Molly's candle shop has several retail stores in the northeast

The Quantitative Analysis Approach & Ex. Of Quantitative Analysis In Research

Part 1 and Part 2 must be at least 150 - 200 words unless otherwise

Part 1: The Quantitative Analysis Approach

1. For this discussion, begin by reviewing the technique of quantitative analysis in your textbook. Then, keeping this technique in mind, read the following quotes:

“Data do not give up their secrets easily. They must be tortured to confess.”—Jeff Hopper, Bell Labs

“Statistics is a body of methods for learning from experience.”—Lincoln Moses

“The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex worldwide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write.”—H.G. Wells

“The coming century is surely the century of data.”—David Donoho (2000)

“Another mistaken notion connected with the law of large numbers is the idea that an event is more or less likely to occur because it has or has not happened recently. The idea that the odds of an event with a fixed probability increase or decrease depending on recent occurrences of the event is called the gambler’s fallacy. For example, if Kerrich landed, say, 44 heads in the first 100 tosses, the coin would not develop a bias towards the tails in order to catch up! That’s what is at the root of such ideas as ‘her luck has run out’ and ‘he is due.’ That does not happen. For what it’s worth, a good streak doesn’t jinx you, and a bad one, unfortunately, does not mean better luck is in store.”―Leonard Mlodinow, The Drunkard’s Walk: How Randomness Rules Our Lives

“A certain elementary training in statistical method is becoming as necessary for everyone living in this world of today as reading and writing.”―H.G. Wells, World Brain

“The non-scientist in the street probably has a clearer notion of physics, chemistry and biology than of statistics, regarding statisticians as numerical philatelists, mere collector of numbers.”―Stephen Senn, Dicing with Death: Chance, Risk and Health

Quotes retrieved from www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search

2. Based on the above quotes, along with this week’s assigned readings and Instructor Guidance, discuss why quantitative analysis is important for describing data sets and presenting distribution information.

Part 2: Examples of Quantitative Analysis in Research

Locate an example of a research study that uses quantitative analysis. Explain what this statistical method allowed the researchers to accomplish and conclude in the study.

Required Resource

Text
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics. (17th ed.). Retrieved from http://connect.mheducation.com/class/

· Chapter 1: What is Statistics?

· Chapter 3: Describing Data: Numerical Measures

· Chapter 5: A Survey of Probability Concepts

· Chapter 6: Discrete Probability Concepts

Statistical Techniques in Business & Economics

LIND

MARCHAL

WATHEN

Seventeenth Edition

Statistical Techniques in

BUSINESS & ECONOMICS

The McGraw-Hill/Irwin Series in Operations and Decision Sciences

SUPPLY CHAIN MANAGEMENT

Benton Purchasing and Supply Chain Management Third Edition

Bowersox, Closs, Cooper, and Bowersox Supply Chain Logistics Management Fourth Edition

Burt, Petcavage, and Pinkerton Supply Management Eighth Edition

Johnson, Leenders, and Flynn Purchasing and Supply Management Fourteenth Edition

Simchi-Levi, Kaminsky, and Simchi-Levi Designing and Managing the Supply Chain: Concepts, Strategies, Case Studies Third Edition

PROJECT MANAGEMENT

Brown and Hyer Managing Projects: A Team-Based Approach First Edition

Larson and Gray Project Management: The Managerial Process Fifth Edition

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Fitzsimmons and Fitzsimmons Service Management: Operations, Strategy, Information Technology Eighth Edition

MANAGEMENT SCIENCE

Hillier and Hillier Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets Fifth Edition

Stevenson and Ozgur

Introduction to Management Science with Spreadsheets First Edition

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Jacobs, Berry, Whybark, and Vollmann Manufacturing Planning & Control for Supply Chain Management Sixth Edition

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Cooper and Schindler Business Research Methods Twelfth Edition

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Wilson, Keating, and John Galt Solutions, Inc. Business Forecasting Sixth Edition

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Kutner, Nachtsheim, and Neter Applied Linear Regression Models Fourth Edition

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Sterman Business Dynamics: Systems Thinking and Modeling for a Complex World First Edition

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Cachon and Terwiesch Matching Supply with Demand: An Introduction to Operations Management Third Edition

Finch Interactive Models for Operations and Supply Chain Management First Edition

Jacobs and Chase Operations and Supply Chain Management Fourteenth Edition

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Schroeder, Goldstein, and Rungtusanatham Operations Management in the Supply Chain: Decisions and Cases Sixth Edition

Stevenson Operations Management Eleventh Edition

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Bowerman, O’Connell, and Murphree Business Statistics in Practice Seventh Edition

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Lind, Marchal, and Wathen Basic Statistics for Business and Economics Eighth Edition

Lind, Marchal, and Wathen Statistical Techniques in Business and Economics Seventeenth Edition

Jaggia and Kelly Business Statistics: Communicating with Numbers First Edition

Jaggia and Kelly Essentials of Business Statistics: Communicating with Numbers First Edition

Statistical Techniques in

BUSINESS & ECONOMICS

S E V E N T E E N T H E D I T I O N

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

WILLIAM G. MARCHAL The University of Toledo

SAMUEL A. WATHEN Coastal Carolina University

STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS, SEVENTEENTH EDITION Published by McGraw-Hill Education, 2 Penn Plaza, New York, NY 10121. Copyright © 2018 by McGraw-Hill Education. All rights reserved. Printed in the United States of America. Previous editions © 2015, 2012, and 2010. 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 LWI 21 20 19 18 17 16

ISBN 978-1-259-66636-0 MHID 1-259-66636-0

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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

Names: Lind, Douglas A., author. | Marchal, William G., author. | Wathen, Samuel Adam. author. Title: 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. Other titles: Statistical techniques in business and economics Description: Seventeenth Edition. | Dubuque, IA : McGraw-Hill Education, [2017] | Revised edition of the authors’ Statistical techniques in business & economics, [2015] Identifiers: LCCN 2016054310| ISBN 9781259666360 (alk. paper) | ISBN 1259666360 (alk. paper) Subjects: LCSH: Social sciences—Statistical methods. | Economics—Statistical methods. | Commercial statistics. Classification: LCC HA29 .M268 2017 | DDC 519.5—dc23 LC record available at https://lccn.loc.gov/2016054310

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.

mheducation.com/highered

DEDICATION

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 Oscar Sambath Marchal, Julian Irving Horowitz, Cecilia Marchal Nicholson and Andrea.

