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Business Statistics A First Course

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7th Edition Levine Test Bank

Northern

David Levine

Baruch College, City University of New York

Kathryn Szabat

La Salle University

David Stephan

Two Bridges Instructional Technology

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SEVENTH

University B USINESS S TATISTICS :

F IRST C OURSE

EDITION

Business Statistics A First Course 7th Edition Levine Test Bank Visit TestBankDeal.com to get complete for all chapters

The author and publisher of this book have used their best efforts in preparing this book. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The author and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book. The author and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs.

Reproduced by Pearson from electronic files supplied by the author

Publishing as Pearson, 501 Boylston Street, Boston, MA 02116.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

ISBN-13: 978-0-321-99822-4

ISBN-10: 0-321-99822-7

www.pearsonhighered.com

i Table of Contents Getting Started Important Things to Learn First GS-1 Chapter 1 Defining and Collecting Data 1-1 Chapter 2 Organizing and Visualizing Data 2-1 Chapter 3 Numerical Descriptive Measures 3-1 Chapter 4 Basic Probability 4-1 Chapter 5 Discrete Probability Distributions 5-1 Chapter 6 The Normal Distribution 6-1 Chapter 7 Sampling Distributions 7-1 Chapter 8 Confidence Interval Estimation 8-1 Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests 9-1 Chapter 10 Two-Sample Tests and One-Way ANOVA 10-1 Chapter 11 Chi-Square Tests 11-1 Chapter 12 Simple Linear Regression 12-1 Chapter 13 Multiple Regression 13-1 Chapter 14 Statistical Applications in Quality Management 14-1

The Test Item File contains a variety of multiple-choice, true-false, problem and fill-in questions based on the definitions, concepts, and ideas developed in each chapter. In addition, numerical problems and Microsoft® Excel computer output problems are also given with solutions provided in multiple-choice, true-false, problem and fill-in format.

The Test Item File is intended to assist instructors in preparing examinations. The questions included herein highlight the key topics covered throughout each chapter. The keywords after each question, and the Keywords to Subsections Cross-reference and Subsections to Keywords Cross-reference are intended to help instructors easily locate questions on a specific topic or concept. Explanation is provided when the rationale of the correct answer to a difficult question is rather obscure. The format for the Test Item File will facilitate grading and should be helpful to instructors who teach very large sections.

The intended difficulty level (easy, moderate, difficult) of each question in the Test Item File is stated in order to facilitate test item selection by instructors wishing to create specific types of exams. However, some words of caution must be given. The classification of question difficulty level is very subjective and each question should be evaluated based on the emphasis the particular topic was given in class and how much emphasis is to be given to numerical results obtained by calculator rather than computerized results obtained from Microsoft® Excel. As an operational definition that is used here, items are classified as easy if they pertain directly to definitions and fundamental concepts. Test items are classified as moderate if they require some numerical calculations with more than a minimal number of steps or if they require a broader understanding of the topic. Test items that are classified as difficult are done so because of the level of rigor of the subject, the length of the narrative, the amount of effort required for solution, or for responses that require more thought and analysis.

Instructors are also advised that all answers in the Test Item File are computed using Microsoft® Excel or PHStat2 with no rounding involved in the intermediate steps. If students use rounding with formulae and a calculator, their answers might be different from those provided in the answer keys. Likewise, if students use the statistical tables at the end of the book instead of Microsoft® Excel or PHStat2, their answers might also differ from those provided in the answer keys due to rounding. Whenever possible, we provide answers obtained using both Microsoft® Excel/PHStat2 and the statistical tables if they are very different.

This Test Item File and others that are similar suffer from one major weakness. They do not permit an evaluation of the students’ written communication skill. The authors highly recommend that, if possible, instructors who use this Test Item File supplement it with at least one short essay type question so that an assessment can be made of the students’ understanding of concepts as well as how they can make connections across various topics.

The following tabular display is a breakdown of the number of questions in each chapter by type.

Preface

v Chapter Multiple Choice True/FalseFillin Problem Total GS 8 19 6 0 33 1 82 49 39 0 170 2 59 51 84 24 218 3 41 34 38 78 191 4 60 20 38 53 171 5 21 12 61 49 143 6 19 12 32 83 146 7 30 37 41 31 139 8 30 111 37 30 208 9 79 89 39 22 229 10 131 58 60 65 314 11 49 41 24 18 132 12 80 46 78 46 250 13 107 84 48 36 275 14 44 59 38 18 159 Total 840 722 663 553 2778

vi Keywords to Subsections Cross-reference Keyword Chapter Section aprioriprobability 4 1 A2factor 14 5 additionrule 4 1 adjustedcoefficientofdetermination 13 2 adjustedr-square 13 2 analysisofmeans 10 5 analysisofproportions 11 2 arithmeticmean 3 1 assumption 9 1 assumption 10 1 assumption 10 2 assumption 10 4 assumption 10 5 assumption 11 2 assumption 11 3 assumption 12 4 assumption 12 5 assumption 13 3 autocorrelation 12 4 autocorrelation 12 5 autocorrelation 12 6 barchart 2 3 Bayes'theorem 4 3 beta-risk 9 1 bigdata GS 3 binomialdistribution 5 2 boxplot 3 3 businessanalytics GS 3 cchart 14 4 capabilityindex 14 6 categoricalvariable 1 1 centerline 14 2 centerline 14 4 centerline 14 5 centrallimittheorem 7 2 centrallimittheorem 8 1 centrallimittheorem 8 2 centrallimittheorem 8 3 challengesinvisualizingdata 2 7 Chebyshevrule 3 4 chi-squaretest 11 1 chi-squaretest 11 2 chi-squaretest 11 3 Chi-squaretestfordifferenceintwoproportions 11 1 Chi-squaretestofindependence 11 3 choiceofchart 2 3 choiceofchart 2 4

