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Statistical Techniques in

BUSINESS & ECONOMICS SIXTEENTH EDITION

DOUGLAS A. LIND Coastal Carolina University and The University of Toledo

WILLIAM G. MARCHAL The University of Toledo

SAMUEL A. WATHEN Coastal Carolina University

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STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS, SIXTEENTH EDITION Published by McGraw-Hill Education, 2 Penn Plaza, New York, NY 10121. Copyright © 2015 by McGraw-Hill Education. All rights reserved. Printed in the United States of America. Previous editions © 2012, 2010, and 2008. No part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written consent of McGraw-Hill Education, including, but not limited to, in any network or other electronic storage or transmission, or broadcast for distance learning. Some ancillaries, including electronic and print components, may not be available to customers outside the United States. This book is printed on acid-free paper. 1 2 3 4 5 6 7 8 9 0 DOW/DOW 1 0 9 8 7 6 5 4 ISBN 978-0-07-802052-0 MHID 0-07-802052-2 Senior Vice President, Products & Markets: Kurt L. Strand Vice President, Content Production & Technology Services: Kimberly Meriwether David Managing Director: Douglas Reiner Senior Brand Manager: Thomas Hayward Executive Director of Development: Ann Torbert Development Editor: Kaylee Putbrese Director of Digital Content: Doug Ruby Digital Development Editor: Meg B. Maloney Senior Marketing Manager: Heather A. Kazakoff Content Project Manager: Diane L. Nowaczyk Content Project Manager: Brian Nacik Senior Buyer: Carol A. Bielski Design: Jana Singer Cover Image: Adrianna Williams/The Image Bank/Getty Images Lead Content Licensing Specialist: Keri Johnson Typeface: 9.5/11 Helvetica Neue 55 Compositor: Aptara®, Inc. Printer: R. R. Donnelley All credits appearing on page or at the end of the book are considered to be an extension of the copyright page. Library of Congress Cataloging-in-Publication Data Lind, Douglas A. Statistical techniques in business & economics / Douglas A. Lind, Coastal Carolina University and The University of Toledo, William G. Marchal, The University of Toledo, Samuel A. Wathen, Coastal Carolina University. — Sixteenth edition. pages cm. — (The McGraw-Hill/Irwin series in operations and decision sciences) Includes index. ISBN 978-0-07-802052-0 (alk. paper) — ISBN 0-07-802052-2 (alk. paper) 1. Social sciences—Statistical methods. 2. Economics—Statistical methods. 3. Commercial statistics. I. Marchal, William G. II. Wathen, Samuel Adam. III. Title. IV. Title: Statistical techniques in business and economics. HA29.M268 2015 519.5—dc23 2013035290

The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a website does not indicate an endorsement by the authors or McGraw-Hill Education, and McGraw-Hill Education does not guarantee the accuracy of the information presented at these sites.

www.mhhe.com

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D E D I C AT I O N To Jane, my wife and best friend, and our sons, their wives, and our grandchildren: Mike and Sue (Steve and Courtney), Steve and Kathryn (Kennedy, Jake, and Brady), and Mark and Sarah (Jared, Drew, and Nate). Douglas A. Lind To my newest grandchildren (George Orn Marchal, Liam Brophy Horowitz, and Eloise Larae Marchal Murray), newest son-in-law (James Miller Nicholson), and newest wife (Andrea). William G. Marchal To my wonderful family: Isaac, Hannah, and Barb. Samuel A. Wathen

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A NOTE FROM THE AUTHORS

Over the years, we have received many compliments on this text and understand that it’s a favorite among students. We accept that as the highest compliment and continue to work very hard to maintain that status. The objective of Statistical Techniques in Business and Economics is to provide students majoring in management, marketing, finance, accounting, economics, and other fields of business administration with an introductory survey of the many applications of descriptive and inferential statistics. We focus on business applications, but we also use many exercises and examples that relate to the current world of the college student. A previous course in statistics is not necessary, and the mathematical requirement is first-year algebra. In this text, we show beginning students every step needed to be successful in a basic statistics course. This step-by-step approach enhances performance, accelerates preparedness, and significantly improves motivation. Understanding the concepts, seeing and doing plenty of examples and exercises, and comprehending the application of statistical methods in business and economics are the focus of this book. The first edition of this text was published in 1967. At that time, locating relevant business data was difficult. That has changed! Today, locating data is not a problem. The number of items you purchase at the grocery store is automatically recorded at the checkout counter. Phone companies track the time of our calls, the length of calls, and the identity of the person called. Credit card companies maintain information on the number, time and date, and amount of our purchases. Medical devices automatically monitor our heart rate, blood pressure, and temperature from remote locations. A large amount of business information is recorded and reported almost instantly. CNN, USA Today, and MSNBC, for example, all have websites that track stock prices with a delay of less than 20 minutes. Today, skills are needed to deal with a large volume of numerical information. First, we need to be critical consumers of information presented by others. Second, we need to be able to reduce large amounts of information into a concise and meaningful form to enable us to make effective interpretations, judgments, and decisions. All students have calculators and most have either personal computers or access to personal computers in a campus lab. Statistical software, such as Microsoft Excel and Minitab, is available on these computers. The commands necessary to achieve the software results are available in Appendix C at the end of the book. We use screen captures within the chapters, so the student becomes familiar with the nature of the software output. Because of the availability of computers and software, it is no longer necessary to dwell on calculations. We have replaced many of the calculation examples with interpretative ones, to assist the student in understanding and interpreting the statistical results. In addition, we now place more emphasis on the conceptual nature of the statistical topics. While making these changes, we still continue to present, as best we can, the key concepts, along with supporting interesting and relevant examples.

WHAT’S NEW IN THIS SIXTEENTH EDITION? We have made changes to this edition that we think you and your students will find useful and timely. • We reorganized the chapters so that each section corresponds to a learning objective. The learning objectives have been revised. • We expanded the hypothesis testing procedure in Chapter 10 to six steps, emphasizing the interpretation of test results.

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• We have revised example/solution sections in various chapters: • Chapter 5 now includes a new example/solution used to demonstrate contingency tables and tree diagrams. Also the example/solution demonstrating the combination formula has been revised. • Chapter 6 includes a revised example/solution demonstrating the binomial distribution. • Chapter 15 includes a new example/solution demonstrating contingency table analysis. • We have revised the simple regression example in Chapter 13 and increased the number of observations to better illustrate the principles of simple linear regression. • We have reordered the nonparametric chapters to follow the traditional statistics chapters. • We moved the sections on one- and two-sample tests of proportions, placing all analysis of nominal data in one chapter: Nonparametric Methods: Nominal Level Hypothesis Tests. • We combined the answers to the Self-Review Exercises into a new appendix. • We combined the Software Commands into a new appendix. • We combined the Glossaries in the section reviews into a single Glossary that follows the appendices at the end of the text. • We improved graphics throughout the text.

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H O W A R E C H A P T E R S O R G A N I Z E D T O E N G AG E S T U D E N T S A N D P R O M O T E L E A R N I N G?

Chapter Learning Objectives Each chapter begins with a set of learning objectives designed to provide focus for the chapter and motivate student learning. These objectives, located in the margins next to the topic, indicate what the student should be able to do after completing each section in the chapter. MERRILL LYNCH recently completed LEARNING OBJECTIVES

Chapter Opening Exercise

a study of online investment portfo-

When you have completed this chapter, you will be able to:

lios for a sample of clients. For the

LO2-1

70 participants in the study, organize

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A representative exercise opens the chapter and shows how the chapter content can be applied to a real-world situation.

Introduction to the Topic

these data into a frequency distribu-

Summarize qualitative variables with frequency and relative frequency tables.

