Introduction to business statistics (7th edition) by m weiers

a = right-tail area. (e.g., for
a right-tail area of 0.025 and
d.f. = 15, the t value is 2.131.)

0

␣:
d.f. ϭ 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

The t-Distribution

t

0.10

0.05

0.025

0.01

3.078
1.886
1.638
1.533
1.476
1.440
1.415
1.397
1.383
1.372
1.363
1.356
1.350
1.345
1.341
1.337
1.333
1.330
1.328
1.325
1.323
1.321
1.319
1.318
1.316

1.315
1.314
1.313
1.311
1.310
1.309
1.309
1.308
1.307
1.306
1.306
1.305
1.304
1.304
1.303
1.303
1.302
1.302
1.301
1.301

6.314
2.920
2.353
2.132
2.015
1.943
1.895
1.860
1.833

1.812
1.796
1.782
1.771
1.761
1.753
1.746
1.740
1.734
1.729
1.725
1.721
1.717
1.714
1.711
1.708
1.706
1.703
1.701
1.699
1.697
1.696
1.694
1.692
1.691
1.690
1.688
1.687
1.686
1.685

1.684
1.683
1.682
1.681
1.680
1.679

12.706
4.303
3.182
2.776
2.571
2.447
2.365
2.306
2.262
2.228
2.201
2.179
2.160
2.145
2.131
2.120
2.110
2.101
2.093
2.086
2.080
2.074
2.069

2.064
2.060
2.056
2.052
2.048
2.045
2.042
2.040
2.037
2.035
2.032
2.030
2.028
2.026
2.024
2.023
2.021
2.020
2.018
2.017
2.015
2.014

31.821
6.965
4.541
3.747
3.365
3.143
2.998

2.896
2.821
2.764
2.718
2.681
2.650
2.624
2.602
2.583
2.567
2.552
2.539
2.528
2.518
2.508
2.500
2.492
2.485
2.479
2.473
2.467
2.462
2.457
2.453
2.449
2.445
2.441
2.438
2.435
2.431

2.429
2.426
2.423
2.421
2.418
2.416
2.414
2.412

0.005
63.657
9.925
5.841
4.604
4.032
3.707
3.499
3.355
3.250
3.169
3.106
3.055
3.012
2.977
2.947
2.921
2.898
2.878
2.861
2.845

2.831
2.819
2.807
2.797
2.787
2.779
2.771
2.763
2.756
2.750
2.744
2.738
2.733
2.728
2.724
2.719
2.715
2.712
2.708
2.704
2.701
2.698
2.695
2.692
2.690


␣:

0.10

0.05

0.025

0.01

0.005

d.f. ϭ 46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66

67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96

97
98
99
100
ϱ

1.300
1.300
1.299
1.299
1.299
1.298
1.298
1.298
1.297
1.297
1.297
1.297
1.296
1.296
1.296
1.296
1.295
1.295
1.295
1.295
1.295
1.294
1.294
1.294

1.294
1.294
1.293
1.293
1.293
1.293
1.293
1.293
1.292
1.292
1.292
1.292
1.292
1.292
1.292
1.292
1.291
1.291
1.291
1.291
1.291
1.291
1.291
1.291
1.291
1.291
1.290
1.290
1.290
1.290

1.290
1.282

1.679
1.678
1.677
1.677
1.676
1.675
1.675
1.674
1.674
1.673
1.673
1.672
1.672
1.671
1.671
1.670
1.670
1.669
1.669
1.669
1.668
1.668
1.668
1.667
1.667
1.667
1.666

1.666
1.666
1.665
1.665
1.665
1.665
1.664
1.664
1.664
1.664
1.663
1.663
1.663
1.663
1.663
1.662
1.662
1.662
1.662
1.662
1.661
1.661
1.661
1.661
1.661
1.661
1.660
1.660
1.645

2.013
2.012
2.011
2.010
2.009
2.008
2.007
2.006
2.005
2.004
2.003
2.002
2.002
2.001
2.000
2.000
1.999
1.998
1.998
1.997
1.997
1.996
1.995
1.995
1.994
1.994
1.993
1.993
1.993
1.992

1.992
1.991
1.991
1.990
1.990
1.990
1.989
1.989
1.989
1.988
1.988
1.988
1.987
1.987
1.987
1.986
1.986
1.986
1.986
1.985
1.985
1.985
1.984
1.984
1.984
1.960

2.410
2.408
2.407

2.405
2.403
2.402
2.400
2.399
2.397
2.396
2.395
2.394
2.392
2.391
2.390
2.389
2.388
2.387
2.386
2.385
2.384
2.383
2.382
2.382
2.381
2.380
2.379
2.379
2.378
2.377
2.376
2.376
2.375

2.375
2.374
2.373
2.373
2.372
2.372
2.371
2.371
2.370
2.369
2.369
2.369
2.368
2.368
2.367
2.367
2.366
2.366
2.365
2.365
2.365
2.364
2.326

2.687
2.685
2.682
2.680
2.678
2.676

2.674
2.672
2.670
2.668
2.667
2.665
2.663
2.662
2.660
2.659
2.658
2.656
2.655
2.654
2.652
2.651
2.650
2.649
2.648
2.647
2.646
2.645
2.644
2.643
2.642
2.641
2.640
2.640
2.639
2.638

2.637
2.636
2.636
2.635
2.634
2.634
2.633
2.632
2.632
2.631
2.630
2.630
2.629
2.629
2.628
2.627
2.627
2.626
2.626
2.576

Source: t-values generated by Minitab, then rounded to three decimal places.