William G. Marchal

To my wonderful family: Barb, Hannah, and Isaac.

Samuel A. Wathen

vi

Over the years, we 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 descriptive and infer- ential statistics. To illustrate the application of statistics, we use many examples and exercises that focus on business applications, but also 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, accel- erates 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 automati- cally 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 in real time.

Today, the practice of data analytics is widely applied to “big data.” The practice of data analytics requires skills and knowledge in several areas. Computer skills are needed to process large volumes of information. Analytical skills are needed to evaluate, summarize, organize, and analyze the information. Critical thinking skills are needed to interpret and communicate the results of processing the information.

Our text supports the development of basic data analytical skills. In this edition, we added a new section at the end of each chapter called Data Analytics. As you work through the text, this section provides the instructor and student with opportu- nities to apply statistical knowledge and statistical software to explore several busi- ness environments. Interpretation of the analytical results is an integral part of these exercises.

A variety of statistical software is available to complement our text. Microsoft Excel includes an add-in with many statistical analyses. Megastat is an add-in available for Microsoft Excel. Minitab and JMP are stand-alone statistical software available to down- load for either PC or MAC computers. In our text, Microsoft Excel, Minitab, and Megastat are used to illustrate statistical software analyses. When a software application is pre- sented, the software commands for the application are available in Appendix C. 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 interpre- tative ones, to assist the student in understanding and interpreting the statistical results. In addition, we 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 con- cepts, along with supporting interesting and relevant examples.

A N O T E F R O M T H E A U T H O R S

vii

WHAT’S NEW IN THE SEVENTEENTH EDITION? We have made many changes to examples and exercises throughout the text. The sec- tion on “Enhancements” to our text details them. The major change to the text is in response to user interest in the area of data analytics. Our approach is to provide in- structors and students with the opportunity to combine statistical knowledge, computer and statistical software skills, and interpretative and critical thinking skills. A set of new and revised exercises is included at the end of chapters 1 through 18 in a section titled “Data Analytics.”

In these sections, exercises refer to three data sets. The North Valley Real Estate sales data set lists 105 homes currently on the market. The Lincolnville School District bus data lists information on 80 buses in the school district’s bus fleet. The authors de- signed these data so that students will be able to use statistical software to explore the data and find realistic relationships in the variables. The Baseball Statistics for the 2016 season is updated from the previous edition.

The intent of the exercises is to provide the basis of a continuing case analysis. We suggest that instructors select one of the data sets and assign the corresponding exer- cises as each chapter is completed. Instructor feedback regarding student performance is important. Students should retain a copy of each chapter’s results and interpretations to develop a portfolio of discoveries and findings. These will be helpful as students progress through the course and use new statistical techniques to further explore the data. The ideal ending for these continuing data analytics exercises is a comprehensive report based on the analytical findings.

We know that working with a statistics class to develop a very basic competence in data analytics is challenging. Instructors will be teaching statistics. In addition, instruc- tors will be faced with choosing statistical software and supporting students in develop- ing or enhancing their computer skills. Finally, instructors will need to assess student performance based on assignments that include both statistical and written compo- nents. Using a mentoring approach may be helpful.

We hope that you and your students find this new feature interesting and engaging.

HOW ARE CHAPTERS ORGANIZED TO ENGAGE STUDENTS AND PROMOTE LEARNING?

Chapter Learning Objectives Each chapter begins with a set of learning objectives designed to pro- vide focus for the chapter and motivate student learning. These objectives, lo- cated in the margins next to the topic, indicate what the student should be able to do after completing each sec- tion in the chapter.

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

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

LO2-1 Summarize qualitative variables with frequency and relative frequency tables.

LO2-2 Display a frequency table using a bar or pie chart.

LO2-3 Summarize quantitative variables with frequency and relative frequency distributions.

LO2-4 Display a frequency distribution using a histogram or frequency polygon.

MERRILL LYNCH recently completed a study of online investment portfolios for a sample of clients. For the 70 participants in the study, organize these data into a frequency distribution. (See Exercise 43 and LO2-3.)

Describing Data: FREQUENCY TABLES, FREQUENCY DISTRIBUTIONS,

AND GRAPHIC PRESENTATION2

Source: © rido/123RF

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Introduction to the Topic Each chapter starts with a review of the important concepts of the previ- ous chapter and provides a link to the material in the current chapter. This step-by-step approach increases com- prehension by providing continuity across the concepts.

DESCRIBING DATA: FREQUENCY TABLES, FREQUENCY DISTRIBUTIONS, AND GRAPHIC PRESENTATION 19

INTRODUCTION The United States automobile retailing industry is highly competitive. It is dominated by megadealerships that own and operate 50 or more franchises, employ over 10,000 people, and generate several billion dollars in annual sales. Many of the top dealerships

are publicly owned with shares traded on the New York Stock Exchange or NASDAQ. In 2014, the largest megadealership was AutoNation (ticker symbol AN), followed by Penske Auto Group (PAG), Group 1 Automotive, Inc. (ticker symbol GPI), and the privately owned Van Tuyl Group.

These large corporations use statistics and analytics to summarize and analyze data and information to support their decisions. As an ex- ample, we will look at the Applewood Auto group. It owns four dealer- ships and sells a wide range of vehicles. These include the popular Korean brands Kia and Hyundai, BMW and Volvo sedans and luxury SUVs, and a full line of Ford and Chevrolet cars and trucks.

