Business statistics: a decision-making approach

941 Pages·21.179 MB·English

by  Groebner, David F;Shannon, Patrick W;Smith, Kent D;Fry, Phillip C

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Business
eighth edition
Statistics
A Decision-Making Approach
D AV 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
P H I L L I P C . F RY
Boise State University, Professor, ITSCM Department Chair
K E N T D . S M I T H
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
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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 mas-
ter’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 writ-
ing 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 statis-
tics,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 anda
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,Alejan-
dra Johanna,and twins Courtney Rene and Candace Marie.
Kent D. Smithreceived 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 mas-
ter 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 Univer-
sity Statistical Consultant at the University of California,Riverside,and at California Polytech-
nic 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,med-
ical and orthodontic research,and assisting graduate students with analysis required for mas-
ter 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 re-
tired,he still teaches part time at the university. The subjects he teaches include upper-division courses in re-
gression,analysis of variance,linear models,and probability and mathematical statistics,as well as a full array
of service courses.
v
Brief Contents
Chapter 1 The Where,Why,and How of Data Collection 1
Chapter 2 Graphs,Charts,and Tables—Describing Your Data 31
Chapter 3 Describing Data Using Numerical Measures 85
Chapters 1–3 Special Review Section 139
Chapter 4 Using Probability and Probability Distributions 146
Chapter 5 Discrete Probability Distributions 191
Chapter 6 Introduction to Continuous Probability Distributions 233
Chapter 7 Introduction to Sampling Distributions 264
Chapter 8 Estimating Single Population Parameters 305
Chapter 9 Introduction to Hypothesis Testing 346
Chapter 10 Estimation and Hypothesis Testing for Two Population Parameters 397
Chapter 11 Hypothesis Tests and Estimation for Population Variances 448
Chapter 12 Analysis of Variance 475
Chapters 8–12 Special Review Section 530
Chapter 13 Goodness-of-Fit Tests and Contingency Analysis 547
Chapter 14 Introduction to Linear Regression and Correlation Analysis 579
Chapter 15 Multiple Regression Analysis and Model Building 633
Chapter 16 Analyzing and Forecasting Time-Series Data 709
Chapter 17 Introduction to Nonparametric Statistics 770
Chapter 18 Introduction to Quality and Statistical Process Control 804
APPENDIXA Random Numbers Table 837
APPENDIXB Binomial Distribution Table 838
APPENDIXC Poisson Probability Distribution Table 851
APPENDIXD Standard Normal Distribution Table 856
APPENDIXE Exponential Distribution Table 857
APPENDIXF Values of tfor Selected Probabilities 858
APPENDIXG Values of(cid:2)2for Selected Probabilities 859
APPENDIXH F-Distribution Table 860
APPENDIXI Critical Values of Hartley’s F Test 866
max
APPENDIXJ Distribution of the Studentized Range (q-values) 867
APPENDIXK Critical Values of rin the Runs Test 869
APPENDIXL Mann-WhitneyUTest Probabilities (n< 9) 870
APPENDIXM Mann-WhitneyUTest Critical Values (9 (cid:3)n(cid:3)20) 872
APPENDIXN Critical Values of Tin the Wilcoxon Matched-Pairs Signed-Ranks Test (n(cid:3)25) 874
APPENDIXO Critical Values d andd of the Durbin-Watson Statistic D 875
L U
APPENDIXP Lower and Upper Critical Values Wof Wilcoxon Signed-Ranks Test 877
APPENDIXQ Control Chart Factors 878
vi
Contents
Preface xix
Chapter 1 The Where, Why, and How of Data Collection 1
What is Business Statistics? 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 29
Chapter 2 Graphs, Charts, and Tables—Describing Your Data 31
Frequency Distributions and Histograms 32
Frequency Distribution 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