Computer
Printouts and Instructions
Solutions
for Excel and Minitab
Visual Description
2.1 The Histogram

2.2 The Stem-And-Leaf Display*
2.3 The Dotplot
2.4 The Bar Chart
2.5 The Line Chart
2.6 The Pie Chart
2.7 The Scatter Diagram
2.8 The Cross-Tabulation
2.9 Cross-Tabulation with
Cell Summary Information
Statistical Description
3.1 Descriptive Statistics: Central
Tendency
3.2 Descriptive Statistics: Dispersion
3.3 The Box Plot*
3.4 Standardizing the Data
3.5 Coefficient of Correlation
Sampling
4.1 Simple Random Sampling
Discrete Probability Distributions
6.1 Binomial Probabilities
6.2 Hypergeometric Probabilities
6.3 Poisson Probabilities
6.4 Simulating Observations
From a Discrete Probability
Distribution
Continuous Probability Distributions
7.1 Normal Probabilities
7.2 Inverse Normal Probabilities
7.3 Exponential Probabilities
7.4 Inverse Exponential Probabilities

7.5 Simulating Observations From a
Continuous Probability Distribution
Sampling Distributions
8.1 Sampling Distributions and Computer
Simulation
Confidence Intervals
9.1 Confidence Interval For Population
Mean, ␴ Known*
9.2 Confidence Interval For Population
Mean, ␴ Unknown*
9.3 Confidence Interval For Population
Proportion*
9.4 Sample Size Determination
Hypothesis Tests: One Sample
10.1 Hypothesis Test For Population
Mean, ␴ Known*
10.2 Hypothesis Test For Population
Mean, ␴ Unknown*

Page
21
26
27
29
30
32
40
45
46

65
75
77
81
88
122
180
185
191
195
219
220
230
232
234

260

279
286
290
297

Computer
Printouts and Instructions
Solutions
for Excel and Minitab
Page
10.3 Hypothesis Test For Population
Proportion*

342
10.4 The Power Curve For A Hypothesis Test 352
Hypothesis Tests: Comparing Two Samples
11.1 Pooled-Variances t-Test for (␮1 Ϫ ␮2),
Population Variances Unknown but
Assumed Equal
11.2 Unequal-Variances t-Test for (␮1 Ϫ ␮2),
Population Variances Unknown and
Not Equal
11.3 The z-Test for (␮1 Ϫ ␮2)
11.4 Comparing the Means of Dependent
Samples
11.5 The z-Test for Comparing Two
Sample Proportions*
11.6 Testing for the Equality of
Population Variances
Analysis of Variance
12.1 One-Way Analysis of Variance
12.2 Randomized Block Analysis of Variance
12.3 Two-Way Analysis of Variance
Chi-Square Applications
13.1 Chi-Square Test for Goodness of Fit
13.2 Chi-Square Goodness-of-Fit Test
for Normality*
13.3 Chi-Square Test for Independence of
Variables*
13.4 Chi-Square Test Comparing Proportions
From Independent Samples*
13.5 Confidence Interval for a Population
Variance

13.6 Hypothesis Test for a Population
Variance
Nonparametric Methods
14.1 Wilcoxon Signed Rank Test for
One Sample*
14.2 Wilcoxon Signed Rank Test for
Comparing Paired Samples*
14.3 Wilcoxon Rank Sum Test for Two
Independent Samples*
14.4 Kruskal-Wallis Test for Comparing
More Than Two Independent Samples*
14.5 Friedman Test for the Randomized
Block Design*
14.6 Sign Test for Comparing Paired
Samples*
14.7 Runs Test for Randomness
14.8 Kolmogorov-Smirnov Test for Normality
14.9 Spearman Coefficient of Rank
Correlation*

371
377
383
388
393
399
424
438
453
475

477
483
488
494
495

512
515
520
524
529
534
538
541
543

326
335

Simple Linear Regression
15.1 Simple Linear Regression

558


Computer
Printouts and Instructions
Solutions
for Excel and Minitab
15.2 Interval Estimation in Simple Linear

Regression*
15.3 Coefficient of Correlation
15.4 Residual Analysis
Multiple Regression
16.1 Multiple Regression
16.2 Interval Estimation in Multiple
Regression*
16.3 Residual Analysis in Multiple
Regression
Model Building
17.1 Fitting a Polynomial Regression
Equation, One Predictor Variable
17.2 Fitting a Polynomial Regression
Equation, Two Predictor Variables
17.3 Multiple Regression With Qualitative
Predictor Variables
17.4 Transformation of the Multiplicative
Model

Page
565
570
580
606
613
627

649
656
661

665

Computer
Printouts and Instructions
Solutions
for Excel and Minitab
17.5 The Correlation Matrix
17.6 Stepwise Regression*

Page
668
671

Models for Time Series and Forecasting
18.1 Fitting a Linear or Quadratic Trend
Equation
18.2 Centered Moving Average For
Smoothing a Time Series
18.3 Excel Centered Moving Average Based
On Even Number of Periods
18.4 Exponentially Smoothing a Time Series
18.5 Determining Seasonal Indexes*
18.6 Forecasting With Exponential Smoothing
18.7 Durbin-Watson Test for Autocorrelation*
18.8 Autoregressive Forecasting

696
699
706
710

720
723

Statistical Process Control
20.1 Mean Chart*
20.2 Range Chart*
20.3 p-Chart*
20.4 c-Chart

780
781
789
792

691
694

* Data Analysis Plus™ 7.0 add-in

Seeing Statistics Applets
Applet
1
2
3
4
5
6
7
8
9

10
11
12
13
14
15
16
17
18
19
20
21
Key Item

Title
Influence of a Single Observation on the Median
Scatter Diagrams and Correlation
Sampling
Size and Shape of Normal Distribution
Normal Distribution Areas
Normal Approximation to Binomial Distribution
Distribution of Means—Fair Dice
Distribution of Means—Loaded Dice
Confidence Interval Size
Comparing the Normal and Student t Distributions
Student t Distribution Areas
z-Interval and Hypothesis Testing
Statistical Power of a Test
Distribution of Difference Between Sample Means
F Distribution

