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eighth edition

Business

Statistics

A Decision-Making Approach
D A V I D F. G R O E B N E R
Boise State University, Professor Emeritus of Production Management

PAT R I C K W. S H A N N O N
Boise State University, Dean of the College of Business and Economics

PHILLIP C. FRY
Boise State University, Professor, ITSCM Department Chair

KENT D. SMITH
California Polytechnic University, Professor Emeritus of Statistics


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Library of Congress Cataloging-in-Publication Data
Business statistics : a decision-making approach / David F. Groebner ... [et al.]. — 8th ed.
p. cm.
Includes bibliographical references and index.
ISBN-13: 978-0-13-612101-5
ISBN-10: 0-13-612101-2
1. Commercial statistics. 2. Statistical decision. I. Groebner, David F.
HF1017.G73 2010
519.5—dc22
2009046074

10 9 8 7 6 5 4 3 2 1
ISBN-13: 978-0-13-612101-5
ISBN-10: 0-13-612101-2


To Jane and my family, who survived the process one more time.

David F. Groebner
To Kathy, my wife and best friend; to our children, Jackie and Jason;
and to my parents, John and Ruth Shannon.
Patrick W. Shannon
To my wonderful family: Susan, Alex, Allie, Candace, and Courtney.
Phillip C. Fry
To Dottie, the bright light in my life and to my father who made it all possible.
Kent D. Smith


This page intentionally left blank


About the Authors
David F. Groebner is Professor Emeritus of Production Management in the
College of Business and Economics at Boise State University. He has bachelor’s and master’s degrees in engineering and a Ph.D. in business administration. After working as an
engineer, he has taught statistics and related subjects for 27 years. In addition to writing textbooks and academic papers, he has worked extensively with both small and
large organizations, including Hewlett-Packard, Boise Cascade, Albertson’s, and
Ore-Ida. He has worked with numerous government agencies, including Boise City
and the U.S. Air Force.
Patrick W. Shannon, Ph.D. is Dean and Professor of Supply Chain Operations
Management in the College of Business and Economics at Boise State University. In addition to his
administrative responsibilities, he has taught graduate and undergraduate courses in business statistics, quality management, and production and operations management. In addition, Dr. Shannon has
lectured and consulted in the statistical analysis and quality management areas for over 20 years.
Among his consulting clients are Boise Cascade Corporation; Hewlett-Packard; PowerBar, Inc.;
Potlatch Corporation; Woodgrain Millwork, Inc.; J.R. Simplot Company; Zilog Corporation;
and numerous other public- and private-sector organizations. Professor Shannon has co-authored
several university-level textbooks and has published numerous articles in such journals as Business Horizons,
Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and
Journal of Marketing Research. He obtained B.S. and M.S. degrees from the University of Montana and a

Ph.D. in Statistics and Quantitative Methods from the University of Oregon.
Phillip C. Fry is a Professor in the College of Business and Economics at Boise
State University, where he has taught since 1988. Phil received his B.A. and M.B.A.
degrees from the University of Arkansas, and his M.S. and Ph.D. degrees from
Louisiana State University. His teaching and research interests are in the areas of
business statistics, production management, and quantitative business modeling. In
addition to his academic responsibilities, Phil has consulted with and provided
training to small and large organizations, including Boise Cascade Corporation;
Hewlett-Packard Corporation; The J.R. Simplot Company; United Water of Idaho;
Woodgrain Millwork, Inc.; Boise City; and Micron Electronics.
Phil spends most of his free time with his wife, Susan, and his four children, Phillip Alexander, Alejandra Johanna, and twins Courtney Rene and Candace Marie.
Kent D. Smith received a Ph.D. in Applied Statistics from the University of California, Riverside.
He holds a master of science degree in Statistics from the University of California, Riverside, and a master of science degree in Systems Analysis from the Air Force Institute of Technology. His bachelor of arts
degree in Mathematics was obtained from the University of Utah. Dr. Smith has served as a University Statistical Consultant at the University of California, Riverside, and at California Polytechnic State University, San Luis Obispo. His private consulting has ranged from serving as an
expert witness in legal cases, survey sampling for corporations and private researchers, medical and orthodontic research, and assisting graduate students with analysis required for master and doctoral degrees in various disciplines.
Dr. Smith began teaching as a part-time lecturer at California State University, San Bernardino. While
completing his doctoral dissertation, he served as a lecturer at University of California, Riverside. Currently,
he is Professor Emeritus of Statistics at California Polytechnic State University, San Luis Obispo. Though retired, he still teaches part time at the university. The subjects he teaches include upper-division courses in regression, analysis of variance, linear models, and probability and mathematical statistics, as well as a full array
of service courses.

v


Brief Contents
Chapter 1
Chapter 2
Chapter 3
Chapters 1–3
Chapter 4
Chapter 5

Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapters 8–12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18

The Where, Why, and How of Data Collection 1
Graphs, Charts, and Tables—Describing Your Data 31
Describing Data Using Numerical Measures 85
Special Review Section 139
Using Probability and Probability Distributions 146
Discrete Probability Distributions 191
Introduction to Continuous Probability Distributions 233
Introduction to Sampling Distributions 264
Estimating Single Population Parameters
Introduction to Hypothesis Testing 346

Estimation and Hypothesis Testing for Two Population Parameters
Hypothesis Tests and Estimation for Population Variances 448

397


Analysis of Variance 475
Special Review Section 530
Goodness-of-Fit Tests and Contingency Analysis 547
Introduction to Linear Regression and Correlation Analysis 579
Multiple Regression Analysis and Model Building 633
Analyzing and Forecasting Time-Series Data 709
Introduction to Nonparametric Statistics 770
Introduction to Quality and Statistical Process Control 804
APPENDIX A