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tion. (See Exercise 43 and LO2-3.)

LO2-3

Summarize quantitative variables with frequency and relative frequency distributions.

LO2-4

Display a frequency distribution using a histogram or frequency polygon.

INTRODUCTION

Chapter 2 began our study of descriptive statistics. To summarize raw data into a Each chapter starts with a review of meaningful form, we organized qualitative data into a frequency table and portrayed the important concepts of the previthe results in a bar chart. In a similar fashion, we organized quantitative data into a ous chapter and provides a link to the frequency distribution and portrayed the results in a histogram. We also looked at other graphical techniques such as pie charts to portray qualitative data and frematerial in the current chapter. This quency polygons to portray quantitative data. step-by-step approach increases This chapter is concerned with two numerical ways of describing quantitative comprehension by providing continuvariables, namely, measures of location and measures of dispersion. Measures of ity across the concepts. location are often referred to as averages. The purpose of a measure of location is to Lin20522_ch04_093-130.indd Page 105 12/10/13 11:43 AM user-f-w-198 /201/MH02018/Lin20522_disk1of1/0078020522/Lin20522_pagefiles pinpoint the center of a distribution of data. An

Example/Solution

E X A M P L E

After important concepts are introduced, a solved example is given. This example provides a how-to illustration and shows a relevant business application that helps students answer the question, “What will I use this for?”

The service departments at Tionesta Ford Lincoln Mercury and Sheffield Motors Inc., two of the four Applewood Auto Group dealerships, were both open 24 days last month. Listed below is the number of vehicles serviced last month at the two dealerships. Construct dot plots and report summary statistics to compare the two dealerships. Tionesta Ford Lincoln Mercury Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

23 30 29 35

33 32 25 32

27 28 36 35

28 33 31 37

39 35 32 36

26 32 27 30

Self-Reviews Self-Reviews are interspersed throughout each chapter and closely patterned after the preceding examples. They help students monitor their progress and provide immediate reinforcement for that particular technique.

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The Quality Control department of Plainsville Peanut Company is responsible for checking the weight of the 8-ounce jar of peanut butter. The weights of a sample of nine jars produced last hour are:

SELF-REVIEW

4–2

7.69 (a) (b)

7.72

7.8

7.86

7.90

7.94

7.97

8.06

What is the median weight? Determine the weights corresponding to the first and third quartiles.

8.09

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Statistics in Action Statistics in Action articles are scattered throughout the text, usually about two per chapter. They provide unique and interesting applications and historical insights in the field of statistics. Page 63 12/10/13 Lin20522_ch03_050-092.indd

STATISTICS IN ACTION If you wish to get some attention at the next gathering you attend, announce 11:37 AM user-f-w-198 that you believe that at least two people present were born on the same date—that is, the same day of the year but not necessarily the same year. If there are 30 people in the room,

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Definitions Definitions of new terms or terms unique to the study of statistics are set apart from the text and highlighted for easy reference and review. They also appear in the Glossary at the end of the book.

JOINT PROBABILITY A probability that measures the likelihood two or more events will happen concurrently.

Formulas Formulas that are used for the first time are boxed and numbered for reference. In addition, a formula card is bound into the back of the text that lists all the key formulas.

Exercises Exercises are included after sections within the chapter and at the end of the chapter. Section exercises cover the material studied in the section.

E X E R C I S E S

SPECIAL RULE OF MULTIPLICATION

P(A and B) 5 P(A)P(B)

[5–5]

33. P(A1 ) 5 .60, P(A2 ) 5 .40, P(B1 ƒ A1 ) 5 .05, and P(B1 ƒ A2 ) 5 .10. Use Bayes’ theorem to determine P(A1 ƒ B1 ). 34. P(A1 ) 5 .20, P(A2 ) 5 .40, P(A3 ) 5 .40, P(B1 ƒ A1 ) 5 .25, P(B1 ƒ A2 ) 5 .05, and P(B1 ƒ A3 ) 5 .10. Use Bayes’ theorem to determine P(A3 ƒ B1 ). 35. The Ludlow Wildcats baseball team, a minor league team in the Cleveland Indians organization, plays 70% of their games at night and 30% during the day. The team wins 50% of their night games and 90% of their day games. According to today’s newspaper, they won yesterday. What is the probability the game was played at night? 36. Dr. Stallter has been teaching basic statistics for many years. She knows that 80% of the students will complete the assigned problems. She has also determined that among those who do their assignments, 90% will pass the course. Among those students who do not do

Computer Output The text includes many software examples, using Excel, MegaStat®, and Minitab.

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HOW DOES THIS TEXT RE INFORCE S T U D E N T L E A R N I N G? Lin20522_ch07_206-246.indd Page 237 10/18/13 8:39 AM f-494

BY C H A P T E R

C H A P T E R

I. A random variable is a numerical value determined by the outcome of an experiment. II. A probability distribution is a listing of all possible outcomes of an experiment and the probability associated with each outcome. A. A discrete probability distribution can assume only certain values. The main features are: 1. The sum of the probabilities is 1.00. 2. The probability of a particular outcome is between 0.00 and 1.00. 3. The outcomes are mutually exclusive. B. A continuous distribution can assume an infinite number of values within a specific range. III. The mean and variance of a probability distribution are computed as follows. A. The mean is equal to:

Chapter Summary Each chapter contains a brief summary of the chapter material, including the vocabulary and the critical formulas.

[6–1]

s2 5 © [ (x 2 m) 2P(x) ]

[6–2]

P R O N U N C I A T I O N

Lin20522_ch07_206-246.indd This tool lists the mathematical symbol, itsPage 241 10/18/13 8:39 AM f-494 meaning, and how to pronounce it. We believe this will help the student retain the meaning of the symbol and generally enhance course communications.

C H A P T E R

K E Y

SYMBOL

MEANING

PRONUNCIATION

P(A)

Probability of A

P of A

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P(,A)

Probability of not A

P of not A

P(A and B)

Probability of A and B

P of A and B

P(A or B)

Probability of A or B

P of A or B

P(A ƒ B)

Probability of A given B has happened

P of A given B

P

Permutation of n items selected r at a time

Pnr

Cr

Combination of n items selected r at a time

Cnr

n r n

E X E R C I S E S 41. The amount of cola in a 12-ounce can is uniformly distributed between 11.96 ounces and 12.05 ounces. a. What is the mean amount per can? b. What is the standard deviation amount per can? c. What is the probability of selecting a can of cola and finding it has less than 12 ounces? d. What is the probability of selecting a can of cola and finding it has more than 11.98 ounces? e. What is the probability of selecting a can of cola and finding it has more than 11.00 ounces? 42. A tube of Listerine Tartar Control toothpaste contains 4.2 ounces. As people use the toothpaste, the amount remaining in any tube is random. Assume the amount of toothpaste remaining in the tube follows a uniform distribution. From this information, we can determine the following information about the amount remaining in a toothpaste tube without invading anyone’s privacy. a. How much toothpaste would you expect to be remaining in the tube? b. What is the standard deviation of the amount remaining in the tube? c. What is the likelihood there is less than 3.0 ounces remaining in the tube? d. What is the probability there is more than 1.5 ounces remaining in the tube? 43. Many retail stores offer their own credit cards. At the time of the credit application, the customer is given a 10% discount on the purchase. The time required for the credit application process follows a uniform distribution with the times ranging from 4 minutes to 10 minutes. a. What is the mean time for the application process? b. What is the standard deviation of the process time? c. What is the likelihood a particular application will take less than 6 minutes?

Generally, the end-of-chapter exercises are the most challenging and integrate the chapter concepts. The answers and worked-out solutions for all odd-numbered exercises are in Appendix D at the end of the text. Many exercises are noted with a data file icon in the margin. For these exercises, there are data files in Excel format located on the text’s website, www .mhhe.com/lind16e. These files help students use statistical software to solve the exercises.