Interaction Graph in Two-Way ANOVA
Chi-Square Distribution
Regression: Point Estimate for y
Point-Insertion Scatter Diagram and Correlation
Regression Error Components
Mean Control Chart

Text Section
3.2
3.6
4.6
7.2
7.3
7.4
8.3
8.3
9.4
9.5
9.5
10.4
10.7
11.4
12.3
12.5
13.2
15.2
15.4
15.4
20.7

Applet Page
99
100
132
241
242
243
268
269
309
310
310
362
363
410
464
465
504
597
598
599
805

Location

Computer setup and notes
Follows preface
t-table
Precedes z-table
z-table

Inside rear cover
Other printed tables
Appendix A
Selected odd answers
Appendix B
Seeing Statistics applets, Thorndike video units, case and exercise data sets,
Excel worksheet templates, and Data Analysis PlusTM 7.0 Excel add-in software
with accompanying workbooks, including Test Statistics and Estimators,
Online Chapter 21, appendices, and additional support
/>

INTRODUCTION TO

ST
A
S
T
S
I
E
STIC
N
I
S
S
BU
7E

Ronald M. Weiers
Eberly College of Business and Information Technology

Indiana University of Pennsylvania
and
H. John Heinz III College
Carnegie Mellon University
WITH BUSINESS CASES BY

J. Brian Gray
University of Alabama
Lawrence H. Peters
Texas Christian University

Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States


Introduction to Business Statistics, Seventh
Edition
Ronald M. Weiers
Vice President of Editorial, Business:
Jack W. Calhoun
Publisher: Joe Sabatino
Sr. Acquisitions Editor: Charles McCormick, Jr.
Developmental Editor: Elizabeth Lowry and
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Printed in the United States of America
1 2 3 4 5 6 7 13 12 11 10


To Connor, Madeleine, Hugh, Christina, Aidan,
Mitchell, Owen, Emmett, Mr. Barney Jim,
and
With loving memories of our wonderful son, Bob,
who is swimming with the dolphins off Ocracoke Island


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CONTENT
F
E
I
S
BR

Part 1: Business Statistics: Introduction and Background
1. A Preview of Business Statistics 1
2. Visual Description of Data 15
3. Statistical Description of Data 57
4. Data Collection and Sampling Methods 101

Part 2: Probability
5. Probability: Review of Basic Concepts 133
6. Discrete Probability Distributions 167
7. Continuous Probability Distributions 205

Part 3: Sampling Distributions and Estimation
8. Sampling Distributions 244
9. Estimation from Sample Data 270

Part 4: Hypothesis Testing
10. Hypothesis Tests Involving a Sample Mean or Proportion 311
11. Hypothesis Tests Involving Two Sample Means or Proportions 364
12. Analysis of Variance Tests 411
13. Chi-Square Applications 467
14. Nonparametric Methods 505

Part 5: Regression, Model Building, and Time Series
15. Simple Linear Regression and Correlation 551
16. Multiple Regression and Correlation 600
17. Model Building 644
18. Models for Time Series and Forecasting 687

Part 6: Special Topics
19. Decision Theory 737

20. Total Quality Management 758
21. Ethics in Statistical Analysis and Reporting (Online Chapter)

Appendices
A. Statistical Tables A-1
B. Selected Answers B-1
Index/Glossary I-1
v


TENTS
N
O
C
PART 1: BUSINESS STATISTICS: INTRODUCTION AND BACKGROUND
Chapter 1: A Preview of Business Statistics

1

1.1 Introduction

2

1.2 Statistics: Yesterday and Today

3

1.3 Descriptive Versus Inferential Statistics

5

1.4 Types of Variables and Scales of Measurement

8

1.5 Statistics in Business Decisions

11

1.6 Business Statistics: Tools Versus Tricks

11

1.7 Summary

12

Chapter 2: Visual Description of Data
2.1 Introduction

15
16

2.2 The Frequency Distribution and the Histogram

16

2.3 The Stem-and-Leaf Display and the Dotplot

24

2.4 Other Methods for Visual Representation of the Data

28

2.5 The Scatter Diagram

37

2.6 Tabulation, Contingency Tables, and the Excel PivotTable

42

2.7 Summary

48

Integrated Case: Thorndike Sports Equipment (Meet the Thorndikes:
See Video Unit One.)

53

Integrated Case: Springdale Shopping Survey

54

Chapter 3: Statistical Description of Data
3.1 Introduction

57

58

3.2 Statistical Description: Measures of Central Tendency

59

3.3 Statistical Description: Measures of Dispersion

67

3.4 Additional Dispersion Topics

77

3.5 Descriptive Statistics from Grouped Data

83

3.6 Statistical Measures of Association

86

3.7 Summary

90

Integrated Case: Thorndike Sports Equipment

96

Integrated Case: Springdale Shopping Survey

97

Business Case: Baldwin Computer Sales (A)

97

Seeing Statistics Applet 1: Influence of a Single Observation on the Median

99

Seeing Statistics Applet 2: Scatter Diagrams and Correlation
vi

100


Contents

vii

Chapter 4: Data Collection and Sampling Methods
4.1 Introduction

101
102

4.2 Research Basics

102

4.3 Survey Research

105

4.4 Experimentation and Observational Research

109

4.5 Secondary Data

112

4.6 The Basics of Sampling

117

4.7 Sampling Methods

119

4.8 Summary

127

Integrated Case: Thorndike Sports Equipment—Video Unit Two

131

Seeing Statistics Applet 3: Sampling

132

PART 2: PROBABILITY
Chapter 5: Probability: Review of Basic Concepts

133

5.1 Introduction

134

5.2 Probability: Terms and Approaches

135

5.3 Unions and Intersections of Events

140

5.4 Addition Rules for Probability

143

5.5 Multiplication Rules for Probability

146

5.6 Bayes’ Theorem and the Revision of Probabilities

150

5.7 Counting: Permutations and Combinations

156

5.8 Summary

160

Integrated Case: Thorndike Sports Equipment

165

Integrated Case: Springdale Shopping Survey

166

Business Case: Baldwin Computer Sales (B)