Random Numbers Table

APPENDIX B

Binomial Distribution Table

APPENDIX C

Poisson Probability Distribution Table

APPENDIX D

Standard Normal Distribution Table

APPENDIX E

Exponential Distribution Table

APPENDIX F


Values of t for Selected Probabilities 858
Values of ␹2 for Selected Probabilities 859

APPENDIX G
APPENDIX H
APPENDIX I
APPENDIX J
APPENDIX K
APPENDIX L
APPENDIX M
APPENDIX N
APPENDIX O
APPENDIX P
APPENDIX Q

vi

305

837
838
851
856

857

F-Distribution Table 860
Critical Values of Hartley’s Fmax Test 866
Distribution of the Studentized Range (q-values) 867

Critical Values of r in the Runs Test 869
Mann-Whitney U Test Probabilities (n < 9) 870
Mann-Whitney U Test Critical Values (9 Յ n Յ 20) 872
Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n Յ 25) 874
Critical Values dL and dU of the Durbin-Watson Statistic D 875
Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test 877
Control Chart Factors 878


Contents
Preface

xix

Chapter 1

The Where, Why, and How of Data Collection
What is Business Statistics?

1

2

Descriptive Statistics 2
Charts and Graphs 3
Inferential Procedures 5
Estimation 5
Hypothesis Testing 5

Procedures for Collecting Data


7

Data Collection 7
Written Questionnaires and Surveys 9
Direct Observation and Personal Interviews

11

Other Data Collection Methods 11
Data Collection Issues 12
Interviewer Bias 12
Nonresponse Bias 12
Selection Bias 12
Observer Bias 12
Measurement Error 13
Internal Validity 13
External Validity 13

Populations, Samples, and Sampling Techniques

14

Populations and Samples 14
Parameters and Statistics 15
Sampling Techniques 15
Statistical Sampling 16

Data Types and Data Measurement Levels


20

Quantitative and Qualitative Data 21
Time-Series Data and Cross-Sectional Data

21

Data Measurement Levels 21
Nominal Data 21
Ordinal Data 22
Interval Data 22
Ratio Data 22

Visual Summary 26

• Key Terms

28 • Chapter Exercises

28

Video Case 1: Statistical Data Collection @ McDonald’s 29
References

Chapter 2

29

Graphs, Charts, and Tables—Describing Your Data
Frequency Distributions and Histograms

Frequency Distribution

31

32

33

Grouped Data Frequency Distributions 36
Steps for Grouping Data into Classes 39
Histograms 41
Issues with Excel 44
Relative Frequency Histograms and Ogives

45

Joint Frequency Distributions 47

Bar Charts, Pie Charts, and Stem and Leaf Diagrams

54

Bar Charts 54
Pie Charts 60
Stem and Leaf Diagrams 62

vii


viii


|

CONTENTS

Line Charts and Scatter Diagrams
Line Charts

66

66

Scatter Diagrams 70
Personal Computers 70

Visual Summary 76 •
Chapter Exercises 77

Equations

77

Key Terms

•

77 •

Video Case 2: Drive-Thru Service Times @ McDonald’s 80
Case 2.1: Server Downtime 81

Case 2.2: Yakima Apples, Inc. 81
Case 2.3: Welco Lumber Company—Part A 83
References

Chapter 3

84

Describing Data Using Numerical Measures
Measures of Center and Location
Parameters and Statistics
Population Mean
Sample Mean

85

86

86

89

The Impact of Extreme Values on the Mean
Median

90

91

Skewed and Symmetric Distributions

Mode

85

92

93

Applying the Measures of Central Tendency 94
Issues with Excel 96
Other Measures of Location
Weighted Mean 97
Percentiles 98
Quartiles 99
Issues with Excel 100
Box and Whisker Plots
Data-Level Issues

100

102

Measures of Variation
Range

97

107

107


Interquartile Range

108

Population Variance and Standard Deviation
Sample Variance and Standard Deviation

109

112

Using the Mean and Standard Deviation Together

118

Coefficient of Variation 118
The Empirical Rule 120
Tchebysheff’s Theorem
Standardized Data Values

Visual Summary 128 •
Chapter Exercises 130

121
122

Equations

129


•

Key Terms

130

•

Video Case 3: Drive-Thru Service Times at McDonald’s 135
Case 3.1: WGI—Human Resources 135
Case 3.2: National Call Center 136
Case 3.3: Welco Lumber Company—Part B 137
Case 3.4: AJ’s Fitness Center 137
References

138

Chapters 1–3 Special Review Section 139
Chapters 1–3 139
Exercises 142
Review Case 1: State Department of Insurance
Term Project Assignments 144

144


CONTENTS

Chapter 4


Introduction to Probability

146

The Basics of Probability 147
Important Probability Terms 147
Events and Sample Space 147
Using Tree Diagrams 148
Mutually Exclusive Events 150
Independent and Dependent Events 150
Methods of Assigning Probability 152
Classical Probability Assessment 152
Relative Frequency Assessment 153
Subjective Probability Assessment 155

The Rules of Probability 159
Measuring Probabilities 159
Possible Values and the Summation of Possible Values
Addition Rule for Individual Outcomes 160
Complement Rule 162
Addition Rule for Two Events 163
Addition Rule for Mutuallly Exclusive Events 167
Conditional Probability 167
Tree Diagrams 170
Conditional Probability for Independent Events
Multiplication Rule 172
Multiplication Rule for Two Events 172
Using a Tree Diagram 173
Multiplication Rule for Independent Events