Data Set Exercises

m 5 © [xP(x) ] B. The variance is equal to:

Pronunciation Key

Chapter Exercises

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S U M M A R Y

D A T A

S E T

E X E R C I S E S

(The data for these exercises are available at the text website: www.mhhe.com/lind16e.) 74. Refer to the Real Estate data, which report information on homes sold in the Goodyear, The last several exercises at the end of Arizona, area during the last year. a. The mean selling price (in $ thousands) of the homes was computed earlier to be each chapter are based on three large Lin20522_appc_744-755.indd Page 747 19/11/13 9:27 AM f-500 /201/MH02018/Lin20522_disk1of1/0078020522/Lin20522_pagefiles $221.10, with a standard deviation of $47.11. Use the normal distribution to estimate data sets. These data sets are printed the percentage of homes selling for more than $280.0. Compare this to the actual results. Does the normal distribution yield a good approximation of the actual results? in Appendix A in the text and are also b. The mean distance from the center of the city is 14.629 miles, with a standard deviation of 4.874 miles. Use the normal distribution to estimate the number of homes 18 or more on the text’s website. These data sets miles but less than 22 miles from the center of the city. Compare this to the actual results. Does the normal distribution yield a good approximation of the actual results? present the students with real-world and more complex applications.

Software Commands Software examples using Excel, MegaStat®, and Minitab are included throughout the text. The explanations of the computer input commands are placed at the end of the text in Appendix C.

x

CHAPTER 5 5–1. The Excel Commands to determine the number of permutations shown on page 164 are: a. Click on the Formulas tab in the top menu, then, on the far left, select Insert Function fx.

b. In the Insert Function box, select Statistical as the category, then scroll down to PERMUT in the Select a function list. Click OK. c. In the PERM box after Number, enter 8 and in the Number_chosen box enter 3. The correct answer of 336 appears twice in the box.

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Answers to Self-Review

16–7 a. Rank

The worked-out solutions to the Self-Reviews are provided at the end of the text in Appendix E.

x

y

x

y

d

d2

805 777 820 682 777 810 805 840 777 820

23 62 60 40 70 28 30 42 55 51

5.5 3.0 8.5 1.0 3.0 7.0 5.5 10.0 3.0 8.5

1 9 8 4 10 2 3 5 7 6

4.5 26.0 0.5 23.0 27.0 5.0 2.5 5.0 24.0 2.5 0

20.25 36.00 0.25 9.00 49.00 25.00 6.25 25.00 16.00 6.25 193.00

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BY S E C T I O N

Section Reviews

A REVIEW OF CHAPTERS 1–4

After selected groups of chapters This section is a review of the major concepts and terms introduced in Chapters 1–4. Chapter 1 began by describing the meaning and purpose of statistics. Next we described the different types of variables and the four levels of measurement. (1–4, 5–7, 8 and 9, 10–12, 13 and Chapter 2 was concerned with describing a set of observations by organizing it into a frequency distribution and then portraying the frequency distribution as a histogram or a frequency polygon. Chapter 3 began by describing measures of location, 14, 15 and 16, and 17 and 18), a such as the mean, weighted mean, median, geometric mean, and mode. This chapter also included measures of dispersion, or spread. Discussed in this section were the range, variance, and standard deviation. Chapter 4 included several graphing Section Review is included. Much techniques such as dot plots, box plots, and scatter diagrams. We also discussed the coefficient of skewness, which reports the lack of symmetry in a set of data. like a review before an exam, these Lin20522_ch04_093-130.indd Page 129 07/11/13 6:44 PM user-f-w-198 /201/MH02018/Lin20522_disk1of1/0078020522/Lin20522_pagefiles include a brief overview of the chapters and problems for review.

Cases

C A S E S

The review also includes continuing cases and several small cases that let students make decisions using tools and techniques from a variety of chapters.

The following case will appear in subsequent review sections. Assume that you work in the Planning Department of the Century National Bank and report to Ms. Lamberg. You will need to do some data analysis and prepare a short written report. Remember, Mr. Selig is the president of the bank, so you will want to ensure that your report is complete and accurate. A copy of the data appears in Appendix A.6. Century National Bank has offices in several cities in the Midwest and the southeastern part of the United States. Mr. Dan Selig, president and CEO, would like to know the characteristics of his checking account customers. What is the balance of a typical customer? How many other bank services do the checking account customers use? Do the customers use the ATM service and, if so, how often? What about debit cards? Who uses them, and how often are they used?

A. Century National Bank

balances for the four branches. Is there a difference among the branches? Be sure to explain the difference between the mean and the median in your report. 3. Determine the range and the standard deviation of the checking account balances. What do the first and third quartiles show? Determine the coefficient of skewness and indicate what it shows. Because Mr. Selig does not deal with statistics daily, include a brief description and interpretation of the standard deviation and other measures.

B. Wildcat Plumbing Supply Inc.: Do We Have Gender Differences? Wildcat Plumbing Supply has served the plumbing needs of Southwest Arizona for more than 40 years. The company was founded by Mr. Terrence St. Julian and is run today by

Practice Test The Practice Test is intended to give students an idea of content that might appear on a test and how the test might be structured. The Practice Test includes both objective questions and problems covering the material studied in the section.

P R A C T I C E

T E S T

There is a practice test at the end of each review section. The tests are in two parts. The first part contains several objective questions, usually in a fill-in-the-blank format. The second part is problems. In most cases, it should take 30 to 45 minutes to complete the test. The problems require a calculator. Check the answers in the Answer Section in the back of the book.

Part 1—Objective 1. The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in . making effective decisions is called 2. Methods of organizing, summarizing, and presenting data in an informative way are . called 3. The entire set of individuals or objects of interest or the measurements obtained from all . individuals or objects of interest are called the 4. List the two types of variables.

1. 2. 3. 4.

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W H AT T E C H N O L O G Y C O N N E C T S S T U D E N T S T O B U S I N E S S S TAT I S T I C S?

MCGRAW-HILL CONNECT® BUSINESS STATISTICS Less Managing. More Teaching. Greater Learning. McGraw-Hill Connect® Business Statistics is an online assignment and assessment solution that connects students with the tools and resources they’ll need to achieve success. McGraw-Hill Connect® Business Statistics helps prepare students for their future by enabling faster learning, more efficient studying, and higher retention of knowledge.

McGraw-Hill Connect® Business Statistics Features Connect® Business Statistics offers a number of powerful tools and features to make managing assignments easier, so faculty can spend more time teaching. With Connect Business Statistics, students can engage with their coursework anytime and anywhere, making the learning process more accessible and efficient. Connect® Business Statistics offers you the features described below.

Simple Assignment Management With Connect® Business Statistics, creating assignments is easier than ever, so you can spend more time teaching and less time managing. The assignment management function enables you to • Create and deliver assignments easily with selectable end-of-chapter questions and test bank items. • Streamline lesson planning, student progress reporting, and assignment grading to make classroom management more efficient than ever. • Go paperless with the eBook and online submission and grading of student assignments.

Smart Grading When it comes to studying, time is precious. Connect® Business Statistics helps students learn more efficiently by providing feedback and practice material when they need it, where they need it. When it comes to teaching, your time is also precious. The grading function enables you to • Have assignments scored automatically, giving students immediate feedback on their work and side-by-side comparisons with correct answers. • Access and review each response; manually change grades or leave comments for students to review. • Reinforce classroom concepts with practice tests and instant quizzes.

Instructor Library The Connect® Business Statistics Instructor Library is your repository for additional resources to improve student engagement in and out of class. You can select and use any asset that enhances your lecture.