Ms. Kathryn Ball is a member of the senior management team at Applewood Auto Group, which has its corporate offices adjacent to Kane Motors. She is responsible for tracking and analyzing vehicle sales and

the profitability of those vehicles. Kathryn would like to summarize the profit earned on the vehicles sold with tables, charts, and graphs that she would review monthly. She wants to know the profit per vehicle sold, as well as the lowest and highest amount of profit. She is also interested in describing the demographics of the buyers. What are their ages? How many vehicles have they previously purchased from one of the Apple- wood dealerships? What type of vehicle did they purchase?

The Applewood Auto Group operates four dealerships:

• Tionesta Ford Lincoln sells Ford and Lincoln cars and trucks. • Olean Automotive Inc. has the Nissan franchise as well as the General Motors

brands of Chevrolet, Cadillac, and GMC Trucks. • Sheffield Motors Inc. sells Buick, GMC trucks, Hyundai, and Kia. • Kane Motors offers the Chrysler, Dodge, and Jeep line as well as BMW and Volvo.

Every month, Ms. Ball collects data from each of the four dealerships and enters them into an Excel spreadsheet. Last month the Applewood Auto Group sold 180 vehicles at the four dealerships. A copy of the first few observations appears to the left. The variables collected include:

• Age—the age of the buyer at the time of the purchase. • Profit—the amount earned by the dealership on the sale of each

vehicle. • Location—the dealership where the vehicle was purchased. • Vehicle type—SUV, sedan, compact, hybrid, or truck. • Previous—the number of vehicles previously purchased at any of the

four Applewood dealerships by the consumer.

The entire data set is available at the McGraw-Hill website (www.mhhe .com/lind17e) and in Appendix A.4 at the end of the text.

Source: © Justin Sullivan/Getty Images

CONSTRUCTING FREQUENCY TABLES Recall from Chapter 1 that techniques used to describe a set of data are called descrip- tive statistics. Descriptive statistics organize data to show the general pattern of the data, to identify where values tend to concentrate, and to expose extreme or unusual data values. The first technique we discuss is a frequency table.

LO2-1 Summarize qualitative variables with frequency and relative frequency tables.

FREQUENCY TABLE A grouping of qualitative data into mutually exclusive and collectively exhaustive classes showing the number of observations in each class.

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Example/Solution 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, “How can I apply this concept?”

DESCRIBING DATA: DISPLAYING AND EXPLORING DATA 95

INTRODUCTION Chapter 2 began our study of descriptive statistics. In order to transform raw or un- grouped data into a meaningful form, we organize the data into a frequency distribution. We present the frequency distribution in graphic form as a histogram or a frequency polygon. This allows us to visualize where the data tend to cluster, the largest and the smallest values, and the general shape of the data.

In Chapter 3, we first computed several measures of location, such as the mean, median, and mode. These measures of location allow us to report a typical value in the set of observations. We also computed several measures of dispersion, such as the range, variance, and standard deviation. These measures of dispersion allow us to de- scribe the variation or the spread in a set of observations.

We continue our study of descriptive statistics in this chapter. We study (1) dot plots, (2) stem-and-leaf displays, (3) percentiles, and (4) box plots. These charts and statistics give us additional insight into where the values are concentrated as well as the general shape of the data. Then we consider bivariate data. In bivariate data, we observe two variables for each individual or observation. Examples include the number of hours a student studied and the points earned on an examination; if a sampled product meets quality specifications and the shift on which it is manufactured; or the amount of electric- ity used in a month by a homeowner and the mean daily high temperature in the region for the month. These charts and graphs provide useful insights as we use business analytics to enhance our understanding of data.

DOT PLOTS Recall for the Applewood Auto Group data, we summarized the profit earned on the 180 vehicles sold with a frequency distribution using eight classes. When we orga- nized the data into the eight classes, we lost the exact value of the observations. A dot plot, on the other hand, groups the data as little as possible, and we do not lose the identity of an individual observation. To develop a dot plot, we display a dot for each observation along a horizontal number line indicating the possible values of the data. If there are identical observations or the observations are too close to be shown individually, the dots are “piled” on top of each other. This allows us to see the shape of the distribution, the value about which the data tend to cluster, and the largest and smallest observations. Dot plots are most useful for smaller data sets, whereas histo- grams tend to be most useful for large data sets. An example will show how to con- struct and interpret dot plots.

LO4-1 Construct and interpret a dot plot.

E X A M P L E

The service departments at Tionesta Ford Lincoln 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

Monday Tuesday Wednesday Thursday Friday Saturday

23 33 27 28 39 26 30 32 28 33 35 32 29 25 36 31 32 27 35 32 35 37 36 30

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Self-Reviews Self-Reviews are interspersed throughout each chapter and follow Example/Solution sec- tions. They help students mon- itor their progress and provide immediate reinforcement for that particular technique. An- swers are in Appendix E.

106 CHAPTER 4

calculate quartiles. Excel 2013 and Excel 2016 offer both methods. The Excel function, Quartile.exc, will result in the same answer as Equation 4–1. The Excel function, Quar- tile.inc, will result in the Excel Method answers.

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 pro- duced last hour are:

7.69 7.72 7.8 7.86 7.90 7.94 7.97 8.06 8.09

(a) What is the median weight? (b) Determine the weights corresponding to the first and third quartiles.

S E L F - R E V I E W 4–2

11. Determine the median and the first and third quartiles in the following data.

46 47 49 49 51 53 54 54 55 55 59

12. Determine the median and the first and third quartiles in the following data.

5.24 6.02 6.67 7.30 7.59 7.99 8.03 8.35 8.81 9.45 9.61 10.37 10.39 11.86 12.22 12.71 13.07 13.59 13.89 15.42

13. The Thomas Supply Company Inc. is a distributor of gas-powered generators. As with any business, the length of time customers take to pay their invoices is im- portant. Listed below, arranged from smallest to largest, is the time, in days, for a sample of The Thomas Supply Company Inc. invoices.

13 13 13 20 26 27 31 34 34 34 35 35 36 37 38 41 41 41 45 47 47 47 50 51 53 54 56 62 67 82

a. Determine the first and third quartiles. b. Determine the second decile and the eighth decile. c. Determine the 67th percentile.