159

171

174

Bayes’ Theorem 175

Visual Summary 185 • Equations
Chapter Exercises 186

186

Key Terms

•

Case 4.1: Great Air Commuter Service
Case 4.2: Let’s Make a Deal 190
References

Chapter 5

186

•

189


190

Discrete Probability Distributions

191

Introduction to Discrete Probability Distributions
Random Variables 192
Displaying Discrete Probability Distributions Graphically

192

Mean and Standard Deviation of Discrete Distributions
Calculating the Mean 193
Calculating the Standard Deviation 194

The Binomial Probability Distribution
The Binomial Distribution

192

193

199

199

Characteristics of the Binomial Distribution 199
Combinations 201
Binomial Formula 202

Using the Binomial Distribution Table 204
Mean and Standard Deviation of the Binomial Distribution 205
Additional Information about the Binomial Distribution 208

Other Discrete Probability Distributions

213

The Poisson Distribution 213
Characteristics of the Poisson Distribution 213
Poisson Probability Distribution Table 214
The Mean and Standard Deviation of the Poisson Distribution

217

The Hypergeometric Distribution 217
The Hypergeometric Distribution with More Than Two Possible Outcomes per Trial

Visual Summary 226 • Equations
Chapter Exercises 227

227

Case 5.1: SaveMor Pharmacies 230
Case 5.2: Arrowmark Vending 231

•

Key Terms


227

•

222

|

ix


x

|

CONTENTS

Case 5.3: Boise Cascade Corporation
References

Chapter 6

232

232

Introduction to Continuous Probability
Distributions 233
The Normal Probability Distribution
The Normal Distribution


234

234

The Standard Normal Distribution 235
Using the Standard Normal Table 237
Approximate Areas under the Normal Curve 245

Other Continuous Probability Distributions
Uniform Probability Distribution

249

249

The Exponential Probability Distribution

Visual Summary 258 • Equations
• Chapter Exercises 259

252

259

•

Key Terms

259


Case 6.1: State Entitlement Programs 262
Case 6.2: Credit Data, Inc. 263
Case 6.3: American Oil Company 263
References

Chapter 7

263

Introduction to Sampling Distributions
Sampling Error: What It Is and Why It Happens
Calculating Sampling Error 265
The Role of Sample Size in Sampling Error

264
265

268

Sampling Distribution of the Mean

273
Simulating the Sampling Distribution for x– 274
Sampling from Normal Populations 277
The Central Limit Theorem

282

Sampling Distribution of a Proportion

Working with Proportions

289

289

Sampling Distribution of p 291
Visual Summary 298 • Equations
• Chapter Exercises 299

299

•

Key Terms

299

Case 7.1: Carpita Bottling Company 303
Case 7.2: Truck Safety Inspection 303
References

Chapter 8

304

Estimating Single Population Parameters

305


Point and Confidence Interval Estimates for a Population Mean
Point Estimates and Confidence Intervals

Confidence Interval Estimate for the Population Mean, ␴ Known
Confidence Interval Calculation 309
Impact of the Confidence Level on the Interval Estimate 311
Impact of the Sample Size on the Interval Estimate 314

308

Confidence Interval Estimates for the Population Mean, ␴ Unknown
Student’s t-Distribution 314
Estimation with Larger Sample Sizes

306

306

314

320

Determining the Required Sample Size for Estimating a Population
Mean 324
Determining the Required Sample Size for Estimating ␮, ␴ Known
Determining the Required Sample Size for Estimating ␮, ␴ Unknown

Estimating a Population Proportion

325

326

330

Confidence Interval Estimate for a Population Proportion

331

Determining the Required Sample Size for Estimating a Population Proportion

Visual Summary 339 • Equations
• Chapter Exercises 340

340

•

Key Terms

340

333


CONTENTS

Video Case 4: New Product Introductions @ McDonald’s 343
Case 8.1: Management Solutions, Inc. 343
Case 8.2: Federal Aviation Administration 344
Case 8.3: Cell Phone Use 344

References

Chapter 9

345

Introduction to Hypothesis Testing 346
Hypothesis Tests for Means 347
Formulating the Hypotheses 347
Null and Alternative Hypotheses 347
Testing the Status Quo 347
Testing a Research Hypothesis 348
Testing a Claim about the Population 348
Types of Statistical Errors 350
Significance Level and Critical Value

351

Hypothesis Test for ␮, ␴ Known 352
Calculating Critical Values 352
Decision Rules and Test Statistics 354
p-Value Approach 357
Types of Hypothesis Tests 358
p-Value for Two-Tailed Tests 359
Hypothesis Test for ␮, ␴ Unknown 361

Hypothesis Tests for Proportions

368


Testing a Hypothesis about a Single Population Proportion

368

Type II Errors 376
Calculating Beta 376
Controlling Alpha and Beta 378
Power of the Test 382

Visual Summary 387 • Equations
• Chapter Exercises 389

388

•

Key Terms

389

Video Case 4: New Product Introductions @ McDonald’s 394
Case 9.1: Campbell Brewery, Inc.—Part 1 394
Case 9.2: Wings of Fire 395
References

396

Chapter 10 Estimation and Hypothesis Testing for
Two Population Parameters 397
Estimation for Two Population Means Using Independent

Samples 398
Estimating the Difference between Two Population Means when ␴1 and ␴2 Are Known,
Using Independent Samples 398
Estimating the Difference between Two Means when ␴1 and ␴2 Are Unknown, Using
Independent Samples 400
What if the Population Variances Are Not Equal 404