166

Chapter 6: Discrete Probability Distributions
6.1 Introduction

167
168

6.2 The Binomial Distribution

175

6.3 The Hypergeometric Distribution

183

6.4 The Poisson Distribution

187

6.5 Simulating Observations from a Discrete Probability Distribution

194

6.6 Summary

199

Integrated Case: Thorndike Sports Equipment

203

Chapter 7: Continuous Probability Distributions
7.1 Introduction

205
206

7.2 The Normal Distribution

208

7.3 The Standard Normal Distribution

212

7.4 The Normal Approximation to the Binomial Distribution

223

7.5 The Exponential Distribution

228


viii

Contents
7.6 Simulating Observations from a Continuous Probability Distribution

233

7.7 Summary

235

Integrated Case: Thorndike Sports Equipment
(Corresponds to Thorndike Video Unit Three)

240

Integrated Case: Thorndike Golf Products Division

240

Seeing Statistics Applet 4: Size and Shape of Normal Distribution

241

Seeing Statistics Applet 5: Normal Distribution Areas

242

Seeing Statistics Applet 6: Normal Approximation to Binomial Distribution

243

PART 3: SAMPLING DISTRIBUTIONS AND ESTIMATION
Chapter 8: Sampling Distributions

244

8.1 Introduction

245

8.2 A Preview of Sampling Distributions

245

8.3 The Sampling Distribution of the Mean

248

8.4 The Sampling Distribution of the Proportion

254

8.5 Sampling Distributions When the Population Is Finite

257

8.6 Computer Simulation of Sampling Distributions

259

8.7 Summary

262

Integrated Case: Thorndike Sports Equipment

266

Seeing Statistics Applet 7: Distribution of Means: Fair Dice

268

Seeing Statistics Applet 8: Distribution of Means: Loaded Dice

269

Chapter 9: Estimation from Sample Data
9.1 Introduction

270
271

9.2 Point Estimates

272

9.3 A Preview of Interval Estimates

273

9.4 Confidence Interval Estimates for the Mean: ␴ Known

276

9.5 Confidence Interval Estimates for the Mean: ␴ Unknown

281

9.6 Confidence Interval Estimates for the Population Proportion

288

9.7 Sample Size Determination

293

9.8 When the Population Is Finite

298

9.9 Summary

302

Integrated Case: Thorndike Sports Equipment (Thorndike Video Unit Four)

307

Integrated Case: Springdale Shopping Survey

308

Seeing Statistics Applet 9: Confidence Interval Size

309

Seeing Statistics Applet 10: Comparing the Normal and Student t Distributions

310

Seeing Statistics Applet 11: Student t Distribution Areas

310

PART 4: HYPOTHESIS TESTING
Chapter 10: Hypothesis Tests Involving a Sample Mean
or Proportion

311

10.1 Introduction

312


Contents

ix

10.2 Hypothesis Testing: Basic Procedures

317

10.3 Testing a Mean, Population Standard Deviation Known

320

10.4 Confidence Intervals and Hypothesis Testing

329

10.5 Testing a Mean, Population Standard Deviation Unknown

330

10.6 Testing a Proportion

338

10.7 The Power of a Hypothesis Test

346

10.8 Summary

354

Integrated Case: Thorndike Sports Equipment

359

Integrated Case: Springdale Shopping Survey

360

Business Case: Pronto Pizza (A)

361

Seeing Statistics Applet 12: z-Interval and Hypothesis Testing

362

Seeing Statistics Applet 13: Statistical Power of a Test

363

Chapter 11: Hypothesis Tests Involving Two Sample
Means or Proportions
11.1 Introduction

364
365

11.2 The Pooled-Variances t-Test for Comparing the

Means of Two Independent Samples

366

11.3 The Unequal-Variances t-Test for Comparing the

Means of Two Independent Samples

374

11.4 The z-Test for Comparing the Means of Two

Independent Samples

380

11.5 Comparing Two Means When the Samples Are Dependent

385

11.6 Comparing Two Sample Proportions

391

11.7 Comparing the Variances of Two Independent Samples

397

11.8 Summary

401

Integrated Case: Thorndike Sports Equipment

407

Integrated Case: Springdale Shopping Survey

407

Business Case: Circuit Systems, Inc. (A)

408

Seeing Statistics Applet 14: Distribution of Difference Between Sample Means

410

Chapter 12: Analysis of Variance Tests
12.1 Introduction

411
412

12.2 Analysis of Variance: Basic Concepts

412

12.3 One-Way Analysis of Variance

416

12.4 The Randomized Block Design

429

12.5 Two-Way Analysis of Variance

441

12.6 Summary

457

Integrated Case: Thorndike Sports Equipment (Video Unit Six)

462

Integrated Case: Springdale Shopping Survey

462

Business Case: Fastest Courier in the West

463

Seeing Statistics Applet 15: F Distribution and ANOVA

464

Seeing Statistics Applet 16: Interaction Graph in Two-Way ANOVA

465


x

Contents

Chapter 13: Chi-Square Applications
13.1 Introduction

467
468

13.2 Basic Concepts in Chi-Square Testing

468

13.3 Tests for Goodness of Fit and Normality

471

13.4 Testing the Independence of Two Variables

479

13.5 Comparing Proportions from k Independent Samples

486

13.6 Estimation and Tests Regarding the Population Variance

489

13.7 Summary

497

Integrated Case: Thorndike Sports Equipment

502

Integrated Case: Springdale Shopping Survey

503

Business Case: Baldwin Computer Sales (C)