Student Study Center The Connect® Business Statistics Student Study Center is the place for students to access additional resources. The Student Study Center • Offers students quick access to lectures, practice materials, eBooks, and more. • Provides instant practice material and study questions, easily accessible on the go.

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LearnSmart Students want to make the best use of their study time. The LearnSmart adaptive self-study technology within Connect® Business Statistics provides students with a seamless combination of practice, assessment, and remediation for every concept in the textbook. LearnSmart’s intelligent software adapts to every student response and automatically delivers concepts that advance the student’s understanding while reducing time devoted to the concepts already mastered. The result for every student is the fastest path to mastery of the chapter concepts. LearnSmart • Applies an intelligent concept engine to identify the relationships between concepts and to serve new concepts to each student only when he or she is ready. • Adapts automatically to each student, so students spend less time on the topics they understand and practice more those they have yet to master. • Provides continual reinforcement and remediation, but gives only as much guidance as students need. • Integrates diagnostics as part of the learning experience. • Enables you to assess which concepts students have efficiently learned on their own, thus freeing class time for more applications and discussion.

LearnSmart Achieve LearnSmart Achieve is a revolutionary new learning system that combines a continually adaptive learning experience with necessary course resources to focus students on mastering concepts they don’t already know. The program adjusts to each student individually as he or she progresses, creating just-in-time learning experiences by presenting interactive content that is tailored to each student’s needs. A convenient time-management feature and reports for instructors also ensure students stay on track.

Student Progress Tracking Connect® Business Statistics keeps instructors informed about how each student, section, and class is performing, allowing for more productive use of lecture and office hours. The progress-tracking function enables you to • View scored work immediately and track individual or group performance with assignment and grade reports. • Access an instant view of student or class performance relative to learning objectives. • Collect data and generate reports required by many accreditation organizations, such as AACSB and AICPA.

McGraw-Hill Connect® Plus Business Statistics McGraw-Hill reinvents the textbook learning experience for the modern student with Connect® Plus Business Statistics. A seamless integration of an eBook and Connect® Business Statistics, Connect® Plus Business Statistics provides all of the Connect Business Statistics features plus the following: • An integrated eBook, allowing for anytime, anywhere access to the textbook. • Dynamic links between the problems or questions you assign to your students and the location in the eBook where that problem or question is covered. • A powerful search function to pinpoint and connect key concepts in a snap. In short, Connect® Business Statistics offers you and your students powerful tools and features that optimize your time and energies, enabling you to focus on course content, teaching, and student learning. Connect® Business Statistics also offers a wealth of content resources for both instructors and students. This state-of-the-art, thoroughly tested system supports you in preparing students for the world that awaits.

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For more information about Connect, go to www.mcgrawhillconnect.com, or contact your local McGraw-Hill sales representative.

COURSESMART CourseSmart is a new way to find and buy eTextbooks. At CourseSmart you can save up to 50% of the cost of your print textbook, reduce your impact on the environment, and gain access to powerful web tools for learning. Try a free chapter to see if it’s right for you. Visit www.CourseSmart.com and search by title, author, or ISBN.

TEGRITY CAMPUS: LECTURES 24/7 Tegrity Campus is a service that makes class time available 24/7 by automatically capturing every lecture in a searchable format for students to review when they ® study and complete assignments. With a simple one-click start-and-stop process, you capture all computer screens and corresponding audio. Students can replay any part of any class with easy-to-use browser-based viewing on a PC or Mac. Educators know that the more students can see, hear, and experience class resources, the better they learn. In fact, studies prove it. With Tegrity Campus, students quickly recall key moments by using Tegrity Campus’s unique search feature. This search helps students efficiently find what they need, when they need it, across an entire semester of class recordings. Help turn all your students’ study time into learning moments immediately supported by your lecture. To learn more about Tegrity watch a two-minute Flash demo at http://tegritycampus.mhhe.com.

ASSURANCE OF LEARNING READY Many educational institutions today are focused on the notion of assurance of learning, an important element of some accreditation standards. Statistical Techniques in Business & Economics is designed specifically to support your assurance of learning initiatives with a simple, yet powerful solution. Each test bank question for Statistical Techniques in Business & Economics maps to a specific chapter learning objective listed in the text. You can use our test bank software, EZ Test and EZ Test Online, or Connect® Business Statistics to easily query for learning objectives that directly relate to the learning objectives for your course. You can then use the reporting features of EZ Test to aggregate student results in similar fashion, making the collection and presentation of assurance of learning data simple and easy.

MCGRAW-HILL CUSTOMER CARE CONTACT INFORMATION At McGraw-Hill, we understand that getting the most from new technology can be challenging. That’s why our services don’t stop after you purchase our products. You can e-mail our product specialists 24 hours a day to get product-training online. Or you can search our knowledge bank of frequently asked questions on our support website. For customer support, call 800-331-5094, e-mail [emailprotected], or visit www.mhhe .com/support. One of our technical support analysts will be able to assist you in a timely fashion.

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W H AT S O F T WA R E I S AVA I L A B L E W I T H THIS TEXT?

MEGASTAT® FOR MICROSOFT EXCEL® MegaStat® by J. B. Orris of Butler University is a full-featured Excel statistical analysis add-in that is available on the MegaStat website at www.mhhe.com/megastat (for purchase). MegaStat works with recent versions of Microsoft Excel® (Windows and Mac OS X). See the website for details on supported versions. Once installed, MegaStat will always be available on the Excel add-ins ribbon with no expiration date or data limitations. MegaStat performs statistical analyses within an Excel workbook. When a MegaStat menu item is selected, a dialog box pops up for data selection and options. Since MegaStat is an easy-to-use extension of Excel, students can focus on learning statistics without being distracted by the software. Ease-of-use features include Auto Expand for quick data selection and Auto Label detect. MegaStat does most calculations found in introductory statistics textbooks, such as descriptive statistics, frequency distributions, and probability calculations as well as hypothesis testing, ANOVA chi-square, and regression (simple and multiple). MegaStat output is carefully formatted and appended to an output worksheet. Video tutorials are included that provide a walkthrough using MegaStat for typical business statistics topics. A context-sensitive help system is built into MegaStat and a User’s Guide is included in PDF format.

MINITAB®/SPSS®/JMP® Minitab® Student Version 14, SPSS® Student Version 18.0, and JMP® Student Edition Version 8 are software tools that are available to help students solve the business statistics exercises in the text. Each can be packaged with any McGraw-Hill business statistics text.

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WHAT R E SO U RC E S AR E AVAI L AB LE FO R I N STR UC TO R S?

ONLINE LEARNING CENTER: www.mhhe.com/lind16e The Online Learning Center (OLC) provides the instructor with a complete Instructor’s Manual in Word format, the complete Test Bank in both Word files and computerized EZ Test format, Instructor PowerPoint slides, text art files, an introduction to ALEKS®, an introduction to McGraw-Hill Connect Business StatisticsTM, and more.

All test bank questions are available in an EZ Test electronic format. Included are a number of multiple-choice, true/false, and short-answer questions and problems. The answers to all questions are given, along with a rating of the level of difficulty, chapter goal the question tests, Bloom’s taxonomy question type, and the AACSB knowledge category.

WebCT/Blackboard/eCollege All of the material in the Online Learning Center is also available in portable WebCT, Blackboard, or eCollege content “cartridges” provided free to adopters of this text.

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W H AT R E S O U R C E S A R E AVA I L A B L E F O R S T U D E N T S?