Hypothesis Tests for Two Population Means Using Independent
Samples 409
Testing for ␮1 – ␮2 When ␴1 and ␴2 Are Known, Using Independent
Samples 409
Using p-Values 412
Testing ␮1 – ␮2 When ␴1 and ␴2 Are Unknown, Using Independent
Samples 412
What If the Population Variances are Not Equal? 419

Interval Estimation and Hypothesis Tests for Paired
Samples 423
Why Use Paired Samples? 423
Hypothesis Testing for Paired Samples

427

|

xi


xii


|

CONTENTS

Estimation and Hypothesis Tests for Two Population Proportions 432
Estimating the Difference between Two Population Proportions

432

Hypothesis Tests for the Difference between Two Population Proportions

Visual Summary 440 • Equations
• Chapter Exercises 442

441

•

Key Terms

433

442

Case 10.1: Motive Power Company—Part 1 445
Case 10.2: Hamilton Marketing Services 446
Case 10.3: Green Valley Assembly Company 446
Case 10.4: U-Need-It Rental Agency 447
References


447

Chapter 11 Hypothesis Tests and Estimation for Population
Variances 448
Hypothesis Tests and Estimation for a Single Population
Variance 449
Chi-Square Test for One Population Variance

449

Interval Estimation for a Population Variance

454

Hypothesis Tests for Two Population Variances
F-Test for Two Population Variances 458
Additional F-Test Considerations 467
Visual Summary 470 • Equations 471
• Chapter Exercises 471

•

Key Terms

Case 11.1: Motive Power Company—Part 2
References

458

471


474

474

Chapter 12 Analysis of Variance 475
One-Way Analysis of Variance

476

Introduction to One-Way ANOVA 476
Partitioning the Sum of Squares
The ANOVA Assumptions

477

478

Applying One-Way ANOVA 481
The Tukey-Kramer Procedure for Multiple Comparisons

488

Fixed Effects Versus Random Effects in Analysis of Variance

493

Randomized Complete Block Analysis of Variance

497


Randomized Complete Block ANOVA 497
Was Blocking Necessary? 500
Fisher’s Least Significant Difference Test

505

Two-Factor Analysis of Variance with Replication
Two-Factor ANOVA with Replications
Interaction Explained 512
A Caution about Interaction

509

510

517

Visual Summary 521 • Equations
• Chapter Exercises 522

522

•

Key Terms

522

Video Case 3: Drive-Thru Service Times @ McDonald’s 526

Case 12.1: Agency for New Americans 526
Case 12.2: McLaughlin Salmon Works 527
Case 12.3: NW Pulp and Paper 527
Case 12.4: Quinn Restoration 528
Business Statistics Capstone Project 528
References

529

Chapters 8–12 Special Review Section 530
Chapters 8–12 530
Using the Flow Diagrams
Exercises 544

543


CONTENTS

Term Project Assignments 546
Business Statistics Capstone Project

546

Chapter 13 Goodness-of-Fit Tests and Contingency Analysis
Introduction to Goodness-of-Fit Tests

547

548


Chi-Square Goodness-of-Fit Test 548

Introduction to Contingency Analysis

562

2 ϫ 2 Contingency Tables 562

r ϫ c Contingency Tables 566
Chi-Square Test Limitations 569
Visual Summary 573 • Equations
• Chapter Exercises 574

574

•

Key Term 574

Case 13.1: American Oil Company 577
Case 13.2: Bentford Electronics—Part 1 577
References

578

Chapter 14 Introduction to Linear Regression and Correlation
Analysis 579
Scatter Plots and Correlation


580

The Correlation Coefficient 580
Significance Test for the Correlation 582
Cause-and-Effect Interpretations 586

Simple Linear Regression Analysis

589

The Regression Model and Assumptions

590

Meaning of the Regression Coefficients

591

Least Squares Regression Properties

596

Significance Tests in Regression Analysis

599

The Coefficient of Determination, R 2 600
Significance of the Slope Coefficient 604

Uses for Regression Analysis

Regression Analysis for Description

612
612

Regression Analysis for Prediction 615
Confidence Interval for the Average y, Given x 616
Prediction Interval for a Particular y, Given x 616

Common Problems Using Regression Analysis
Visual Summary 624 • Equations
• Chapter Exercises 626

625

•

618

Key Terms

626

Case 14.1: A & A Industrial Products 630
Case 14.2: Sapphire Coffee—Part 1 630
Case 14.3: Alamar Industries 631
Case 14.4: Continental Trucking 631
References

632


Chapter 15 Multiple Regression Analysis and Model Building
Introduction to Multiple Regression Analysis

634

Basic Model-Building Concepts 636
Model Specification 636
Model Building 637
Model Diagnosis 637
Computing the Regression Equation 640
The Coefficient of Determination 642
Is the Model Significant? 643
Are the Individual Variables Significant? 645
Is the Standard Deviation of the Regression Model Too Large? 646
Is Multicollinearity a Problem? 647
Confidence Interval Estimation for Regression Coefficients 649

633

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xiv

|

CONTENTS


Using Qualitative Independent Variables

654

Possible Improvements to the First City Appraisal Model

Working with Nonlinear Relationships
The Partial-F Test

661

671

Stepwise Regression
Forward Selection

657

678

678

Backward Elimination

679

Standard Stepwise Regression
Best Subsets Regression


683

683

Determining the Aptness of the Model

689

Analysis of Residuals 689
Checking for Linearity 690
Do the Residuals Have Equal Variances at all Levels of Each x Variable?
Are the Residuals Independent? 693
Checking for Normally Distributed Error Terms 693
Corrective Actions