14. Kevin Horn is the national sales manager for National Textbooks Inc. He has a sales staff of 40 who visit college professors all over the United States. Each Saturday morning he requires his sales staff to send him a report. This re- port includes, among other things, the number of professors visited during the previous week. Listed below, ordered from smallest to largest, are the number of visits last week.

38 40 41 45 48 48 50 50 51 51 52 52 53 54 55 55 55 56 56 57 59 59 59 62 62 62 63 64 65 66 66 67 67 69 69 71 77 78 79 79

a. Determine the median number of calls. b. Determine the first and third quartiles. c. Determine the first decile and the ninth decile. d. Determine the 33rd percentile.

E X E R C I S E S

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viii

ix

Statistics in Action Statistics in Action articles are scattered through- out the text, usually about two per chapter. They provide unique, interesting applications and his- torical insights in the field of statistics.

144 CHAPTER 5

The General Rule of Addition The outcomes of an experiment may not be mutually exclusive. For example, the Florida Tourist Commission selected a sample of 200 tourists who visited the state during the year. The survey revealed that 120 tourists went to Disney World and 100 went to Busch Gardens near Tampa. What is the probability that a person selected visited either Disney World or Busch Gardens? If the special rule of addition is used, the probability of selecting a tourist who went to Disney World is .60, found by 120/200. Similarly, the probability of a tourist going to Busch Gardens is .50. The sum of these probabilities is 1.10. We know, however, that this probability cannot be greater than 1. The explanation is that many tour- ists visited both attractions and are being counted twice! A check of the survey responses revealed that 60 out of 200 sampled did, in fact, visit both attractions.

To answer our question, “What is the probability a selected person visited either Disney World or Busch Gardens?” (1) add the probability that a tourist visited Disney World and the probability he or she visited Busch Gardens, and (2) subtract the proba- bility of visiting both. Thus:

P(Disney or Busch) = P(Disney) + P(Busch) − P(both Disney and Busch) = .60 + .50 − .30 = .80

When two events both occur, the probability is called a joint probability. The prob- ability (.30) that a tourist visits both attractions is an example of a joint probability.

© Rostislav Glinsky/Shutterstock.com

The following Venn diagram shows two events that are not mutually exclusive. The two events overlap to illustrate the joint event that some people have visited both attractions.

A sample of employees of Worldwide Enterprises is to be surveyed about a new health care plan. The employees are classified as follows:

Classification Event Number of Employees

Supervisors A 120 Maintenance B 50 Production C 1,460 Management D 302 Secretarial E 68

(a) What is the probability that the first person selected is: (i) either in maintenance or a secretary? (ii) not in management? (b) Draw a Venn diagram illustrating your answers to part (a). (c) Are the events in part (a)(i) complementary or mutually exclusive or both?

S E L F - R E V I E W 5–3

STATISTICS IN ACTION

If you wish to get some attention at the next gath- ering you attend, announce 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, the probability of a duplicate is .706. If there are 60 people in the room, the probability is .994 that at least two people share the same birthday. With as few as 23 people the chances are even, that is .50, that at least two people share the same birthday. Hint: To compute this, find the probability everyone was born on a different day and use the complement rule. Try this in your class.

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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.

A SURVEY OF PROBABILITY CONCEPTS 145

P (Disney) = .60 P (Busch) = .50

P (Disney and Busch) = .30

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

So the general rule of addition, which is used to compute the probability of two events that are not mutually exclusive, is:

GENERAL RULE OF ADDITION P(A or B) = P(A) + P(B) − P(A and B) [5–4]

For the expression P(A or B), the word or suggests that A may occur or B may occur. This also includes the possibility that A and B may occur. This use of or is sometimes called an inclusive. You could also write P(A or B or both) to emphasize that the union of the events includes the intersection of A and B.

If we compare the general and special rules of addition, the important difference is determining if the events are mutually exclusive. If the events are mutually exclusive, then the joint probability P(A and B) is 0 and we could use the special rule of addition. Other- wise, we must account for the joint probability and use the general rule of addition.

E X A M P L E

What is the probability that a card chosen at random from a standard deck of cards will be either a king or a heart?

S O L U T I O N

We may be inclined to add the probability of a king and the probability of a heart. But this creates a problem. If we do that, the king of hearts is counted with the kings and also with the hearts. So, if we simply add the probability of a king (there are 4 in a deck of 52 cards) to the probability of a heart (there are 13 in a deck of 52 cards) and report that 17 out of 52 cards meet the requirement, we have counted the king of hearts twice. We need to subtract 1 card from the 17 so the king of hearts is counted only once. Thus, there are 16 cards that are either hearts or kings. So the probability is 16/52 = .3077.

Card Probability Explanation

King P(A) = 4/52 4 kings in a deck of 52 cards Heart P(B) = 13/52 13 hearts in a deck of 52 cards King of Hearts P(A and B) = 1/52 1 king of hearts in a deck of 52 cards

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

A SURVEY OF PROBABILITY CONCEPTS 147

16. Two coins are tossed. If A is the event “two heads” and B is the event “two tails,” are A and B mutually exclusive? Are they complements?

17. The probabilities of the events A and B are .20 and .30, respectively. The probability that both A and B occur is .15. What is the probability of either A or B occurring?

18. Let P(X) = .55 and P(Y) = .35. Assume the probability that they both occur is .20. What is the probability of either X or Y occurring?

19. Suppose the two events A and B are mutually exclusive. What is the probability of their joint occurrence?

20. A student is taking two courses, history and math. The probability the student will pass the history course is .60, and the probability of passing the math course is .70. The probability of passing both is .50. What is the probability of passing at least one?

21. The aquarium at Sea Critters Depot contains 140 fish. Eighty of these fish are green swordtails (44 female and 36 male) and 60 are orange swordtails (36 female and 24 males). A fish is randomly captured from the aquarium:

a. What is the probability the selected fish is a green swordtail? b. What is the probability the selected fish is male? c. What is the probability the selected fish is a male green swordtail? d. What is the probability the selected fish is either a male or a green swordtail?