ALEKS is an assessment and learning program that provides individualized instruction in Business Statistics, Business Math, and Accounting. Available online, ALEKS interacts with students much like a skilled human tutor, with the ability to assess precisely a student’s knowledge and provide instruction on the exact topics the student is most ready to learn. By providing topics to meet individual students’ needs, allowing students to move between explanation and practice, correcting and analyzing errors, and defining terms, ALEKS helps students to master course content quickly and easily. ALEKS also includes a new instructor module with powerful, assignment-driven features and extensive content flexibility. ALEKS simplifies course management and allows instructors to spend less time with administrative tasks and more time directing student learning. To learn more about ALEKS, visit www.aleks.com.

ONLINE LEARNING CENTER: www.mhhe.com/lind16e The Online Learning Center (OLC) provides students with the following content: • • • • •

Quizzes PowerPoints Data sets/files Appendixes Chapter 20

BUSINESS STATISTICS CENTER (BSC): www.mhhe.com/bstat The BSC contains links to statistical publications and resources, software downloads, learning aids, statistical websites and databases, and McGraw-Hill product websites and online courses.

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AC K N O W L E D G M E N T S

This edition of Statistical Techniques in Business and Economics is the product of many people: students, colleagues, reviewers, and the staff at McGraw-Hill/Irwin. We thank them all. We wish to express our sincere gratitude to the survey and focus group participants, and the reviewers:

Reviewers Sung K. Ahn Washington State University– Pullman Vaughn S. Armstrong Utah Valley University Scott Bailey Troy University Douglas Barrett University of North Alabama Arnab Bisi Purdue University Pamela A. Boger Ohio University–Athens Emma Bojinova Canisius College Ann Brandwein Baruch College Giorgio Canarella California State University–Los Angeles Lee Cannell El Paso Community College James Carden University of Mississippi Mary Coe St. Mary College of California Anne Davey Northeastern State University Neil Desnoyers Drexel University Nirmal Devi Embry Riddle Aeronautical University David Doorn University of Minnesota–Duluth Ronald Elkins Central Washington University Vickie Fry Westmoreland County Community College Xiaoning Gilliam Texas Tech University

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Mark Gius Quinnipiac University Clifford B. Hawley West Virginia University Peter M. Hutchinson Saint Vincent College Lloyd R. Jaisingh Morehead State University Ken Kelley University of Notre Dame Mark Kesh University of Texas Melody Kiang California State University–Long Beach Morris Knapp Miami Dade College David G. Leupp University of Colorado–Colorado State Teresa Ling Seattle University Cecilia Maldonado Georgia Southwestern State University John D. McGinnis Pennsylvania State–Altoona Mary Ruth J. McRae Appalachian State University Jackie Miller The Ohio State University Carolyn Monroe Baylor University Valerie Muehsam Sam Houston State University Tariq Mughal University of Utah Elizabeth J. T. Murff Eastern Washington University Quinton Nottingham Virginia Polytechnic Institute and State University René Ordonez Southern Oregon University

Ed Pappanastos Troy University Michelle Ray Parsons Aims Community College Robert Patterson Penn State University Joseph Petry University of Illinois at UrbanaChampaign Germain N. Pichop Oklahoma City Community College Tammy Prater Alabama State University Michael Racer University of Memphis Darrell Radson Drexel University Steven Ramsier Florida State University Emily N. Roberts University of Colorado–Denver Christopher W. Rogers Miami Dade College Stephen Hays Russell Weber State University Martin Sabo Community College of Denver Farhad Saboori Albright College Amar Sahay Salt Lake Community College and University of Utah Abdus Samad Utah Valley University Nina Sarkar Queensborough Community College Roberta Schini West Chester University of Pennsylvania Robert Smidt California Polytechnic State University Gary Smith Florida State University

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Stanley D. Stephenson Texas State University–San Marcos Debra Stiver University of Nevada–Reno Bedassa Tadesse University of Minnesota–Duluth Stephen Trouard Mississippi College Elzbieta Trybus California State University– Northridge Daniel Tschopp Daemen College Sue Umashankar University of Arizona Bulent Uyar University of Northern Iowa Jesus M. Valencia Slippery Rock University Joseph Van Matre University of Alabama at Birmingham Raja Vatti St. John’s University Holly Verhasselt University of Houston–Victoria Angie Waits Gadsden State Community College Bin Wang St. Edwards University Kathleen Whitcomb University of South Carolina Blake Whitten University of Iowa Oliver Yu San Jose State University Zhiwei Zhu University of Louisiana

Survey and Focus Group Participants Nawar Al-Shara American University Charles H. Apigian Middle Tennessee State University Nagraj Balakrishnan Clemson University

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Philip Boudreaux University of Louisiana at Lafayette Nancy Brooks University of Vermont Qidong Cao Winthrop University Margaret M. Capen East Carolina University Robert Carver Stonehill College Jan E. Christopher Delaware State University James Cochran Louisiana Tech University Farideh Dehkordi-Vakil Western Illinois University Brant Deppa Winona State University Bernard Dickman Hofstra University Casey DiRienzo Elon University Erick M. Elder University of Arkansas at Little Rock Nicholas R. Farnum California State University–Fullerton K. Renee Fister Murray State University Gary Franko Siena College Maurice Gilbert Troy State University Deborah J. Gougeon University of Scranton Christine Guenther Pacific University Charles F. Harrington University of Southern Indiana Craig Heinicke Baldwin-Wallace College George Hilton Pacific Union College Cindy L. Hinz St. Bonaventure University Johnny C. Ho Columbus State University

Shaomin Huang Lewis-Clark State College J. Morgan Jones University of North Carolina at Chapel Hill Michael Kazlow Pace University John Lawrence California State University–Fullerton Sheila M. Lawrence Rutgers, The State University of New Jersey Jae Lee State University of New York at New Paltz Rosa Lemel Kean University Robert Lemke Lake Forest College Francis P. Mathur California State Polytechnic University, Pomona Ralph D. May Southwestern Oklahoma State University Richard N. McGrath Bowling Green State University Larry T. McRae Appalachian State University Dragan Miljkovic Southwest Missouri State University John M. Miller Sam Houston State University Cameron Montgomery Delta State University Broderick Oluyede Georgia Southern University Andrew Paizis Queens College Andrew L. H. Parkes University of Northern Iowa Paul Paschke Oregon State University Srikant Raghavan Lawrence Technological University Surekha K. B. Rao Indiana University Northwest

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Timothy J. Schibik University of Southern Indiana Carlton Scott University of California, Irvine Samuel L. Seaman Baylor University Scott J. Seipel Middle Tennessee State University Sankara N. Sethuraman Augusta State University Daniel G. Shimshak University of Massachusetts, Boston Robert K. Smidt California Polytechnic State University

William Stein Texas A&M University Robert E. Stevens University of Louisiana at Monroe Debra Stiver University of Nevada–Reno Ron Stunda Birmingham-Southern College Edward Sullivan Lebanon Valley College Dharma Thiruvaiyaru Augusta State University Daniel Tschopp Daemen College Bulent Uyar University of Northern Iowa

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Lee J. Van Scyoc University of Wisconsin–Oshkosh Stuart H. Warnock Tarleton State University Mark H. Witkowski University of Texas at San Antonio William F. Younkin University of Miami Shuo Zhang State University of New York, Fredonia Zhiwei Zhu University of Louisiana at Lafayette

Their suggestions and thorough reviews of the previous edition and the manuscript of this edition make this a better text. Special thanks go to a number of people. Professor Malcolm Gold, Avila University, reviewed the page proofs and the solutions manual, checking text and exercises for accuracy. Professor Jose Lopez–Calleja, Miami Dade College–Kendall, prepared the test bank. Professor Vickie Fry, Westmoreland County Community College, accuracy checked the Connect exercises. We also wish to thank the staff at McGraw-Hill. This includes Thomas Hayward, Senior Brand Manager; Kaylee Putbrese, Development Editor; Diane Nowaczyk, Content Project Manager; and others we do not know personally, but who have made valuable contributions.