692

697

Visual Summary 700
Exercises 701

Equations

•

701

Key Terms


•

701

•

Chapter

Case 15.1: Dynamic Scales, Inc. 705
Case 15.2: Glaser Machine Works 706
Case 15.3: Hawlins Manufacturing 706
Case 15.4: Sapphire Coffee—Part 2 707
Case 15.5: Wendell Motors 707
References

708

Chapter 16 Analyzing and Forecasting Time-Series Data 709
Introduction to Forecasting, Time-Series Data, and Index
Numbers 710
General Forecasting Issues

710

Components of a Time Series
Trend Component 711
Seasonal Component 712
Cyclical Component 713
Random Component 713


711

Introduction to Index Numbers
Aggregate Price Indexes

714

715

Weighted Aggregate Price Indexes
The Paasche Index 717
The Laspeyres Index 718
Commonly Used Index Numbers
Consumer Price Index 719
Producer Price Index 720
Stock Market Indexes

717

719

720

Using Index Numbers to Deflate a Time Series

Trend-Based Forecasting Techniques
Developing a Trend-Based Forecasting Model

721


724
724

Comparing the Forecast Values to the Actual Data
Autocorrelation 728
True Forecasts 732
Nonlinear Trend Forecasting 734
Some Words of Caution 738
Adjusting for Seasonality 738
Computing Seasonal Indexes 739
The Need to Normalize the Indexes 741
Deseasonalizing 742
Using Dummy Variables to Represent Seasonality

744

727


CONTENTS

Forecasting Using Smoothing Methods

750

Exponential Smoothing 750
Single Exponential Smoothing 750
Double Exponential Smoothing 755

Visual Summary 762

Exercises 764

• Equations

763

Key Terms

•

763

Chapter

•

Video Case 2: Restaurant Location and Re-imaging Decisions @
McDonald’s 766
Case 16.1: Park Falls Chamber of Commerce 767
Case 16.2: The St. Louis Companies 768
Case 16.3: Wagner Machine Works 768
References

769

Chapter 17 Introduction to Nonparametric Statistics 770
The Wilcoxon Signed Rank Test for One Population Median
The Wilcoxon Signed Rank Test—Single Population

771


Nonparametric Tests for Two Population Medians

776

The Mann–Whitney U-Test 776
Mann–Whitney U-Test—Large Samples 780
The Wilcoxon Matched-Pairs Signed Rank Test 782
Ties in the Data 784
Large-Sample Wilcoxon Test 784

Kruskal–Wallis One-Way Analysis of Variance
Limitations and Other Considerations

Visual Summary 797

• Equations

798

Chapter Exercises

•

Case 17.1: Bentford Electronics—Part 2
References

789

793


802

803

Chapter 18 Introduction to Quality and Statistical
Process Control 804
Quality Management and Tools for Process
Improvement 805
The Tools of Quality for Process Improvement
Process Flowcharts 807
Brainstorming 807
Fishbone Diagram 807
Histograms 807
Trend Charts 807
Scatter Plots 807
Statistical Process Control Charts 807

806

Introduction to Statistical Process Control Charts
The Existence of Variation 808
Sources of Variation 808
Types of Variation 809
The Predictability of Variation: Understanding the Normal Distribution
The Concept of Stability 810
Introducing Statistical Process Control Charts
x–-Chart and R-Chart 811
Using the Control Charts 818
p-Charts 820

Using the p-Chart 823
c-Charts 824
Other Control Charts 827
Visual Summary 831 • Equations
• Chapter Exercises 833

832

835

810

810

•

Case 18.1: Izbar Precision Casters, Inc.
References

808

Key Terms

834

833

799

771


|

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Appendices 836
APPENDIX A

Random Numbers Table

APPENDIX B

Binomial Distribution Table

APPENDIX C

Poisson Probability Distribution Table

APPENDIX D

Standard Normal Distribution Table

APPENDIX E


Exponential Distribution Table

837
838
851
856

857

APPENDIX F

Values of t for Selected Probabilities

APPENDIX G

Values of ␹2 for Selected Probabilities

APPENDIX H

F-Distribution Table 860
Critical Values of Hartley’s Fmax Test 866
Distribution of the Studentized Range (q-values) 867
Critical Values of r in the Runs Test 869
Mann-Whitney U Test Probabilities (n < 9) 870
Mann-Whitney U Test Critical Values (9 Յ n Յ 20) 872
Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test
(n Յ 25) 874
Critical Values dL and dU of the Durbin-Watson Statistic D 875
Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test 877

Control Chart Factors 879

APPENDIX I
APPENDIX J
APPENDIX K
APPENDIX L
APPENDIX M
APPENDIX N
APPENDIX O
APPENDIX P
APPENDIX Q

Answers to Selected Odd-Numbered Problems
Glossary 900
Index 906

879

858
859


Preface
In today’s workplace, students can have an immediate competitive edge over both new graduates and experienced employees if they know how to apply statistical analysis skills to realworld decision-making problems.
Our intent in writing Business Statistics: A Decision-Making Approach is to provide an introductory business statistics text for students who do not necessarily have an extensive mathematics background but who need to understand how statistical tools and techniques are applied
in business decision making.
This text differs from its competitors in three key ways:
1. Use of a direct approach and concepts and techniques consistently presented in a systematic and ordered way.
2. Presentation of the content at a level that makes it accessible to students of all levels of
mathematical maturity. The text features clear, step-by-step explanations that make learning business statistics straightforward.