503

Seeing Statistics Applet 17: Chi-Square Distribution

504

Chapter 14: Nonparametric Methods

505

14.1 Introduction

506

14.2 Wilcoxon Signed Rank Test for One Sample

508

14.3 Wilcoxon Signed Rank Test for Comparing Paired Samples

513

14.4 Wilcoxon Rank Sum Test for Comparing Two

Independent Samples

517

14.5 Kruskal-Wallis Test for Comparing More Than

Two Independent Samples

521

14.6 Friedman Test for the Randomized Block Design

525

14.7 Other Nonparametric Methods

530

14.8 Summary

545

Integrated Case: Thorndike Sports Equipment

549

Business Case: Circuit Systems, Inc. (B)

550

PART 5: REGRESSION, MODEL BUILDING, AND TIME SERIES
Chapter 15: Simple Linear Regression and Correlation

551

15.1 Introduction

552

15.2 The Simple Linear Regression Model

553

15.3 Interval Estimation Using the Sample Regression Line

561

15.4 Correlation Analysis

567

15.5 Estimation and Tests Regarding the Sample Regression Line

572

15.6 Additional Topics in Regression and Correlation Analysis

578

15.7 Summary

587

Integrated Case: Thorndike Sports Equipment

595

Integrated Case: Springdale Shopping Survey

596

Business Case: Pronto Pizza (B)

596

Seeing Statistics Applet 18: Regression: Point Estimate for y

597

Seeing Statistics Applet 19: Point Insertion Diagram and Correlation

598

Seeing Statistics Applet 20: Regression Error Components

599


Contents

xi

Chapter 16: Multiple Regression and Correlation
16.1 Introduction

600
601

16.2 The Multiple Regression Model

602

16.3 Interval Estimation in Multiple Regression

609

16.4 Multiple Correlation Analysis

615

16.5 Significance Tests in Multiple Regression and Correlation

617

16.6 Overview of the Computer Analysis and Interpretation

622

16.7 Additional Topics in Multiple Regression and Correlation

632

16.8 Summary

634

Integrated Case: Thorndike Sports Equipment

639

Integrated Case: Springdale Shopping Survey

640

Business Case: Easton Realty Company (A)

641

Business Case: Circuit Systems, Inc. (C)

643

Chapter 17: Model Building

644

17.1 Introduction

645

17.2 Polynomial Models with One Quantitative

Predictor Variable

645

17.3 Polynomial Models with Two Quantitative

Predictor Variables

653

17.4 Qualitative Variables

658

17.5 Data Transformations

663

17.6 Multicollinearity

667

17.7 Stepwise Regression

670

17.8 Selecting a Model

676

17.9 Summary

677

Integrated Case: Thorndike Sports Equipment

681

Integrated Case: Fast-Growing Companies

681

Business Case: Westmore MBA Program

682

Business Case: Easton Realty Company (B)

685

Chapter 18: Models for Time Series and Forecasting

687

18.1 Introduction

688

18.2 Time Series

688

18.3 Smoothing Techniques

693

18.4 Seasonal Indexes

702

18.5 Forecasting

708

18.6 Evaluating Alternative Models: MAD and MSE

713

18.7 Autocorrelation, The Durbin-Watson Test, and

Autoregressive Forecasting

715

18.8 Index Numbers

724

18.9 Summary

729

Integrated Case: Thorndike Sports Equipment (Video Unit Five)

735


xii

Contents

PART 6: SPECIAL TOPICS
Chapter 19: Decision Theory

737

19.1 Introduction

738

19.2 Structuring the Decision Situation

738

19.3 Non-Bayesian Decision Making

742

19.4 Bayesian Decision Making

745

19.5 The Opportunity Loss Approach

749

19.6 Incremental Analysis and Inventory Decisions

751

19.7 Summary

754

Integrated Case: Thorndike Sports Equipment (Video Unit Seven)

757

Online Appendix to Chapter 19: The Expected Value of Imperfect Information

Chapter 20: Total Quality Management

758

20.1 Introduction

759

20.2 A Historical Perspective and Defect Detection

762

20.3 The Emergence of Total Quality Management

763

20.4 Practicing Total Quality Management

765

20.5 Some Statistical Tools for Total Quality Management

770

20.6 Statistical Process Control: The Concepts

774

20.7 Control Charts for Variables

776

20.8 Control Charts for Attributes

786

20.9 Additional Statistical Process Control and

Quality Management Topics

795

20.10 Summary

799

Integrated Case: Thorndike Sports Equipment

803

Integrated Case: Willard Bolt Company

804

Seeing Statistics Applet 21: Mean Control Chart

805

Appendix A: Statistical Tables
Appendix B: Selected Answers

A-1
B-1

Index/Glossary

Online Chapter 21: Ethics in Statistical Analysis and Reporting

I-1


P R E FA C E

Philosophies and Goals of the Text:
A Message to the Student
A book is a very special link between author and reader. In a mystery novel, the
author presents the reader with a maze of uncertainty, perplexity, and general
quicksand. Intellectual smokescreens are set up all the way to the “whodunit”
ending. Unfortunately, many business statistics texts seem to be written the same
way—except for the “whodunit” section. This text is specifically designed to be
different. Its goals are: (1) to be a clear and friendly guide as you learn about
business statistics, (2) to avoid quicksand that could inhibit either your interest or
your learning, and (3) to earn and retain your trust in our ability to accomplish
goals 1 and 2.
Business statistics is not only relevant to your present academic program, it is
also relevant to your future personal and professional life. As a citizen, you will
be exposed to, and perhaps may even help generate, statistical descriptions and
analyses of data that are of vital importance to your local, state, national, and
world communities. As a business professional, you will constantly be dealing
with statistical measures of performance and success, as well as with employers
who will expect you to be able to utilize the latest statistical techniques and computer software tools—including spreadsheet programs like Excel and statistical
software packages like Minitab—in working with these measures.
The chapters that follow are designed to be both informal and informative,
as befits an introductory text in business statistics. You will not be expected to
have had mathematical training beyond simple algebra, and mathematical symbols and notations will be explained as they become relevant to our discussion.
Following an introductory explanation of the purpose and the steps involved in
each technique, you will be provided with several down-to-earth examples of
its use. Each section has a set of exercises based on the section contents. At the
end of each chapter you’ll find a summary of what you’ve read and a listing of
equations that have been introduced, as well as chapter exercises, an interesting
minicase or two, and in most of the chapters—a realistic business case to help
you practice your skills.