22. A National Park Service survey of visitors to the Rocky Mountain region revealed that 50% visit Yellowstone Park, 40% visit the Tetons, and 35% visit both.

a. What is the probability a vacationer will visit at least one of these attractions? b. What is the probability .35 called? c. Are the events mutually exclusive? Explain.

RULES OF MULTIPLICATION TO CALCULATE PROBABILITY In this section, we discuss the rules for computing the likelihood that two events both happen, or their joint probability. For example, 16% of the 2016 tax returns were pre- pared by H&R Block and 75% of those returns showed a refund. What is the likelihood a person’s tax form was prepared by H&R Block and the person received a refund? Venn diagrams illustrate this as the intersection of two events. To find the likelihood of two events happening, we use the rules of multiplication. There are two rules of multipli- cation: the special rule and the general rule.

Special Rule of Multiplication The special rule of multiplication requires that two events A and B are independent. Two events are independent if the occurrence of one event does not alter the probabil- ity of the occurrence of the other event.

LO5-4 Calculate probabilities using the rules of multiplication.

INDEPENDENCE The occurrence of one event has no effect on the probability of the occurrence of another event.

One way to think about independence is to assume that events A and B occur at differ- ent times. For example, when event B occurs after event A occurs, does A have any effect on the likelihood that event B occurs? If the answer is no, then A and B are independent events. To illustrate independence, suppose two coins are tossed. The outcome of a coin toss (head or tail) is unaffected by the outcome of any other prior coin toss (head or tail).

For two independent events A and B, the probability that A and B will both occur is found by multiplying the two probabilities. This is the special rule of multiplication and is written symbolically as:

SPECIAL RULE OF MULTIPLICATION P(A and B) = P(A)P(B) [5–5]

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Exercises Exercises are included after sec- tions within the chapter and at the end of the chapter. Section exercises cover the material stud- ied in the section. Many exercises have data files available to import into statistical software. They are indicated with the FILE icon. Answers to the odd-numbered exercises are in Appendix D.

DESCRIBING DATA: NUMERICAL MEASURES 79

INTERPRETATION AND USES OF THE STANDARD DEVIATION The standard deviation is commonly used as a measure to compare the spread in two or more sets of observations. For example, the standard deviation of the biweekly amounts invested in the Dupree Paint Company profit-sharing plan is computed to be $7.51. Suppose these employees are located in Georgia. If the standard deviation for a group of employees in Texas is $10.47, and the means are about the same, it indicates that the amounts invested by the Georgia employees are not dispersed as much as those in Texas (because $7.51 < $10.47). Since the amounts invested by the Georgia employees are clustered more closely about the mean, the mean for the Georgia em- ployees is a more reliable measure than the mean for the Texas group.

Chebyshev’s Theorem We have stressed that a small standard deviation for a set of values indicates that these values are located close to the mean. Conversely, a large standard deviation reveals that the observations are widely scattered about the mean. The Russian mathematician P. L. Chebyshev (1821–1894) developed a theorem that allows us to determine the minimum proportion of the values that lie within a specified number of standard deviations of the mean. For example, according to Chebyshev’s theorem, at least three out of every four, or 75%, of the values must lie between the mean plus two standard deviations and the mean minus two standard deviations. This relationship applies regardless of the shape of the distribution. Further, at least eight of nine values, or 88.9%, will lie between plus three standard deviations and minus three standard deviations of the mean. At least 24 of 25 values, or 96%, will lie between plus and minus five standard deviations of the mean.

Chebyshev’s theorem states:

LO3-5 Explain and apply Chebyshev’s theorem and the Empirical Rule.

STATISTICS IN ACTION

Most colleges report the “average class size.” This information can be mislead- ing because average class size can be found in several ways. If we find the number of students in each class at a particular university, the result is the mean number of students per class. If we compile a list of the class sizes for each student and find the mean class size, we might find the mean to be quite different. One school found the mean number of students in each of its 747 classes to be 40. But when

(continued)

CHEBYSHEV’S THEOREM For any set of observations (sample or population), the proportion of the values that lie within k standard deviations of the mean is at least 1 – 1/k2, where k is any value greater than 1.

For Exercises 47–52, do the following:

a. Compute the sample variance. b. Determine the sample standard deviation.

47. Consider these values a sample: 7, 2, 6, 2, and 3. 48. The following five values are a sample: 11, 6, 10, 6, and 7. 49. Dave’s Automatic Door, referred to in Exercise 37, installs automatic garage

door openers. Based on a sample, following are the times, in minutes, required to install 10 door openers: 28, 32, 24, 46, 44, 40, 54, 38, 32, and 42.

50. The sample of eight companies in the aerospace industry, referred to in Exer- cise 38, was surveyed as to their return on investment last year. The results are 10.6, 12.6, 14.8, 18.2, 12.0, 14.8, 12.2, and 15.6.

51. The Houston, Texas, Motel Owner Association conducted a survey regarding weekday motel rates in the area. Listed below is the room rate for business-class guests for a sample of 10 motels.

$101 $97 $103 $110 $78 $87 $101 $80 $106 $88

52. A consumer watchdog organization is concerned about credit card debt. A survey of 10 young adults with credit card debt of more than $2,000 showed they paid an average of just over $100 per month against their balances. Listed below are the amounts each young adult paid last month.

$110 $126 $103 $93 $99 $113 $87 $101 $109 $100

E X E R C I S E S

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Computer Output The text includes many software examples, using Excel, MegaStat®, and Minitab. The software results are illustrated in the chapters. Instructions for a particular software example are in Appendix C.

64 CHAPTER 3

E X A M P L E

Table 2–4 on page 26 shows the profit on the sales of 180 vehicles at Applewood Auto Group. Determine the mean and the median selling price.

S O L U T I O N

The mean, median, and modal amounts of profit are reported in the following output (highlighted in the screen shot). (Reminder: The instructions to create the output appear in the Software Commands in Appendix C.) There are 180 vehicles in the study, so using a calculator would be tedious and prone to error.