3. Engaging examples, drawn from our years of experience as authors, educators, and consultants, to show the relevance of the statistical techniques in realistic business decision
situations.
Regardless of how accessible or engaging a textbook is, we recognize that many students
do not read the chapters from front to back. Instead, they use the text “backward.” That is, they
go to the assigned exercises and try them, and if they get stuck, they turn to the text to look for
examples to help them. Thus, this text features clearly marked, step-by-step examples that students can follow. Each detailed example is linked to a section exercise, which students can use
to build specific skills needed to work exercises in the section.
Each chapter begins with a clear set of specific chapter outcomes. The examples and practice exercises are designed to reinforce the objectives and lead students toward the desired outcomes. The exercises are ordered from easy to more difficult and are divided into categories:
Conceptual, Skill Development, Business Applications, and Database Exercises.
Another difference is the importance this text places on data and how data are obtained.
Many business statistics texts assume that data have already been collected. We have decided
to underscore a more modern theme: Data are the starting point. We believe that effective decision making relies on a good understanding of the different types of data and the different data
collection options that exist. To highlight our theme, we begin a discussion of data and collecting data in Chapter 1 before any discussion of data analysis is presented. In Chapters 2 and 3,
where the important descriptive statistical techniques are introduced, we tie these statistical
techniques to the type and level of data for which they are best suited.
Although we know that the role of the computer is important in applying business statistics, it can be overdone at the beginning level to the point where instructors are required to
spend too much time teaching the software and too little time teaching statistical concepts.
This text features Excel and Minitab but limits the inclusion of software output to those areas
where it is of particular advantage to beginning students.

New to This Edition
᭿

Textual examples: More than 50 new examples throughout the text provide step-bystep details, enabling students to follow solution techniques easily. Students can then
apply the methodology from each example to solve other problems. These examples are
provided in addition to the vast array of business applications to give students a realworld, competitive edge. Featured companies in these new examples include Dove
Shampoo and Soap, The Frito-Lay Company, Goodyear Tire Company, Lockheed
Martin Corporation, the National Federation of Independent Business, Oakland Raiders
NFL Football, Southwest Airlines, and Whole Foods Grocery.
᭿ Visual summaries: Each main heading is summarized using a flow diagram, which

reminds students of the intended outcomes and leads them to the chapter’s conclusion.
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PREFACE

MyStatLab
᭿

᭿
᭿

᭿
᭿

MyStatLab: This proven book-specific online homework and assessment tool provides
a rich and flexible set of course materials, featuring free-response exercises that are
algorithmically generated for unlimited practice and mastery. Students can also use a
variety of online tools to independently improve their understanding and performance in
the course. Instructors can use MyStatLab’s homework and test manager to select and
assign their own online exercises and can import TestGen tests for added flexibility.
Quick prep links: At the beginning of each chapter, students are supplied with several
ways to get ready for the topics discussed in the chapter.
Chapter outcomes: Identifying what is to be gained from completing the chapter helps
focus a student’s attention. At the beginning of each chapter, every outcome is linked to
the corresponding main heading. Throughout the text, the chapter outcomes are recalled

at main headings to remind students of the objectives.
How to do it: Associated with the textual examples, lists are provided throughout each
chapter to summarize major techniques and reinforce fundamental concepts.
Online chapter—Introduction to Decision Analysis: This chapter discusses the analytic methods used to deal with the wide variety of decision situations a student might
encounter.

Key Pedagogical Features
᭿

᭿
᭿

᭿

᭿

᭿

Business applications: One of the strengths of the previous editions of this textbook
has been the emphasis on business applications and decision making. This feature is
expanded even more in the eighth edition. Many new applications are included, and all
applications are highlighted in the text with special icons, making them easier for students to locate as they use the text.
Quick prep links: Each chapter begins with a list that provides several ways to get
ready for the topics discussed in the chapter.
Chapter outcomes: At the beginning of each chapter, outcomes, which identify what is
to be gained from completing the chapter, are linked to the corresponding main headings. Throughout the text, the chapter outcomes are recalled at the appropriate main
headings to remind students of the objectives.
Step-by-step approach: This edition provides continued and improved emphasis on
providing concise, step-by-step details to reinforce chapter material.
• How to do it lists are provided throughout each chapter to summarize major

techniques and reinforce fundamental concepts.
• Textual examples throughout the text provide step-by-step details, enabling
students to follow solution techniques easily. Students can then apply the methodology from each example to solve other problems. These examples are provided in
addition to the vast array of business applications to give students a real-world,
competitive edge.
Real-world application: The chapters and cases feature real companies, actual applications, and rich data sets, allowing the authors to concentrate their efforts on addressing
how students apply this statistical knowledge to the decision-making process.
• McDonald’s Corporation video cases —The authors’ relationship with McDonald’s
provides students with real-world statistical data and integrated video case series.
• Chapter cases —Cases provided in nearly every chapter are designed to give students the opportunity to apply statistical tools. Each case challenges students to
define a problem, determine the appropriate tool to use, apply it, and then write a
summary report.
Special review sections: For Chapters 1 to 3 and Chapters 8 to 12, special review sections provide a summary and review of the key issues and statistical techniques. Highly
effective flow diagrams help students sort out which statistical technique is appropriate
to use in a given problem or exercise. These flow diagrams serve as a mini-decision support system that takes the emphasis off memorization and encourages students to seek a


PREFACE

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xix

higher level of understanding and learning. Integrative questions and exercises ask
students to demonstrate their comprehension of the topics covered in these sections.
᭿ Problems and exercises: This edition includes an extensive revision of exercise sections,
featuring more than 250 new problems. The exercise sets are broken down into three
categories for ease of use and assignment purposes:
1. Skill Development—These problems help students build and expand upon statistical
methods learned in the chapter.