Features New to the Seventh Edition
Data Analysis PlusTM 7.0
The Seventh Edition makes extensive use of Data Analysis PlusTM 7.0, an
updated version of the outstanding add-in that enables Microsoft Excel to carry
out practically all of the statistical tests and procedures covered in the text. This
excellent software is easy to use, and is available on the premium website that
accompanies this text.
xiii


xiv

Preface

The Test Statistics and Estimators Workbooks
The Excel workbooks Test Statistics and Estimators accompany and are an important complement to Data Analysis PlusTM 7.0. These workbooks enable Excel
users to quickly perform statistical tests and interval-estimation procedures by
simply entering the relevant summary statistics. The workbooks are terrific for
solving exercises, checking solutions, and especially for playing “what-if” by trying different inputs to see how they would affect the results. These workbooks,
along with Beta-mean and three companion workbooks to determine the power
of a hypothesis test, accompany Data Analysis PlusTM 7.0 and are also available
on the premium website at www.cengage.com/bstats/weiers.

Updated Set of 82 Computer Solutions Featuring
Complete Printouts and Step-By-Step Instructions
for Obtaining Them
Featuring the very latest versions of both Excel and Minitab—Excel 2007 and
Minitab 16, respectively—these pieces are located in most of the major sections
of the book. Besides providing relevant computer printouts for most of the text
examples, they are accompanied by friendly step-by-step instructions written in

plain English.

Updated Exercises and Content
The Seventh Edition includes a total of nearly 1600 section and chapter exercises,
and more than 300 of them are new or updated. Altogether, there are about 1800
chapter, case, and applet exercises, with about 450 data sets for greater ease and
convenience in using the computer. The datasets are in Excel, Minitab, and other
popular formats, and are available on the text’s premium website. Besides numerous new or updated chapter examples, vignettes, and Statistics in Action items,
Chapter 20 (Total Quality Management) has been expanded to include coverage
of Process Capability indices and measurement. In response to user preferences
and for greater ease of use, the normal distribution table is now cumulative, and
it is conveniently located on the rear endsheet of the text.

Continuing Features of Introduction
to Business Statistics
Chapter-Opening Vignettes and Statistics In
Action Items
Each chapter begins with a vignette that’s both interesting and relevant to the
material ahead. Within each chapter, there are also Statistics In Action items that
provide further insights into the issues and applications of business statistics in
the real world. They include a wide range of topics, including using the consumer
price index to time-travel to the (were they really lower?) prices in days gone by,
and surprisingly-relevant discussion of an odd little car in which the rear passengers faced to the rear. Some of the vignette and Statistics in Action titles:
Get That Cat off the Poll! (p. 116)
Proportions Testing and the Restroom Police (p. 467)
Time-Series-Based Forecasting and the Zündapp (p. 687)
Probabilities, Stolen Lawn Mowers, and the Chance of Rain (p. 138)


Preface

The CPI Time Machine (p. 728)
A Sample of Sampling By Giving Away Samples (p. 126)
Gender Stereotypes and Asking for Directions (p. 364)

Extensive Use of Examples and Analogies
The chapters continue to be packed with examples to illustrate the techniques
being discussed. In addition to describing a technique and presenting a smallscale example of its application, we will typically present one or more Excel
and Minitab printouts showing how the analysis can be handled with popular
statistical software. This pedagogical strategy is used so the reader will better
appreciate what’s going on inside the computer when it’s applied to problems of
a larger scale.

The Use of Real Data
The value of statistical techniques becomes more apparent through the consistent
use of real data in the text. Data sets gathered from such publications as USA
Today, Fortune, Newsweek, and The Wall Street Journal are used in more than
400 exercises and examples to make statistics both relevant and interesting.

Computer Relevance
The text includes nearly 200 computer printouts generated by Excel and Minitab,
and the text’s premium website contains data sets for section and chapter exercises, integrated and business cases, and chapter examples. In addition to the new
Data Analysis PlusTM 7.0 software and the handy Test Statistics and Estimators
workbooks that accompany it, the Seventh Edition offers the separate collection
of 26 Excel worksheet templates generated by the author specifically for exercise
solutions and “what-if” analyses based on summary data.

Seeing Statistics Applets
The Seventh Edition continues with the 21 popular interactive java applets,
available at the text’s premium website. Many of these interesting and insightful

applets are customized by their author to specific content and examples in this
textbook, and they include a total of 85 applet exercises. The applets are from the
award-winning Seeing Statistics, authored by Gary McClelland of the University
of Colorado, and they bring life and action to many of the most important
statistical concepts in the text.

Integrated Cases
At the end of each chapter, you’ll find one or both of these case scenarios helpful
in understanding and applying concepts presented within the chapter:
(1) Thorndike Sports Equipment Company
The text continues to follow the saga of Grandfather (Luke) and Grandson (Ted)
Thorndike as they apply chapter concepts to the diverse opportunities, interesting problems, and assorted dilemmas faced by the Thorndike Sports Equipment
Company. At the end of each chapter, the reader has the opportunity to help
Luke and Ted apply statistics to their business. The text’s premium website offers
seven Thorndike video units designed to accompany and reinforce selected written cases. Viewers will find that they enhance the relevance of the cases as well

xv


xvi

Preface

as provide some entertaining background for the Thorndikes’ many statistical
adventures.
(2) Springdale Shopping Survey
The Springdale Shopping Survey cases provide the opportunity to apply chapter
concepts and the computer to real numbers representing the opinions and behaviors of real people in a real community. The only thing that isn’t real is the name
of the community. The entire database contains 30 variables for 150 respondents,
and is available from the premium website accompanying the text.