Software Solution We can use a statistical software package to find many measures of location.

a. What is the arithmetic mean of the Alaska unemployment rates? b. Find the median and the mode for the unemployment rates. c. Compute the arithmetic mean and median for just the winter (Dec–Mar) months.

Is it much different? 22. Big Orange Trucking is designing an information system for use in “in-cab”

communications. It must summarize data from eight sites throughout a region to describe typical conditions. Compute an appropriate measure of central location for the variables wind direction, temperature, and pavement.

City Wind Direction Temperature Pavement

Anniston, AL West 89 Dry Atlanta, GA Northwest 86 Wet Augusta, GA Southwest 92 Wet Birmingham, AL South 91 Dry Jackson, MS Southwest 92 Dry Meridian, MS South 92 Trace Monroe, LA Southwest 93 Wet Tuscaloosa, AL Southwest 93 Trace

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HOW DOES THIS TEXT REINFORCE STUDENT LEARNING?

x

BY CHAPTER

Chapter Summary Each chapter contains a brief summary of the chapter material, including vocab- ulary, definitions, and critical formulas.

202 CHAPTER 6

the number of transmission services, muffler replacements, and oil changes per day at Avellino’s Auto Shop. They follow Poisson distributions with means of 0.7, 2.0, and 6.0, respectively.

In summary, the Poisson distribution is a family of discrete distributions. All that is needed to construct a Poisson probability distribution is the mean number of defects, errors, or other random variable, designated as μ.

From actuary tables, Washington Insurance Company determined the likelihood that a man age 25 will die within the next year is .0002. If Washington Insurance sells 4,000 policies to 25-year-old men this year, what is the probability they will pay on exactly one policy?

S E L F - R E V I E W 6–6

31. In a Poisson distribution μ = 0.4. a. What is the probability that x = 0? b. What is the probability that x > 0?

32. In a Poisson distribution μ = 4. a. What is the probability that x = 2? b. What is the probability that x ≤ 2? c. What is the probability that x > 2?

33. Ms. Bergen is a loan officer at Coast Bank and Trust. From her years of experience, she estimates that the probability is .025 that an applicant will not be able to repay his or her installment loan. Last month she made 40 loans.

a. What is the probability that three loans will be defaulted? b. What is the probability that at least three loans will be defaulted?

34. Automobiles arrive at the Elkhart exit of the Indiana Toll Road at the rate of two per minute. The distribution of arrivals approximates a Poisson distribution.

a. What is the probability that no automobiles arrive in a particular minute? b. What is the probability that at least one automobile arrives during a particular

minute? 35. It is estimated that 0.5% of the callers to the Customer Service department of Dell

Inc. will receive a busy signal. What is the probability that of today’s 1,200 callers at least 5 received a busy signal?

36. In the past, schools in Los Angeles County have closed an average of 3 days each year for weather emergencies. What is the probability that schools in Los Angeles County will close for 4 days next year?

E X E R C I S E S

C H A P T E R S U M M A R Y

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:

μ = Σ[xP(x)] (6–1) B. The variance is equal to:

σ2 = Σ[(x − μ)2P(x)] (6–2)

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Pronunciation Key This section lists the mathematical symbol, its meaning, and how to pronounce it. We believe this will help the student retain the meaning of the symbol and generally en- hance course communications.

168 CHAPTER 5

P R O N U N C I A T I O N K E Y

SYMBOL MEANING PRONUNCIATION

P(A) Probability of A P of A

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 | B) Probability of A given B has happened P of A given B

nPr Permutation of n items selected r at a time Pnr

nCr Combination of n items selected r at a time Cnr

C H A P T E R E X E R C I S E S

47. The marketing research department at Pepsico plans to survey teenagers about a newly developed soft drink. Each will be asked to compare it with his or her favorite soft drink. a. What is the experiment? b. What is one possible event?

48. The number of times a particular event occurred in the past is divided by the number of occurrences. What is this approach to probability called?

49. The probability that the cause and the cure for all cancers will be discovered before the year 2020 is .20. What viewpoint of probability does this statement illustrate?

50. Berdine’s Chicken Factory has several stores in the Hilton Head, South Carolina, area. When interviewing applicants for server positions, the owner would like to in- clude information on the amount of tip a server can expect to earn per check (or bill). A study of 500 recent checks indicated the server earned the following amounts in tips per 8-hour shift.

Amount of Tip Number

$0 up to $ 20 200 20 up to 50 100 50 up to 100 75 100 up to 200 75 200 or more 50

Total 500

a. What is the probability of a tip of $200 or more? b. Are the categories “$0 up to $20,” “$20 up to $50,” and so on considered mutually

exclusive? c. If the probabilities associated with each outcome were totaled, what would that total be? d. What is the probability of a tip of up to $50? e. What is the probability of a tip of less than $200?

51. Winning all three “Triple Crown” races is considered the greatest feat of a pedigree racehorse. After a successful Kentucky Derby, Corn on the Cob is a heavy favorite at 2 to 1 odds to win the Preakness Stakes. a. If he is a 2 to 1 favorite to win the Belmont Stakes as well, what is his probability of

winning the Triple Crown? b. What do his chances for the Preakness Stakes have to be in order for him to be

“even money” to earn the Triple Crown? 52. The first card selected from a standard 52-card deck is a king.

a. If it is returned to the deck, what is the probability that a king will be drawn on the second selection?

b. If the king is not replaced, what is the probability that a king will be drawn on the second selection?

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Chapter Exercises 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/Lind17e. These files help students use statistical software to solve the exercises.

348 CHAPTER 10

The major characteristics of the t distribution are: 1. It is a continuous distribution. 2. It is mound-shaped and symmetrical. 3. It is flatter, or more spread out, than the standard normal distribution. 4. There is a family of t distributions, depending on the number of degrees of freedom.

V. There are two types of errors that can occur in a test of hypothesis. A. A Type I error occurs when a true null hypothesis is rejected.

1. The probability of making a Type I error is equal to the level of significance. 2. This probability is designated by the Greek letter α.