2. Business Applications—These problems involve realistic situations in which students
apply decision-making techniques.
3. Computer Applications—In addition to the problems that may be worked out manually, many problems have associated data files and can be solved using Excel,
Minitab, or other statistical software.
᭿ Virtual office hours: The authors appear in three- to five-minute video clips in which
they work examples taken directly from the book. Now students can watch and listen to
the instructor walk through an example and obtain even greater clarity with respect to
how the example is worked and how the results are interpreted.
᭿ Computer integration: The text seamlessly integrates computer applications with
textual examples and figures, always focusing on interpreting the output. The goal is
for students to be able to know which tools to use, how to apply the tools, and how to
analyze their results for making decisions.
• Minitab 14 is featured, with associated instructions.
• Microsoft Excel 2007 integration instructs students in how to use the Excel 2007
user interface for statistical applications.
• PHStat2 is a specially developed Excel add-in package that is compatible with the
Excel 2007 release. It performs a number of statistical features not included with
Excel. The added functions and procedures are useful in the study and application of
business statistics. When installed, PHStat2 attaches itself to the Excel menu bar,
providing users with a pull-down menu of topics that supplement the Data Analysis
add-in tools in Microsoft Excel.
PHStat2 uses a set of simple and consistent dialog boxes that allow students to
specify values and options for almost 50 tools included in the software. PHStat2
produces Excel worksheets organized into areas for input data, intermediate calculations, and the results of analyses. Unlike with some competitors’ add-ins, most of
these worksheets contain live formulas that allow students to engage immediately in
further “what-if” explorations of the data. (Where applicable, these worksheets
contain special cell tints that distinguish the cells that contain user-modifiable input
values from the cells containing the results, making “what-if” analysis even easier.)
Completing the package is an excellent online help system.


MyStatLab
• MyStatLab is a proven book-specific online homework and assessment tool that
provides a rich and flexible set of course materials, featuring free-response exercises that are algorithmically generated for unlimited practice and mastery. Students can also use a variety of online tools to independently improve their
understanding and performance in the course. Instructors can use MyStatLab’s
homework and test manager to select and assign their own online exercises and
import TestGen tests for added flexibility.

Student Resources
Student Solutions Manual
The Student Solutions Manual contains worked-out solutions to odd-numbered problems in
the text. It displays the detailed process that students should use to work through the problems.
The manual also provides interpretation of the answers and serves as a valuable learning tool
for students.


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MyStatLab
MyStatLab™
Part of the MyMathLab® and MathXL® product family, MyStatLab™ is a text-specific, easily customizable online course that integrates interactive multimedia instruction with textbook
content. MyStatLab gives you the tools you need to deliver all or a portion of your course
online, whether your students are in a lab setting or working from home.
᭿

᭿


᭿

᭿

᭿

᭿

Interactive tutorial exercises: A comprehensive set of exercises—correlated to your
textbook at the objective level—are algorithmically generated for unlimited practice and
mastery. Most exercises are free-response exercises and provide guided solutions, sample problems, and learning aids for extra help at point-of-use.
Personalized study plan: When a student completes a test or quiz in MyStatLab, the
program generates a personalized study plan for that student, indicating which topics
have been mastered and linking students directly to tutorial exercises for topics they
need to study and retest.
Multimedia learning aids: Students can use online learning aids, such as video lectures, animations, and a complete multimedia textbook, to help independently improve
their understanding and performance.
Statistics tools: MyStatLab includes built-in tools for statistics, including statistical
software called StatCrunch. Students also have access to statistics animations and applets that illustrate key ideas for the course. For those who use technology in their
course, technology manual PDFs are included.
StatCrunch: This powerful online tool provides an interactive environment for doing
statistics. You can use StatCrunch for both numerical and graphical data analysis, taking
advantage of interactive graphics to help you see the connection between objects selected in a graph and the underlying data. In MyStatLab, the data sets from your textbook are preloaded into StatCrunch. StatCrunch is also available as a tool from the
online homework and practice exercises in MyStatLab and in MathXL for Statistics.
Also available is Statcrunch.com, Web-based software that allows students to perform
complex statistical analysis in a simple manner.
Pearson Tutor Center (www.pearsontutorservices.com): Access to the Pearson Tutor
Center is automatically included with MyStatLab. The Tutor Center is staffed by qualified mathematics instructors who provide textbook-specific tutoring for students via
toll-free phone, fax, e-mail, and interactive Web sessions.


MyStatLab is powered by CourseCompass™, Pearson Education’s online teaching and learning environment, and by MathXL®, an online homework, tutorial, and assessment system. For
more information about MyStatLab, visit www.mystatlab.com.

Student Videos
Student videos—located at MyStatLab only—feature McDonald’s video cases and the virtual
office hours videos.

Student Companion Web Site
The Companion Web Site, www.pearsonhighered.com/groebner, contains valuable online resources for both students and professors, including:
᭿

Online chapter—Introduction to Decision Analysis: This chapter discusses the analytic
methods used to deal with the wide variety of decision situations a student might encounter.
᭿ Data files: The text provides an extensive number of data files for examples, cases, and
exercises. These files are also located at MyStatLab.
᭿ Excel and Minitab tutorials: Customized PowerPoint tutorials for both Minitab and
Excel use data sets from text examples. Separate tutorials for Excel 2003 and Excel
2007 are provided. Students who need additional instruction in Excel or Minitab can access the menu-driven tutorial, which shows exactly the steps needed to replicate all
computer examples in the text. These tutorials are also located at MyStatLab.
᭿ Excel simulations: Several interactive simulations illustrate key statistical topics and
allow students to do “what-if” scenarios. These simulations are also located at MyStatLab.


