Equation 626
15.2 Least Squares Method 627
An Example: Butler Trucking Company 628
Note on Interpretation of Coefficients 630
15.3 Multiple Coefficient of Determination 636
15.4 Model Assumptions 639
15.5 Testing for Significance 640
F Test 640
t Test 643
Multicollinearity 644
15.6 Using the Estimated Regression Equation for Estimation
and Prediction 647

15.7 Qualitative Independent Variables 649
An Example: Johnson Filtration, Inc. 649
Interpreting the Parameters 651
More Complex Qualitative Variables 653
15.8 Residual Analysis 658
Detecting Outliers 659
Studentized Deleted Residuals and Outliers 660


Contents

xix

Influential Observations 661
Using Cook’s Distance Measure to Identify Influential Observations 661
15.9 Logistic Regression 665
Logistic Regression Equation 666
Estimating the Logistic Regression Equation 667
Testing for Significance 669
Managerial Use 669
Interpreting the Logistic Regression Equation 670
Logit Transformation 672
Summary 676
Glossary 677
Key Formulas 678
Supplementary Exercises 680
Case Problem 1: Consumer Research, Inc. 685
Case Problem 2: Predicting Student Proficiency Test Scores 686
Case Problem 3: Alumni Giving 687
Case Problem 4: Predicting Winning Percentage for the NFL 689

Appendix 15.1 Multiple Regression with Minitab 690
Appendix 15.2 Multiple Regression with Excel 690
Appendix 15.3 Logistic Regression with Minitab 691

Chapter 16 Regression Analysis: Model Building 693
Statistics in Practice: Monsanto Company 694
16.1 General Linear Model 695
Modeling Curvilinear Relationships 695
Interaction 699
Transformations Involving the Dependent Variable 701
Nonlinear Models That Are Intrinsically Linear 705
16.2 Determining When to Add or Delete Variables 710
General Case 712
Use of p-Values 713
16.3 Analysis of a Larger Problem 717
16.4 Variable Selection Procedures 720
Stepwise Regression 721
Forward Selection 722
Backward Elimination 723
Best-Subsets Regression 723
Making the Final Choice 724
16.5 Multiple Regression Approach to Experimental Design 727
16.6 Autocorrelation and the Durbin-Watson Test 731
Summary 736
Glossary 736
Key Formulas 736


xx

Contents

Supplementary Exercises 737
Case Problem 1: Analysis of PGA Tour Statistics 740
Case Problem 2: Fuel Economy for Cars 741
Case Problem 3: Predicting Graduation Rates for Colleges
and Universities 741
Appendix 16.1: Variable Selection Procedures with Minitab 742

Chapter 17 Index Numbers 744
Statistics in Practice: U.S. Department of Labor, Bureau of
Labor Statistics 745
17.1 Price Relatives 746
17.2 Aggregate Price Indexes 746
17.3 Computing an Aggregate Price Index from Price Relatives 750
17.4 Some Important Price Indexes 752
Consumer Price Index 752
Producer Price Index 752
Dow Jones Averages 753
17.5 Deflating a Series by Price Indexes 754
17.6 Price Indexes: Other Considerations 758
Selection of Items 758
Selection of a Base Period 758
Quality Changes 758
17.7 Quantity Indexes 759
Summary 761
Glossary 761
Key Formulas 761
Supplementary Exercises 762

Chapter 18 Forecasting 765
Statistics in Practice: Nevada Occupational Health Clinic 766
18.1 Components of a Time Series 767
Trend Component 767
Cyclical Component 769
Seasonal Component 770
Irregular Component 770
18.2 Smoothing Methods 770
Moving Averages 770
Weighted Moving Averages 772
Exponential Smoothing 774
18.3 Trend Projection 780


Contents

18.4 Trend and Seasonal Components 786
Multiplicative Model 786
Calculating the Seasonal Indexes 787
Deseasonalizing the Time Series 791
Using the Deseasonalized Time Series to Identify Trend 791
Seasonal Adjustments 794
Models Based on Monthly Data 794
Cyclical Component 794
18.5 Regression Analysis 796
18.6 Qualitative Approaches 798
Delphi Method 798
Expert Judgment 799
Scenario Writing 799
Intuitive Approaches 799

Summary 799
Glossary 800
Key Formulas 801
Supplementary Exercises 801
Case Problem 1: Forecasting Food and Beverage Sales 806
Case Problem 2: Forecasting Lost Sales 807
Appendix 18.1 Forecasting with Minitab 808
Appendix 18.2 Forecasting with Excel 810

Chapter 19 Nonparametric Methods 812
Statistics in Practice: West Shell Realtors 813
19.1 Sign Test 815
Small-Sample Case 815
Large-Sample Case 817
Hypothesis Test About a Median 818
19.2 Wilcoxin Signed-Rank Test 820
19.3 Mann-Whitney-Wilcoxon Test 825
Small-Sample Case 825
Large-Sample Case 827
19.4 Kruskal-Wallis Test 833
19.5 Rank Correlation 837
Test for Significant Rank Correlation 839
Summary 841
Glossary 842
Key Formulas 842
Supplementary Exercises 843

xxi

xxii

Contents

Chapter 20 Statistical Methods for Quality Control 846
Statistics in Practice: Dow Chemical Company 847
20.1 Philosophies and Frameworks 848
Malcolm Baldrige National Quality Award 848
ISO 9000 849
Six Sigma 849
20.2 Statistical Process Control 851
Control Charts 852
_
x Chart: Process Mean and Standard Deviation Known 853
_
x Chart: Process Mean and Standard Deviation Unknown 855
R Chart 857
p Chart 859
np Chart 862
Interpretation of Control Charts 862
20.3 Acceptance Sampling 865
KALI, Inc.: An Example of Acceptance Sampling 866
Computing the Probability of Accepting a Lot 867
Selecting an Acceptance Sampling Plan 870
Multiple Sampling Plans 871
Summary 874
Glossary 874
Key Formulas 875
Supplementary Exercises 876
Appendix 20.1 Control Charts with Minitab 878

Chapter 21 Decision Analysis 879
Statistics in Practice: Ohio Edison Company 880
21.1 Problem Formulation 881
Payoff Tables 882
Decision Trees 882
21.2 Decision Making with Probabilities 883
Expected Value Approach 883
Expected Value of Perfect Information 885
21.3 Decision Analysis with Sample Information 891
Decision Tree 892
Decision Strategy 893
Expected Value of Sample Information 896
21.4 Computing Branch Probabilities Using Bayes’ Theorem 902
Summary 906
Glossary 907
Key Formulas 908
Case Problem: Lawsuit Defense Strategy 908
Appendix 21.1 Solving the PDC Problem with TreePlan 909