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E N H A N C E M E N T S T O S TAT I S T I C A L T E C H N I Q U E S I N B U S I N E S S & E C O N O M I C S , 16 E

MAJOR CHANGES MADE TO INDIVIDUAL CHAPTERS: CHAPTER 1 What Is Statistics? • New photo and chapter opening exercise on the Nook Color sold by Barnes & Noble.

• Revised Self-Review 6–4 applying the binomial distribution. • New exercise 10 using the number of “underwater” loans. • New exercise using a raffle at a local golf club to demonstrate probability and expected returns.

CHAPTER 7 Continuous Probability Distributions

• New introduction with new graphic showing the increasing amount of information collected and processed with new technologies.

• Updated Statistics in Action.

• New ordinal scale example based on rankings of states based on business climate.

• Revised explanation of the Empirical Rule as it relates to the normal distribution.

• Revised Self-Review 7–2 based on daily personal water consumption.

• The chapter includes several new examples. • Chapter is more focused on the revised learning objectives and improving the chapter’s flow.

CHAPTER 8 Sampling Methods and the Central Limit Theorem

• Revised exercise 17 is based on economic data.

• New example of simple random sampling and the application of the table of random numbers.

CHAPTER 2 Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation

• The discussions of systematic random, stratified random, and cluster sampling have been revised. • Revised exercise 44 based on the price of a gallon of milk.

• Revised Self-Review 2–3 to include data. • Updated the company list in revised exercise 38. • New or revised exercises 45, 47, and 48.

CHAPTER 9 Estimation and Confidence Intervals • New Statistics in Action describing EPA fuel economy. • New separate section on point estimates.

CHAPTER 3 Describing Data: Numerical Measures • Reorganized chapter based on revised learning objectives. • Replaced the mean deviation with more emphasis on the variance and standard deviation. • Updated statistics in action.

• Integration and application of the central limit theorem. • A revised simulation demonstrating the interpretation of confidence level. • New presentation on using the t table to find z values. • A revised discussion of determining the confidence interval for the population mean. • Expanded section on calculating sample size.

CHAPTER 4 Describing Data: Displaying and Exploring Data • Updated exercise 22 with 2012 New York Yankee player salaries.

CHAPTER 5 A Survey of Probability Concepts • New explanation of odds compared to probabilities. • New exercise 21. • New example/solution for demonstrating contingency tables and tree diagrams.

• New exercise 12 (milk consumption).

CHAPTER 10 One-Sample Tests of Hypothesis • New example/solution involving airport parking. • Revised software solution and explanation of p-values. • New exercises 17 (daily water consumption) and 19 (number of text messages by teenagers). • Conducting a test of hypothesis about a population proportion is moved to Chapter 15.

• New contingency table exercise 31.

• New example introducing the concept of hypothesis testing.

• Revised example/solution demonstrating the combination formula.

• Sixth step added to the hypothesis testing procedure emphasizing the interpretation of the hypothesis test results.

CHAPTER 6 Discrete Probability Distributions

CHAPTER 11 Two-Sample Tests of Hypothesis

• Revised the section on the binomial distribution.

• New introduction to the chapter.

• Revised example/solution demonstrating the binomial distribution.

• Section of two-sample tests about proportions moved to Chapter 15.

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• Changed subscripts in example/solution for easier understanding. • Updated exercise with 2012 New York Yankee player salaries.

CHAPTER 12 Analysis of Variance • New introduction to the chapter. • New exercise 24 using the speed of browsers to search the Internet. • Revised exercise 33 comparing learning in traditional versus online courses. • New section on Comparing Two Population Variances. • New example illustrating the comparison of variances. • Revised section on two-way ANOVA with interaction with new examples and revised example/solution. • Revised the names of the airlines in the one-way ANOVA example. • Changed the subscripts in example/solution for easier understanding. • New exercise 30 (flight times between Los Angeles and SanFrancisco).

CHAPTER 13 Correlation and Linear Regression

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CHAPTER 15 Nonparametric Methods: Nominal Level Hypothesis Tests • Moved and renamed chapter. • Moved one-sample and two-sample tests of proportions from Chapters 10 and 11 to Chapter 15. • New example introducing goodness-of-fit tests. • Removed the graphical methods to evaluate normality. • Revised section on contingency table analysis with a new example/solution. • Revised Data Set exercises.

CHAPTER 16 Nonparametric Methods: Analysis of Ordinal Data • Moved and renamed chapter. • New example/solution and self-review demonstrating a hypothesis test about the median. • New example/solution demonstrating the rank-order correlation.

CHAPTER 17 Index Numbers • Moved chapter to follow nonparametric statistics. • Updated dates, illustrations, and examples.

• Rewrote the introduction section to the chapter.

• Revised example/solution demonstrating the use of the Production Price Index to deflate sales dollars.

• The data used as the basis for the North American Copier Sales example/solution used throughout the chapter has been changed and expanded to 15 observations to more clearly demonstrate the chapter’s learning objectives.

• Revised example/solution demonstrating the comparison of the Dow Jones Industrial Average and the Nasdaq using indexing.

• Revised section on transforming data using the economic relationship between price and sales. • New exercises 35 (transforming data), 36 (Masters prizes and scores), 43 (2012 NFL points scored versus points allowed), 44 (store size and sales), and 61 (airline distance and fare).

CHAPTER 14 Multiple Regression Analysis • Rewrote the section on evaluating the multiple regression equation. • More emphasis on the regression ANOVA table. • Enhanced the discussion of the p-value in decision making. • More emphasis on calculating the variance inflation factor to evaluate multicollinearity.

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• New self-review about using indexes to compare two different measures over time. • Revised Data Set Exercise.

CHAPTER 18 Time Series and Forecasting • Moved chapter to follow nonparametric statistics and index numbers. • Updated dates, illustrations, and examples. • Revised section on the components of a time series. • Revised graphics for better illustration.

CHAPTER 19 Statistical Process Control and Quality Management • Updated 2012 Malcolm Baldrige National Quality Award winners.

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BRIEF CONTENTS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

What Is Statistics?

1

Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation 17 Describing Data: Numerical Measures

50

Describing Data: Displaying and Exploring Data A Survey of Probability Concepts

131

Discrete Probability Distributions

173

Continuous Probability Distributions

93

206

Review Section

Sampling Methods and the Central Limit Theorem Estimation and Confidence Intervals

247

279

One-Sample Tests of Hypothesis

315

Two-Sample Tests of Hypothesis

348

Analysis of Variance

Review Section

Review Section

379

Review Section

Correlation and Linear Regression Multiple Regression Analysis

426

476

Review Section

Nonparametric Methods: Nominal Level Hypothesis Tests Nonparametric Methods: Analysis of Ordinal Data Index Numbers

570

Review Section

608

Time Series and Forecasting

639

Review Section

Statistical Process Control and Quality Management An Introduction to Decision Theory

716

816

Photo Credits Index

682

On the website: www.mhhe.com/lind16e

Appendixes: Data Sets, Tables, Software Commands, Answers Glossary

533

822

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CONTENTS

A Note from the Authors

vi

1 What Is Statistics?

1

EXERCISES

40

Chapter Summary 41

Introduction 2

Chapter Exercises 42

Why Study Statistics? 2

Data Set Exercises 49

What Is Meant by Statistics? 3 Types of Statistics 4 Descriptive Statistics 4 Inferential Statistics 5

3 Describing Data: Numerical Measures

Types of Variables 6

Introduction 51

Levels of Measurement 7

Measures of Location 51

Nominal-Level Data 7 Ordinal-Level Data 8 Interval-Level Data 9 Ratio-Level Data 10 EXERCISES