B. A Type II error occurs when a false null hypothesis is not rejected. 1. The probability of making a Type II error is designated by the Greek letter β. 2. The likelihood of a Type II error must be calculated comparing the hypothesized

distribution to an alternate distribution based on sample results.

P R O N U N C I A T I O N K E Y

SYMBOL MEANING PRONUNCIATION

H0 Null hypothesis H sub zero

H1 Alternate hypothesis H sub one

α/2 Two-tailed significance level Alpha divided by 2 xc Limit of the sample mean x bar sub c

μ0 Assumed population mean mu sub zero

C H A P T E R E X E R C I S E S

25. According to the local union president, the mean gross income of plumbers in the Salt Lake City area follows the normal probability distribution with a mean of $45,000 and a standard deviation of $3,000. A recent investigative reporter for KYAK TV found, for a sample of 120 plumbers, the mean gross income was $45,500. At the .10 significance level, is it reasonable to conclude that the mean income is not equal to $45,000? Deter- mine the p-value.

26. Rutter Nursery Company packages its pine bark mulch in 50-pound bags. From a long history, the production department reports that the distribution of the bag weights follows the normal distribution and the standard deviation of the packaging process is 3 pounds per bag. At the end of each day, Jeff Rutter, the production manager, weighs 10 bags and computes the mean weight of the sample. Below are the weights of 10 bags from today’s production.

45.6 47.7 47.6 46.3 46.2 47.4 49.2 55.8 47.5 48.5

a. Can Mr. Rutter conclude that the mean weight of the bags is less than 50 pounds? Use the .01 significance level.

b. In a brief report, tell why Mr. Rutter can use the z distribution as the test statistic. c. Compute the p-value.

27. A new weight-watching company, Weight Reducers International, advertises that those who join will lose an average of 10 pounds after the first two weeks. The standard devi- ation is 2.8 pounds. A random sample of 50 people who joined the weight reduction program revealed a mean loss of 9 pounds. At the .05 level of significance, can we conclude that those joining Weight Reducers will lose less than 10 pounds? Determine the p-value.

28. Dole Pineapple Inc. is concerned that the 16-ounce can of sliced pineapple is being overfilled. Assume the standard deviation of the process is .03 ounce. The quality-con- trol department took a random sample of 50 cans and found that the arithmetic mean weight was 16.05 ounces. At the 5% level of significance, can we conclude that the mean weight is greater than 16 ounces? Determine the p-value.

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Data Analytics The goal of the Data Analytics sec- tions is to develop analytical skills. The exercises present a real world context with supporting data. The data sets are printed in Appendix A and available to download from the text’s website www.mhhe.com/Lind17e. Statistical software is required to analyze the data and respond to the exercises. Each data set is used to explore questions and dis- cover findings that relate to a real world context. For each business context, a story is uncovered as students progress from chapters one to seventeen.

244 CHAPTER 7

68. In establishing warranties on HDTVs, the manufacturer wants to set the limits so that few will need repair at the manufacturer’s expense. On the other hand, the warranty period must be long enough to make the purchase attractive to the buyer. For a new HDTV, the mean number of months until repairs are needed is 36.84 with a standard deviation of 3.34 months. Where should the warranty limits be set so that only 10% of the HDTVs need repairs at the manufacturer’s expense?

69. DeKorte Tele-Marketing Inc. is considering purchasing a machine that randomly selects and automatically dials telephone numbers. DeKorte Tele-Marketing makes most of its calls during the evening, so calls to business phones are wasted. The manufacturer of the machine claims that its programming reduces the calling to business phones to 15% of all calls. To test this claim, the director of purchasing at DeKorte programmed the machine to select a sample of 150 phone numbers. What is the likelihood that more than 30 of the phone numbers selected are those of businesses, assuming the manu- facturer’s claim is correct?

70. A carbon monoxide detector in the Wheelock household activates once every 200 days on average. Assume this activation follows the exponential distribution. What is the probability that: a. There will be an alarm within the next 60 days? b. At least 400 days will pass before the next alarm? c. It will be between 150 and 250 days until the next warning? d. Find the median time until the next activation.

71. “Boot time” (the time between the appearance of the Bios screen to the first file that is loaded in Windows) on Eric Mouser’s personal computer follows an exponential distribu- tion with a mean of 27 seconds. What is the probability his “boot” will require: a. Less than 15 seconds? b. More than 60 seconds? c. Between 30 and 45 seconds? d. What is the point below which only 10% of the boots occur?

72. The time between visits to a U.S. emergency room for a member of the general popula- tion follows an exponential distribution with a mean of 2.5 years. What proportion of the population: a. Will visit an emergency room within the next 6 months? b. Will not visit the ER over the next 6 years? c. Will visit an ER next year, but not this year? d. Find the first and third quartiles of this distribution.

73. The times between failures on a personal computer follow an exponential distribution with a mean of 300,000 hours. What is the probability of: a. A failure in less than 100,000 hours? b. No failure in the next 500,000 hours? c. The next failure occurring between 200,000 and 350,000 hours? d. What are the mean and standard deviation of the time between failures?

D A T A A N A L Y T I C S

(The data for these exercises are available at the text website: www.mhhe.com/lind17e.)

74. Refer to the North Valley Real Estate data, which report information on homes sold during the last year. a. The mean selling price (in $ thousands) of the homes was computed earlier to be $357.0,

with a standard deviation of $160.7. Use the normal distribution to estimate the percent- age of homes selling for more than $500.000. Compare this to the actual results. Is price normally distributed? Try another test. If price is normally distributed, how many homes should have a price greater than the mean? Compare this to the actual number of homes. Construct a frequency distribution of price. What do you observe?

b. The mean days on the market is 30 with a standard deviation of 10 days. Use the normal distribution to estimate the number of homes on the market more than 24 days. Compare this to the actual results. Try another test. If days on the market is normally distributed, how many homes should be on the market more than the mean number of days? Compare this to the actual number of homes. Does the normal

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Software Commands Software examples using Excel, Mega- Stat®, and Minitab are included through- out the text. The explanations of the computer input commands are placed at the end of the text in Appendix C.