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᭿


PHStat: PHStat is a collection of statistical tools that enhance the capabilities of Excel
and assist students in learning the concepts of statistics; published by Pearson Education. This tool is also located at MyStatLab.
᭿ Online study guide: This guide contains practice or homework quizzes consisting of
multiple-choice, true/false, and essay questions that effectively review textual material.
It is located on the Companion Web site only.

Instructor Resources
᭿

Instructor Resource Center: The Instructor Resource Center contains the electronic
files for the complete Instructor’s Solutions Manual, the Test Item File, and Lecture
PowerPoint presentations (www.pearsonhighered.com/groebner).
᭿ Register, Redeem, Login: At www.pearsonhighered.com/irc, instructors can access a
variety of print, media, and presentation resources that are available with this text in
downloadable, digital format. For most texts, resources are also available for course
management platforms such as Blackboard, WebCT, and Course Compass.
᭿ It gets better: Once you register, you will not have additional forms to fill out or multiple
usernames and passwords to remember to access new titles and/or editions. As a registered
faculty member, you can log in directly to download resource files and receive immediate
access and instructions for installing course management content to your campus server.
᭿ Need help? Our dedicated technical support team is ready to assist instructors with
questions about the media supplements that accompany this text. Visit http://247
.prenhall.com/ for answers to frequently asked questions and toll-free user support
phone numbers. The supplements are available to adopting instructors. Detailed
descriptions are provided on the Instructor Resource Center.

Instructor’s Solutions Manual
The Instructor’s Solutions Manual contains worked-out solutions to all the problems and cases
in the text.


Lecture PowerPoint Presentations
A PowerPoint presentation, created by Angela Mitchell of Wilmington College of Ohio, is available for each chapter. The PowerPoint slides provide instructors with individual lecture outlines to
accompany the text. The slides include many of the figures and tables from the text. Instructors can
use these lecture notes as is or can easily modify the notes to reflect specific presentation needs.

Test Item File
The Test Item File, by Tariq Mughal of The University of Utah, contains a variety of true/false,
multiple-choice, and short-answer questions for each chapter.

TestGen
The computerized TestGen package allows instructors to customize, save, and generate classroom tests. The test program permits instructors to edit, add, or delete questions from the test
bank; edit existing graphics and create new graphics; analyze test results; and organize a database of test and student results. This software allows for extensive flexibility and ease of use.
It provides many options for organizing and displaying tests, along with search and sort
features. The software and the test banks can be downloaded from the Instructor Resource
Center, at www.pearsonhighered.com/groebner.

MyStatLab
MyStatLab
᭿

MathXL® for Statistics: This powerful online homework, tutorial, and assessment system accompanies Pearson Education textbooks in statistics. With MathXL for Statistics,
instructors can create, edit, and assign online homework and tests, using algorithmically


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᭿

᭿

᭿

᭿

generated exercises correlated at the objective level to the textbook. They can also create
and assign their own online exercises and import TestGen tests for added flexibility. All
student work is tracked in MathXL’s online gradebook. Students can take chapter tests
in MathXL and receive personalized study plans based on their test results. The study
plan diagnoses weaknesses and links students directly to tutorial exercises for the objectives they need to study and retest. Students can also access supplemental animations
and video clips directly from selected exercises. MathXL for Statistics is available to
qualified adopters. For more information, visit www.mathxl.com or contact your sales
representative.
MyStatLab™: Part of the MyMathLab® and MathXL® product family, MyStatLab™
is a text-specific, easily customizable online course that integrates interactive multimedia instruction with textbook content. MyStatLab gives you the tools you need to deliver
all or a portion of your course online, whether your students are in a lab setting or working from home.
Assessment Manager: An easy-to-use assessment manager lets instructors create online
homework, quizzes, and tests that are automatically graded and correlated directly to the
textbook. Assignments can be created using a mix of questions from the MyStatLab
exercise bank, instructor-created custom exercises, and/or TestGen test items.
Gradebook: Designed specifically for mathematics and statistics, the MyStatLab
gradebook automatically tracks students’ results and gives you control over how to calculate final grades. You can also add offline (paper-and-pencil) grades to the gradebook.
Math Exercise Builder: You can use the MathXL Exercise Builder to create static and
algorithmic exercises for your online assignments. A library of sample exercises provides an easy starting point for creating questions, and you can also create questions
from scratch.


Acknowledgments
Publishing this eighth edition of Business Statistics: A Decision-Making Approach has been a
team effort involving the contributions of many people. At the risk of overlooking someone, we
express our sincere appreciation to the many key contributors. Throughout the two years we
have worked on this revision, many of our colleagues from colleges and universities around the
country have taken time from their busy schedules to provide valuable input and suggestions
for improvement. We would like to thank the following people:
Donald I. Bosshardt, Canisius College
Sara T. DeLoughy, Western Connecticut State University
Nicholas R. Farnum, California State University—Fullerton
Kent E. Foster, Winthrop University
John Gum, University of South Florida—St. Petersburg
Jeffery Guyse, California State Polytechnic University, Pomona
Chaiho Kim, Santa Clara University
David Knopp, Chattanooga State Technical Community College
Linda Leighton, Fordham University
Sally A. Lesik, Central Connecticut State University
Merrill W. Liechty, Drexel University
Robert M. Lynch, University of Northern Colorado—Monfort College of Business
Jennifer Martin, York College of Pennsylvania
Constance McLaren, Indiana State University
Mahour Mellat-Parast, University of North Carolina—Pembroke
Carl E. Miller, Northern Kentucky University
Tariq Mughal, David Eccles, School of Business, University of Utah
Tom Naugler, Johns Hopkins University