The Population Mean 52 The Sample Mean 53 Properties of the Arithmetic Mean

11

EXERCISES

Ethics and Statistics 12

EXERCISES

Chapter Summary 13 Data Set Exercises 16

EXERCISES

2 Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation 17 Introduction 18 Relative Class Frequencies

Graphic Presentation of Qualitative Data 20 24 30

31

EXERCISES

67

Why Study Dispersion? 68

72

Population Variance 73 Population Standard Deviation

75

75

Sample Variance and Standard Deviation 76 Software Solution 77 78

Interpretation and Uses of the Standard Deviation 78

34

Chebyshev’s Theorem 78 The Empirical Rule 79

36

Cumulative Frequency Distributions

65

The Geometric Mean 65

EXERCISES

Graphic Presentation of a Frequency Distribution 32

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EXERCISES

EXERCISES

Constructing Frequency Distributions 25

EXERCISES

63

The Weighted Mean 64

EXERCISES

20

Relative Frequency Distribution

62

Software Solution

Range 69 Variance 70

Constructing Frequency Tables 19

Histogram 32 Frequency Polygon

60

The Relative Positions of the Mean, Median, and Mode 61

Chapter Exercises 14

EXERCISES

55

The Median 56 The Mode 58

Computer Software Applications 12

EXERCISES

54

37

EXERCISES

80

50

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CONTENTS

The Mean and Standard Deviation of Grouped Data 81 Arithmetic Mean of Grouped Data 81 Standard Deviation of Grouped Data 82 EXERCISES

84

Pronunciation Key 87

EXERCISES

Chapter Exercises 87

Special Rule of Multiplication 146 General Rule of Multiplication 147 Contingency Tables 149 93

EXERCISES

Stem-and-Leaf Displays 96

154

100

Quartiles, Deciles, and Percentiles

159

Principles of Counting 160

Measures of Position 102 102

105

Box Plots 106

The Multiplication Formula 160 The Permutation Formula 161 The Combination Formula 163 EXERCISES

165

Chapter Summary 165 108

Skewness 109

Pronunciation Key 166 Chapter Exercises 166

113

Describing the Relationship between Two Variables 114 Contingency Tables 116 EXERCISES

152

Bayes’ Theorem 156

Dot Plots 94

EXERCISES

Tree Diagrams EXERCISES

Introduction 94

EXERCISES

145

Rules of Multiplication to Calculate Probability 146

Data Set Exercises 91

EXERCISES

139

Special Rule of Addition 140 Complement Rule 142 The General Rule of Addition 143

Chapter Summary 85

EXERCISES

EXERCISES

Rules of Addition for Computing Probabilities 140

Ethics and Reporting Results 85

4 Describing Data: Displaying and Exploring Data

Classical Probability 135 Empirical Probability 136 Subjective Probability 138

117

Chapter Summary 119 Pronunciation Key 119 Chapter Exercises 120 Data Set Exercises 125

A REVIEW OF CHAPTERS 1–4 125 PROBLEMS 126 CASES 128 PRACTICE TEST 129

5 A Survey of Probability Concepts 131 Introduction 132 What Is a Probability? 133 Approaches to Assigning Probabilities 135

Data Set Exercises 171

6 Discrete Probability Distributions 173 Introduction 174 What Is a Probability Distribution? 174 Random Variables 176 Discrete Random Variable 177 Continuous Random Variable 177 The Mean, Variance, and Standard Deviation of a Discrete Probability Distribution 178 Mean 178 Variance and Standard Deviation EXERCISES

178

180

Binomial Probability Distribution 182 How Is a Binomial Probability Computed? Binomial Probability Tables 185 EXERCISES

188

Cumulative Binomial Probability Distributions 189 EXERCISES

190

Hypergeometric Probability Distribution 191

183

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CONTENTS

EXERCISES

Simple Random Sampling 249 Systematic Random Sampling 252 Stratified Random Sampling 252 Cluster Sampling 253

194

Poisson Probability Distribution 194 EXERCISES

199

Chapter Summary 199

EXERCISES

254

Chapter Exercises 200

Sampling “Error” 256

Data Set Exercises 205

Sampling Distribution of the Sample Mean 258 EXERCISES

7 Continuous Probability Distributions 206

EXERCISES

268

Using the Sampling Distribution of the Sample Mean 269

Introduction 207 The Family of Uniform Probability Distributions 207 EXERCISES

261

The Central Limit Theorem 262

EXERCISES

272

Chapter Summary 272

210

The Family of Normal Probability Distributions 211

Pronunciation Key 273

The Standard Normal Probability Distribution 214

Data Set Exercises 278

Chapter Exercises 273

Applications of the Standard Normal Distribution 215 The Empirical Rule 215 EXERCISES

9 Estimation and Confidence Intervals

217

Finding Areas under the Normal Curve EXERCISES

221

EXERCISES

223

EXERCISES

226

217

Population Standard Deviation, Known s A Computer Simulation 286

Continuity Correction Factor 227 How to Apply the Correction Factor

229

230 235

288

EXERCISES

296

A Confidence Interval for a Population Proportion 297 EXERCISES

Chapter Summary 236

300

Choosing an Appropriate Sample Size 300

Chapter Exercises 237 Data Set Exercises 241

A REVIEW OF CHAPTERS 5–7 242 PROBLEMS 242

Sample Size to Estimate a Population Mean 301 Sample Size to Estimate a Population Proportion 302 EXERCISES

304

Finite-Population Correction Factor 304 EXERCISES

CASES 243

306

Chapter Summary 307

PRACTICE TEST 245

Chapter Exercises 308

8 Sampling Methods and the Central Limit Theorem 247 Introduction 248

Data Set Exercises 311

A REVIEW OF CHAPTERS 8–9 312 PROBLEMS 312 CASE 313

Sampling Methods 248 Reasons to Sample

EXERCISES

282

Population Standard Deviation, s Unknown 289

The Family of Exponential Distributions 231 EXERCISES

Point Estimate for a Population Mean 280 Confidence Intervals for a Population Mean 281

The Normal Approximation to the Binomial 226

EXERCISES

279

Introduction 280

248

PRACTICE TEST 313

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CONTENTS

Comparing Dependent and Independent Samples 368

10 One-Sample Tests of Hypothesis 315

EXERCISES

370

Introduction 316

Chapter Summary 371

What Is a Hypothesis? 316

Pronunciation Key 372

What Is Hypothesis Testing? 317

Chapter Exercises 372

Six-Step Procedure for Testing a Hypothesis 317

Data Set Exercises 378

Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1) 318 Step 2: Select a Level of Significance 319 Step 3: Select the Test Statistic 320 Step 4: Formulate the Decision Rule 320 Step 5: Make a Decision 321 Step 6: Interpret the Result 322 One-Tailed and Two-Tailed Tests of Significance 322 Testing for a Population Mean: Known Population Standard Deviation 324

EXERCISES

The F Distribution 380 Testing a Hypothesis of Equal Population Variances 381 EXERCISES

385

ANOVA: Analysis of Variance 385

EXERCISES

394 397

Two-Way Analysis of Variance 399

336

EXERCISES 337

403

Two-Way ANOVA with Interaction 404

338

Interaction Plots 404 Testing for Interaction 405 Hypothesis Tests for Interaction

Type II Error 339 EXERCISES

Comparing Two Population Variances 380

Inferences about Pairs of Treatment Means 395

330

Testing for a Population Mean: Population Standard Deviation Unknown 331 A Software Solution