780

11–2. The Minitab commands for the two-sample t-test on page 368 are:

a. Put the amount absorbed by the Store brand in C1 and the amount absorbed by the Name brand paper towel in C2.

b. From the toolbar, select Stat, Basic Statistics, and then 2-Sample, and click OK.

c. In the next dialog box, select Samples in different col- umns, select C1 Store for the First column and C2 Name of the Second, click the box next to Assume equal variances, and click OK.

11–3. The Excel commands for the paired t-test on page 373 are: a. Enter the data into columns B and C (or any other two col-

umns) in the spreadsheet, with the variable names in the first row.

b. Select the Data tab on the top menu. Then, on the far right, select Data Analysis. Select t-Test: Paired Two Sample for Means, and then click OK.

c. In the dialog box, indicate that the range of Variable 1 is from B1 to B11 and Variable 2 from C1 to C11, the Hypothesized Mean Difference is 0, click Labels, Alpha is .05, and the Output Range is E1. Click OK.

CHAPTER 12 12–1. The Excel commands for the test of variances on page 391 are: a. Enter the data for U.S. 25 in column A and for I-75 in col-

umn B. Label the two columns. b. Select the Data tab on the top menu. Then, on the far right,

select Data Analysis. Select F-Test: Two-Sample for Variances, then click OK.

c. The range of the first variable is A1:A8, and B1:B9 for the second. Click on Labels, enter 0.05 for Alpha, select D1 for the Output Range, and click OK.

12–2. The Excel commands for the one-way ANOVA on page 400 are: a. Key in data into four columns labeled Northern, WTA, Po-

cono, and Branson. b. Select the Data tab on the top menu. Then, on the far right,

select Data Analysis. Select ANOVA: Single Factor, then click OK.

c. In the subsequent dialog box, make the input range A1:D8, click on Grouped by Columns, click on Labels in first row, the Alpha text box is 0.05, and finally select Output Range as F1 and click OK.

c. In the dialog box, indicate that the range of Variable 1 is from A1 to A6 and Variable 2 from B1 to B7, the Hypothe- sized Mean Difference is 0, click Labels, Alpha is 0.05, and the Output Range is D1. Click OK.

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xi

Answers to Self-Review The worked-out solutions to the Self-Reviews are pro- vided at the end of the text in Appendix E.

11

16–7 a. Rank

x y x y d d 2

805 23 5.5 1 4.5 20.25 777 62 3.0 9 −6.0 36.00 820 60 8.5 8 0.5 0.25 682 40 1.0 4 −3.0 9.00 777 70 3.0 10 −7.0 49.00 810 28 7.0 2 5.0 25.00 805 30 5.5 3 2.5 6.25 840 42 10.0 5 5.0 25.00 777 55 3.0 7 −4.0 16.00 820 51 8.5 6 2.5 6.25

0 193.00

rs = 1 − 6(193)

10(99) = −.170

b. H0: ρ = 0; H1: ρ ≠ 0. Reject H0 if t < −2.306 or t > 2.306.

t = −.170√ 10 − 2

1 − (−0.170)2 = −0.488

H0 is not rejected. We have not shown a relationship between the two tests.

CHAPTER 17 17–1 1.

Country Amount Index (Based=US) China 822.7 932.8 Japan 110.7 125.5 United States 88.2 100.0 India 86.5 98.1 Russia 71.5 81.1

China Produced 832.8% more steel than the US

2. a.

Year Average Hourly Earnings Index (1995 = Base) 1995 11.65 100.0 2000 14.02 120.3 2005 16.13 138.5 2013 19.97 171.4 2016 21.37 183.4

2016 Average wage Increased 83.4% from 1995

b.

Year Average Hourly Earnings Index (1995 – 2000 = Base) 1995 11.65 90.8 2000 14.02 109.2 2005 16.13 125.7 2013 19.97 155.6 2016 21.37 166.5

2016 Average wage Increased 86.5% from the average of 1995, 2000

17–2 1. a. P1 = ($85/$75)(100) = 113.3 P2 = ($45/$40)(100) = 112.5 P = (113.3 + 112.5)/2 = 112.9 b. P = ($130/$115)(100) = 113.0

c. P = $85(500) + $45(1,200) $75(500) + $40(1,200)

(100)

= $96,500 85,500

(100) = 112.9

d. P = $85(520) + $45(1,300) $75(520) + $40(1,300)

(100)

= $102,700

$91,000 (100) = 112.9

e. P = √(112.9) (112.9) = 112.9

17–3 a. P = $4(9,000) + $5(200) + $8(5,000) $3(10,000) + $1(600) + $10(3,000)

(100)

= $77,000 60,600

(100) = 127.1

b. The value of sales went up 27.1% from 2001 to 2017

17–4 a. For 2011

Item Weight

Cotton ($0.25/$0.20)(100)(.10) = 12.50 Autos (1,200/1,000)(100)(.30) = 36.00 Money turnover (90/80)(100)(.60) = 67.50 Total 116.00

For 2016

Item Weight

Cotton ($0.50/$0.20)(100)(.10) = 25.00 Autos (900/1,000)(100)(.30) = 27.00 Money turnover (75/80)(100)(.60) = 56.25 Total 108.25

b. Business activity increased 16% from 2004 to 2009. It increased 8.25% from 2004 to 2014.

17–5 In terms of the base period, Jon’s salary was $14,637 in 2000 and $17,944 in 2016. This indicates that take-home pay in- creased at a faster rate than the rate of prices paid for food, transportation, etc.

17–6 $0.42, round by ($1.00/238.132)(100). The purchasing power has declined by $0.58.

17–7 Year IPI PPI

2007 111.07 92.9 2008 107.12 100.2 2009 94.80 95.3 2010 100.00 100.0 2011 102.93 107.8 2012 105.80 110.1 2013 107.83 110.5 2014 110.98 111.5 2015 111.32 105.8

The Industrial Production index (IPI) increased 11.32% from 2010 to 2015. The Producer Price Index (PPI) increases 5.8%.