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Statistics for business and economics 11th edition 2011


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
.9916
.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

.9987

.9987

.9987

.9988

.9988

.9989

.9989

.9989

.9990

.9990


STATISTICS FOR
BUSINESS AND
ECONOMICS 11e


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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,
Eleventh Edition
David R. Anderson, Dennis J. Sweeney,
Thomas A. Williams
VP/Editorial Director:
Jack W. Calhoun
Publisher:
Joe Sabatino
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© 2011, 2008 South-Western, Cengage Learning
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Dedicated to
Marcia, Cherri, and Robbie


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Brief Contents

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


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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 5
Scales of Measurement 6
Categorical 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 13
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 33
Relative Frequency and Percent Frequency Distributions 34
Bar Charts and Pie Charts 34


x

Contents

2.2 Summarizing Quantitative Data 39
Frequency Distribution 39
Relative Frequency and Percent Frequency Distributions 41
Dot Plot 41
Histogram 41
Cumulative Distributions 43
Ogive 44
2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 48
2.4 Crosstabulations and Scatter Diagrams 53
Crosstabulation 53
Simpson’s Paradox 56
Scatter 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 87
Median 88
Mode 89
Percentiles 90
Quartiles 91
3.2 Measures of Variability 95
Range 96
Interquartile Range 96
Variance 97
Standard Deviation 99
Coefficient of Variation 99
3.3 Measures of Distribution Shape, Relative Location, and Detecting
Outliers 102
Distribution Shape 102
z-Scores 103
Chebyshev’s Theorem 104
Empirical Rule 105
Detecting Outliers 106


Contents

3.4 Exploratory Data Analysis 109
Five-Number Summary 109
Box Plot 110
3.5 Measures of Association Between Two Variables 115
Covariance 115
Interpretation of the Covariance 117
Correlation Coefficient 119
Interpretation of the Correlation Coefficient 120
3.6 The Weighted Mean and Working with
Grouped Data 124
Weighted Mean 124
Grouped Data 125
Summary 129
Glossary 130
Key Formulas 131
Supplementary Exercises 133
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
Complement of an Event 164
Addition Law 165
4.4 Conditional Probability 171
Independent Events 174
Multiplication Law 174
4.5 Bayes’ Theorem 178
Tabular Approach 182
Summary 184
Glossary 184

xi


xii

Contents

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 195
Continuous Random Variables 196
5.2 Discrete Probability Distributions 197
5.3 Expected Value and Variance 202
Expected Value 202
Variance 203
5.4 Binomial Probability Distribution 207
A Binomial Experiment 208
Martin Clothing Store Problem 209
Using Tables of Binomial Probabilities 213
Expected 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 238
Standard Normal Probability Distribution 240
Computing Probabilities for Any Normal Probability Distribution 245
Grear Tire Company Problem 246
6.3 Normal Approximation of Binomial Probabilities 250
6.4 Exponential Probability Distribution 253
Computing Probabilities for the Exponential Distribution 254
Relationship Between the Poisson and Exponential Distributions 255


Contents

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
7.3 Point Estimation 273
Practical Advice 275
7.4 Introduction to Sampling Distributions 276
_
7.5 Sampling Distribution of x 278
_
Expected Value of x 279
_
Standard Deviation of x 280
_
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
Cluster Sampling 298
Systematic Sampling 298
Convenience Sampling 299
Judgment Sampling 299
Summary 300
Glossary 300
Key Formulas 301

xiii


xiv

Contents

Supplementary Exercises 302
_
Appendix 7.1 The Expected Value and Standard Deviation of x 304
Appendix 7.2 Random Sampling with Minitab 306
Appendix 7.3 Random Sampling with Excel 306
Appendix 7.4 Random Sampling with StatTools 307

Chapter 8 Interval Estimation 308
Statistics in Practice: Food Lion 309
8.1 Population Mean: ␴ Known 310
Margin of Error and the Interval Estimate 310
Practical Advice 314
8.2 Population Mean: ␴ Unknown 316
Margin of Error and the Interval Estimate 317
Practical Advice 320
Using a Small Sample 320
Summary 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