Introduction 380

EXERCISES

p-Value in Hypothesis Testing 328

EXERCISES

342

EXERCISES

Chapter Summary 342

407

409

Chapter Summary 410

Pronunciation Key 343

Pronunciation Key 412

Chapter Exercises 344

Chapter Exercises 412

Data Set Exercises 347

Data Set Exercises 421

11 Two-Sample Tests of Hypothesis 348

A REVIEW OF CHAPTERS 10–12 421 PROBLEMS 422

Introduction 349

CASES 424

Two-Sample Tests of Hypothesis: Independent Samples 349 EXERCISES

PRACTICE TEST 424

354

Comparing Population Means with Unknown Population Standard Deviations 355 Two-Sample Pooled Test EXERCISES

355

364

Two-Sample Tests of Hypothesis: Dependent Samples 364

13 Correlation and Linear Regression 426 Introduction 427

359

Unequal Population Standard Deviations EXERCISES

379

ANOVA Assumptions 385 The ANOVA Test 387

A Two-Tailed Test 324 A One-Tailed Test 327 EXERCISES

12 Analysis of Variance

What Is Correlation Analysis? 427 361

The Correlation Coefficient 430 EXERCISES

435

Testing the Significance of the Correlation Coefficient 437

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CONTENTS

EXERCISES

Variation in Residuals Same for Large and Small yˆ Values 496 Distribution of Residuals 496 Multicollinearity 497 Independent Observations 499

440

Regression Analysis 440 Least Squares Principle 441 Drawing the Regression Line 443 EXERCISES

446

Qualitative Independent Variables 499

Testing the Significance of the Slope 448 EXERCISES

Regression Models with Interaction 502

450

Stepwise Regression 504

Evaluating a Regression Equation’s Ability to Predict 450

EXERCISES

Review of Multiple Regression 508

The Standard Error of Estimate 450 The Coefficient of Determination 451 EXERCISES

Chapter Summary 514 Pronunciation Key 516

452

Relationships among the Correlation Coefficient, the Coefficient of Determination, and the Standard Error of Estimate 453 EXERCISES

454

Assumptions Underlying Linear Regression Constructing Confidence and Prediction Intervals 456

Data Set Exercises 527

PROBLEMS 529 455

CASES 530 PRACTICE TEST 531

459

Transforming Data 459 EXERCISES

Chapter Exercises 516

A REVIEW OF CHAPTERS 13–14 528

Interval Estimates of Prediction 455

EXERCISES

506

462

Chapter Summary 463

15 Nonparametric Methods: Nominal Level Hypothesis Tests 533

Pronunciation Key 465

Introduction 534

Chapter Exercises 465

Test a Hypothesis of a Population Proportion 534

Data Set Exercises 474

14 Multiple Regression Analysis 476 Introduction 477 Multiple Regression Analysis 477 EXERCISES

481

Evaluating a Multiple Regression Equation 482 The ANOVA Table 483 Multiple Standard Error of Estimate 484 Coefficient of Multiple Determination 484 Adjusted Coefficient of Determination 485 EXERCISES

486

Inferences in Multiple Linear Regression 487 Global Test: Testing the Multiple Regression Model 487 Evaluating Individual Regression Coefficients 489 EXERCISES

537

EXERCISES

542

Goodness-of-Fit Tests: Comparing Observed and Expected Frequency Distributions 543 Hypothesis Test of Equal Expected Frequencies 543 EXERCISES

548

Hypothesis Test of Unequal Expected Frequencies 549 Limitations of Chi-Square 551 EXERCISES

553

Testing the Hypothesis That a Distribution Is Normal 554 EXERCISES

557

Contingency Table Analysis 558 EXERCISES

561

Chapter Summary 562

493

Evaluating the Assumptions of Multiple Regression 494 Linear Relationship

EXERCISES

Two-Sample Tests about Proportions 538

494

Pronunciation Key 563 Chapter Exercises 563 Data Set Exercises 569

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CONTENTS

16 Nonparametric Methods: Analysis of Ordinal Data

Paasche Price Index 618 Fisher’s Ideal Index 619 EXERCISES 570

Introduction 571 The Sign Test 571 EXERCISES

580

Wilcoxon Signed-Rank Test for Dependent Populations 580 EXERCISES

593

Rank-Order Correlation 595

Special Uses of the Consumer Price Index 628 Shifting the Base 630 633

Chapter Summary 633 Chapter Exercises 634

18 Time Series and Forecasting 639 Introduction 640

Testing the Significance of rs 597 EXERCISES

626

Data Set Exercise 638

589

Kruskal-Wallis Test: Analysis of Variance by Ranks 589 EXERCISES

625

Consumer Price Index 627

EXERCISES

584

Wilcoxon Rank-Sum Test for Independent Populations 585 EXERCISES

622

Consumer Price Index 623 Producer Price Index 624 Dow Jones Industrial Average (DJIA) EXERCISES

578

Testing a Hypothesis about a Median 578 EXERCISES

621

Special-Purpose Indexes 622

575

Using the Normal Approximation to the Binomial 576 EXERCISES

Value Index EXERCISES

620

Components of a Time Series 640

598

Secular Trend 640 Cyclical Variation 641 Seasonal Variation 642 Irregular Variation 642

Chapter Summary 600 Pronunciation Key 601 Chapter Exercises 601

A Moving Average 643

Data Set Exercises 604

Weighted Moving Average 646 EXERCISES

A REVIEW OF CHAPTERS 15–16 604

649

Linear Trend 649

PROBLEMS 605

Least Squares Method

CASES 606

EXERCISES

PRACTICE TEST 607

650

652

Nonlinear Trends 653 EXERCISES

17 Index Numbers

Seasonal Variation 655 608

Introduction 609

661

Deseasonalizing Data 662

Why Convert Data to Indexes? 612 Construction of Index Numbers 612

Using Deseasonalized Data to Forecast 663 EXERCISES

614

665

The Durbin-Watson Statistic 665

Unweighted Indexes 614 Simple Average of the Price Indexes Simple Aggregate Index 616 Weighted Indexes 616 Laspeyres Price Index

Determining a Seasonal Index 656 EXERCISES

Simple Index Numbers 609

EXERCISES

655

615

EXERCISES

671

Chapter Summary 671 Chapter Exercises 671

616

Data Set Exercise 678

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CONTENTS

A REVIEW OF CHAPTERS 17–18 679 PROBLEMS 680 PRACTICE TEST 680

EXERCISES Opportunity Loss EXERCISES Expected Opportunity Loss

19 Statistical Process Control and Quality Management 682 Introduction 683 A Brief History of Quality Control 683 Six Sigma

686

EXERCISES Maximin, Maximax, and Minimax Regret Strategies Value of Perfect Information Sensitivity Analysis EXERCISES Decision Trees

Sources of Variation 686

Chapter Summary

Diagnostic Charts 687

Chapter Exercises

Pareto Charts 687 Fishbone Diagrams 689 EXERCISES

Purpose and Types of Quality Control Charts 691 Control Charts for Variables 691 Range Charts 694 In-Control and Out-of-Control Situations 696 EXERCISES

698

Attribute Control Charts 699 p-Charts 699 c-Bar Charts 702 EXERCISES

APPENDIXES

715

690

Appendix A: Data Sets 716 Appendix B: Tables 726 Appendix C: Software Commands 744 Appendix D: Answers to Odd-Numbered Chapter Exercises & Review Exercises & Solutions to Practice Tests 756 Appendix E: Answers to Self-Review 802

704

Acceptance Sampling 705 EXERCISES

709

Glossary 816

Chapter Summary 709

Photo Credits 822

Pronunciation Key 710

Index 823

Chapter Exercises 710

On the website: www.mhhe.com/lind16e

20 An Introduction to Decision Theory Introduction Elements of a Decision Decision Making under Conditions of Uncertainty Payoff Table Expected Payoff