Business Cases
The Seventh Edition also provides a set of 12 real-world business cases in 10 different chapters of the text. These interesting and relatively extensive cases feature
disguised organizations, but include real data pertaining to real business problems and situations. In each case, the company or organization needs statistical
assistance in analyzing their database to help them make more money, make better decisions, or simply make it to the next fiscal year. The organizations range all
the way from an MBA program, to a real estate agency, to a pizza delivery service,
and these cases and their variants are featured primarily among the chapters in
the latter half of the text. The cases have been adapted from the excellent presentations in Business Cases in Statistical Decision Making, by Lawrence H. Peters,
of Texas Christian University and J. Brian Gray, of the University of Alabama.
Just as answers to problems in the real world are not always simple, obvious,
and straightforward, neither are some of the solutions associated with the real
problems faced by these real (albeit disguised) companies and organizations.
However, in keeping with the “Introduction to …” title of this text, we do provide
a few guidelines in the form of specific questions or issues the student may wish
to address while using business statistics in helping to formulate observations
and recommendations that could be informative or helpful to his or her “client.”

Organization of the Text
The text can be used in either a one-term or a two-term course. For one-term
applications, Chapters 1 through 11 are suggested. For two-term use, it is recommended that the first term include Chapters 1 through 11, and that the second
term include at least Chapters 12 through 18. In either one- or two-term use, the
number and variety of chapters allow for instructor flexibility in designing either
a course or a sequence of courses that will be of maximum benefit to the student.
This flexibility includes the possibility of including one or more of the two remaining chapters, which are in the Special Topics section of the text.
Chapter 1 provides an introductory discussion of business statistics and its
relevance to the real world. Chapters 2 and 3 cover visual summarization methods and descriptive statistics used in presenting statistical information. Chapter 4
discusses popular approaches by which statistical data are collected or generated,
including relevant sampling methods. In Chapters 5 through 7, we discuss the
basic notions of probability and go on to introduce the discrete and continuous probability distributions upon which many statistical analyses depend. In
Chapters 8 and 9, we discuss sampling distributions and the vital topic of making

estimates based on sample findings.
Chapters 10 through 14 focus on the use of sample data to reach conclusions
regarding the phenomena that the data represent. In these chapters, the reader


Preface

will learn how to use statistics in deciding whether to reject statements that have
been made concerning these phenomena. Chapters 15 and 16 introduce methods
for obtaining and using estimation equations in describing how one variable
tends to change in response to changes in one or more others.
Chapter 17 extends the discussion in the two previous chapters to examine
the important issue of model building. Chapter 18 discusses time series, forecasting, and index number concepts used in analyzing data that occur over a
period of time. Chapter 19 discusses the role of statistics in decision theory, while
Chapter 20 explores total quality management and its utilization of statistics.
At the end of the text, there is a combined index and glossary of key terms,
a set of statistical tables, and answers to selected odd exercises. For maximum
convenience, immediately preceding the back cover of the text are pages containing the two statistical tables to which the reader will most often be referring: the
t-distribution and the standard normal, or z-distribution.

Ancillary Items
To further enhance the usefulness of the text, a complete package of complementary ancillary items has been assembled, and they are available at the premium
website accompanying the text:

Student Premium Website
This website available at www.cengage.com/bstats/weiers, contains Data Analysis PlusTM 7.0 Excel add-in software and accompanying workbooks, including
Test Statistics and Estimators; Seeing Statistics applets; datasets for exercises,
cases, and text examples; author-developed Excel worksheet templates for exercise solutions and “what-if” analyses; and the Thorndike Sports Equipment video
cases. Also included, in pdf format, are Chapter 21, Ethics in Statistical Analysis
and Reporting, and the Chapter 19 appendix on the expected value of imperfect

information.

Instructor’s Suite Resource
The Instructor’s Resource CD (IRCD) is available to qualified adopters and contains author-generated complete and detailed solutions to all section, chapter, and
applet exercises, integrated cases and business cases; a test bank in Microsoft Word
format that includes test questions by section; ExamView testing software, which
allows a professor to create exams in minutes; PowerPoint presentations featuring
concepts and examples for each chapter; and a set of display Seeing Statistics
applets based on those in the text and formatted for in-class projection.

Also Available from the Publisher
Available separately from the publisher are other items for enhancing students’
learning experience with the textbook. Among them are the following:

Student Solutions Manual (Weiers)
This manual is author-generated and contains complete, detailed solutions to all
odd-numbered exercises in the text. It can be purchased electronically via Cengage
Brain at www.cengagebrain.com.

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xviii

Preface

Instructor’s Solutions Manual (Weiers)
The Instructor’s Solutions Manual contains author-generated complete and detailed solutions to all section, chapter, and applet exercises, integrated cases and
business cases. It is available to qualified adopters and is in Microsoft Word format
on the password-protected instructor’s website at www.cengage.com/bstats/weiers.

Test Bank (Doug Barrett)
Containing over 2600 test questions, including true-false, multiple-choice, and
problems similar to those at the ends of the sections and chapters of the text, the
computerized Test Bank makes test creation a cinch. The ExamView program is
available from the text’s premium website and on the IRCD.

PPTs: (Priscilla Chaffe-Stengel)
The PowerPoint slides contain the chapter learning outcomes, key terms, theoretical overviews, and practical examples to facilitate classroom instruction and
student learning. The PowerPoint files are available from the text’s premium
website and on the IRCD.

Minitab, Student Version for Windows (Minitab, Inc.)
The student version of this popular statistical software package. Available at a
discount when bundled with the text.