Chapter 9 Hypothesis Tests 348
Statistics in Practice: John Morrell & Company 349
9.1 Developing Null and Alternative Hypotheses 350
The Alternative Hypothesis as a Research Hypothesis 350
The Null Hypothesis as an Assumption to Be Challenged 351
Summary 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 356
Two-Tailed Test 362
Summary and Practical Advice 365


xv

Contents

Relationship Between Interval Estimation and
Hypothesis Testing 366
9.4 Population Mean: ␴ Unknown 370
One-Tailed Test 371
Two-Tailed Test 372
Summary and Practical Advice 373
9.5 Population Proportion 376
Summary 379
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
a Population Mean 387
Summary 391
Glossary 392
Key Formulas 392
Supplementary Exercises 393
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:
␴1 and ␴2 Known 408
Interval Estimation of ␮1 – ␮2 408
Hypothesis Tests About ␮1 – ␮2 410
Practical Advice 412
10.2 Inferences About the Difference Between Two Population Means:
␴1 and ␴2 Unknown 415
Interval Estimation of ␮1 – ␮2 415
Hypothesis Tests About ␮1 – ␮2 417
Practical Advice 419
10.3 Inferences About the Difference Between Two Population Means:
Matched Samples 423
10.4 Inferences About the Difference Between Two Population
Proportions 429
Interval Estimation of p1 – p2 429
Hypothesis Tests About p1 – p2 431
Summary 436


xvi

Contents

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 450
Hypothesis 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 487
Normal 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


Contents

xvii

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
ANOVA Procedure 532
Computations and Conclusions 533
13.5 Factorial Experiment 537
ANOVA Procedure 539
Computations and Conclusions 539
Summary 544
Glossary 545
Key Formulas 545
Supplementary Exercises 547
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
14.3 Coefficient of Determination 576
Correlation Coefficient 579
14.4 Model Assumptions 583
14.5 Testing for Significance 585
Estimate of ␴2 585
t Test 586


xviii

Contents

Confidence Interval for ␤1 587
F Test 588
Some Cautions About the Interpretation of Significance Tests 590
14.6 Using the Estimated Regression Equation for Estimation
and Prediction 594
Point Estimation 594
Interval 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 607
Normal Probability Plot 610
14.9 Residual Analysis: Outliers and Influential Observations 614
Detecting Outliers 614
Detecting 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 644
Estimated Multiple Regression Equation 644
15.2 Least Squares Method 645
An Example: Butler Trucking Company 646
Note on Interpretation of Coefficients 648
15.3 Multiple Coefficient of Determination 654
15.4 Model Assumptions 657


Contents

15.5 Testing for Significance 658
F Test 658
t Test 661
Multicollinearity 662
15.6 Using the Estimated Regression Equation for Estimation
and Prediction 665
15.7 Categorical Independent Variables 668
An Example: Johnson Filtration, Inc. 668
Interpreting the Parameters 670
More Complex Categorical Variables 672
15.8 Residual Analysis 676
Detecting Outliers 678
Studentized Deleted Residuals and Outliers 678
Influential Observations 679
Using Cook’s Distance Measure to Identify
Influential Observations 679
15.9 Logistic Regression 683
Logistic Regression Equation 684
Estimating the Logistic Regression Equation 685
Testing for Significance 687
Managerial Use 688
Interpreting the Logistic Regression Equation 688
Logit Transformation 691
Summary 694
Glossary 695
Key Formulas 696
Supplementary Exercises 698
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

xix


xx

Contents

Transformations Involving the Dependent Variable 720
Nonlinear Models That Are Intrinsically Linear 724
16.2 Determining When to Add or Delete Variables 729
General Case 730
Use of p-Values 732
16.3 Analysis of a Larger Problem 735
16.4 Variable Selection Procedures 739
Stepwise Regression 739
Forward Selection 740
Backward Elimination 741
Best-Subsets Regression 741
Making the Final Choice 742
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

Chapter 17 Index Numbers 763
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 771
Producer Price Index 771
Dow Jones Averages 772
17.5 Deflating a Series by Price Indexes 773
17.6 Price Indexes: Other Considerations 777
Selection of Items 777
Selection of a Base Period 777
Quality Changes 777
17.7 Quantity Indexes 778
Summary 780


Contents

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
Horizontal Pattern 786
Trend Pattern 788
Seasonal Pattern 788
Trend and Seasonal Pattern 789
Cyclical Pattern 789
Selecting a Forecasting Method 791
18.2 Forecast Accuracy 792
18.3 Moving Averages and Exponential Smoothing 797
Moving Averages 797
Weighted Moving Averages 800
Exponential Smoothing 800
18.4 Trend Projection 807
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
Seasonal Adjustments 836
Models Based on Monthly Data 837
Cyclical Component 837
Summary 839
Glossary 840
Key Formulas 841
Supplementary Exercises 842
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

xxi


xxii

Contents

Chapter 19 Nonparametric Methods 855
Statistics in Practice: West Shell Realtors 856
19.1 Sign Test 857
Hypothesis Test About a Population Median 857
Hypothesis 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 901

Chapter 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 906
ISO 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
R Chart 915
p Chart 917
np Chart 919
Interpretation of Control Charts 920
20.3 Acceptance Sampling 922
KALI, Inc.: An Example of Acceptance Sampling 924
Computing the Probability of Accepting a Lot 924
Selecting an Acceptance Sampling Plan 928
Multiple 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


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