Acknowledgements
Advice and guidance from my colleagues have been invaluable to the generation
of the Seventh Edition, and I would like to thank the following individuals for
their helpful comments and suggestions:
J. Douglas Barrett
University of North Alabama

Linda Leighton
Fordham University

David Bush
Villanova University

Edward Mansfield

University of Alabama

Priscilla Chaffe-Stengel
California State University-Fresno

Elizabeth Mayer
St. Bonaventure University

Fred Dehner
Rivier College

Rich McGowan
Boston College

Jim Ford
University of Delaware

Patricia Mullins
University of Wisconsin

Jeff Grover
Dynamics Research Corporation

Alan Olinsky
Bryant University

Janice Harder
Motlow State Community College

Deborah J. Rumsey

The Ohio State University

Farid Islam
Utah Valley State College

Farhad Saboori
Albright College

Yunus Kathawala
Eastern Illinois University

Dan Shimshak
University of Massachusetts


Preface

xix

Kathy Smith
Carnegie Mellon University

Mark A. Thompson
University of Arkansas at Little Rock

Debra K. Stiver
University of Nevada, Reno

Joseph Van Metre
University of Alabama

I would also like to thank colleagues who were kind enough to serve as reviewers for previous editions of the text: Randy Anderson, California State
University—Fresno; Leland Ash, Yakima Valley Community College; James
O. Flynn, Cleveland State University; Marcelline Fusilier, Northwestern State
University of Louisiana; Thomas Johnson, North Carolina State University; Mark
P. Karscig, Central Missouri State University; David Krueger, Saint Cloud State
University; Richard T. Milam, Jr., Appalachian State University; Erl Sorensen,
Northeastern University; Peter von Allmen, Moravian College: R. C. Baker,
University of Texas-Arlington; Robert Boothe, Memphis State University;
Raymond D. Brown, Drexel University; Shaw K. Chen, University of Rhode
Island; Gary Cummings, Walsh College; Phyllis Curtiss, Bowling Green State
University; Fred Derrick, Loyola College; John Dominguez, University of
Wisconsin—Whitewater; Robert Elrod, Georgia State University; Mohammed A.
El-Saidi, Ferris State University; Stelios Fotopoulos, Washington State University;
Oliver Galbraith, San Diego State University; Patricia Gaynor, University
of Scranton; Edward George, University of Texas—El Paso; Jerry Goldman,
DePaul University; Otis Gooden, Cleveland State University; Deborah Gougeon,
Appalachian State University; Jeffry Green, Ball State University; Irene
Hammerbacher, Iona College; Robert Hannum, University of Denver; Burt Holland,
Temple University; Larry Johnson, Austin Community College; Shimshon
Kinory, Jersey City State College; Ron Koot, Pennsylvania State University;
Douglas Lind, University of Toledo; Subhash Lonial, University of Louisville;
Tom Mathew, Troy State University—Montgomery; John McGovern, Georgian
Court College; Frank McGrath, Iona College; Jeff Mock, Diablo Valley
College; Kris Moore, Baylor University; Ryan Murphy, University of Arizona;
Buddy Myers, Kent State University; Leon Neidleman, San Jose State University;
Julia Norton, California State University—Hayward; C. J. Park, San Diego State
University; Leonard Presby, William Patterson State College; Harry Reinken,
Phoenix College; Vartan Safarian, Winona State University; Sue Schou, Idaho State
University; John Sennetti, Texas Tech University; William A. Shrode, Florida

State University; Lynnette K. Solomon, Stephen F. Austin State University; Sandra
Strasser, Valparaiso State University; Joseph Sukta, Moraine Valley Community
College; J. B. Spaulding, University of Northern Texas; Carol Stamm, Western
Michigan University; Priscilla Chaffe-Stengel, California State University—
Fresno; Stan Stephenson, Southwest Texas State University; Patti Taylor, Angelo
State University; Patrick Thompson, University of Florida—Gainesville;
Russell G. Thompson, University of Houston; Susan Colvin-White, Northwestern
State University; Nancy Williams, Loyola College; Dick Withycombe, University
of Montana; Cliff Young, University of Colorado at Denver; and Mustafa Yilmaz,
Northeastern University.
I would like to thank Vince Taiani for assistance with and permission to
use what is known here as the Springdale Shopping Survey computer database.
Thanks to Minitab, Inc. for the support and technical assistance they have provided. Thanks to Gary McCelland for his excellent collection of applets for this
text, and to Lawrence H. Peters and J. Brian Gray for their outstanding cases


xx

Preface

and the hands-on experience they have provided to the student. Special thanks
to my friend and fellow author Gerry Keller and the producers of Data Analysis
PlusTM 7.0 for their excellent software that has enhanced this edition.
The editorial staff of Cengage Learning is deserving of my gratitude for their
encouragement, guidance, and professionalism throughout what has been an
arduous, but rewarding task. Among those without whom this project would
not have come to fruition are Charles McCormick, Acquisitions Editor; Suzanna
Bainbridge and Elizabeth Lowry, Developmental Editors; Kelly Hillerich, Content
Project Manager; Bill Hendee, Vice President of Marketing; Stacy Shirley, Art
Director; Eleanora Heink, Editorial Assistant; Suellen Ruttkay, Marketing Coordinator, and Libby Shipp, Marketing Communications Manager. In addition,

the world-class editorial skills of Susan Reiland and the detail-orientation of
Dr. Jeff Grover, Dr. Debra Stiver, and Dr. Doug Barrett are greatly appreciated.
Last, but certainly not least, I remain extremely thankful to my family for
their patience and support through seven editions of this work.

Ronald M. Weiers, Ph.D.
Eberly College of Business and Information Technology
Indiana University of Pennsylvania
and
Adjunct, H. John Heinz III College
Carnegie Mellon University