vii Keyword Chapter Section choiceofchart 2 5 classboundaries 2 2 classinterval 2 2 classmidpoint 2 2 clustersample 1 3 coefficientofcorrelation 12 7 coefficientofdetermination 12 3 coefficientofmultipledetermination 13 2 coefficientofvariation 3 2 collectiveexhaustive 4 1 columnpercentages 2 1 combination 4 4 commoncausesofvariation 14 1 commoncausesofvariation 14 2 commoncausesofvariation 14 3 commoncausesofvariation 14 6 complement 4 1 completelyrandomizeddesign 10 5 conclusion 9 1 conclusion 9 2 conclusion 9 3 conclusion 9 4 conclusion 10 1 conclusion 10 2 conclusion 10 3 conclusion 10 4 conclusion 10 5 conclusion 11 1 conclusion 11 2 conclusion 11 3 conclusion 12 7 conclusion 13 2 conclusion 13 4 conclusion 13 5 conditionalprobability 4 2 confidencecoefficient 9 1 confidenceinterval 8 1 confidenceinterval 8 2 confidenceinterval 8 3 confidenceinterval 9 1 confidenceinterval 9 2 confidenceinterval 9 4 confidenceinterval 10 1 confidenceinterval 10 2 confidenceinterval 10 3 confidenceinterval 12 7 confidenceinterval 12 8 confidenceinterval 13 4 contingencytable 2 1 contingencytable 2 6

viii Keyword Chapter Section contingencytable 4 1 contingencytable 11 1 contingencytable 11 2 contingencytable 11 3 continuousvariable 1 1 controlchart 14 1 controlchart 14 2 controlchart 14 4 controlchart 14 5 controllimit 14 1 controllimit 14 2 controllimit 14 4 controllimit 14 5 conveniencesample 1 3 countingrule 4 4 covariance 3 5 coverageerror 1 4 Cpindex 14 6 Cpkindex 14 6 CPLindex 14 6 CPUindex 14 6 criticalvalue 8 1 criticalvalue 8 2 criticalvalue 8 3 criticalvalue 9 1 criticalvalue 9 2 criticalvalue 9 3 criticalvalue 9 4 criticalvalue 10 1 criticalvalue 10 2 criticalvalue 10 3 criticalvalue 10 4 criticalvalue 10 5 criticalvalue 11 1 criticalvalue 11 2 criticalvalue 11 3 criticalvalue 12 7 criticalvalue 13 2 criticalvalue 13 4 criticalvalue 13 5 cumulativedistribution 2 2 cumulativepercentagedistribution 2 2 cumulativepercentagedistribution 2 4 cumulativepercentagepolygon(ogive) 2 4 cumulativerelativefrequency 2 4 d2factor 14 5 d3factor 14 5 D3factor 14 5 D4factor 14 5 data GS 2

ix Keyword Chapter Section DCOVAframework GS 1 decision 9 1 decision 9 2 decision 9 3 decision 9 4 decision 10 1 decision 10 2 decision 10 3 decision 10 4 decision 10 5 decision 11 1 decision 11 2 decision 11 3 decision 12 7 decision 13 2 decision 13 4 decision 13 5 degreesoffreedom 10 1 degreesoffreedom 10 2 degreesoffreedom 10 4 degreesoffreedom 10 5 degreesoffreedom 11 1 degreesoffreedom 11 2 degreesoffreedom 11 3 degreesoffreedom 12 7 degreesoffreedom 13 2 degreesoffreedom 13 4 Deming's14points 14 7 descriptivestatistics GS 2 differenceamongmorethantwomeans 10 5 differencebetweentwomeans 10 1 differencebetweentwomeans 10 2 differencebetweentwoproportions 10 3 differencebetweentwovariances 10 4 discretevariable 1 1 dummyvariable 13 5 Durbin-Watsonstatistic 12 6 empiricalprobability 4 1 empiricalrule 3 4 estimation 12 7 estimation 12 8 estimation 13 1 estimationofmeanvalues 12 8 estimationofmeanvalues 13 1 ethicalissues 1 4 ethicalissues 8 5 ethicalissues 9 5 Fdistribution 10 4 Ftest 10 4 Ftestforfactor 10 5

x Keyword Chapter Section Ftestonslope 12 7 Ftestontheentireregression 13 2 five-numbersummary 3 3 formofhypothesis 9 1 formofhypothesis 9 2 formofhypothesis 9 3 formofhypothesis 9 4 formofhypothesis 10 1 formofhypothesis 10 2 formofhypothesis 10 3 formofhypothesis 10 4 formofhypothesis 10 5 formofhypothesis 11 1 formofhypothesis 11 2 formofhypothesis 11 3 formofhypothesis 12 7 formofhypothesis 13 2 formofhypothesis 13 4 formofhypothesis 13 5 frame 1 3 frequencydistribution 2 2 histogram 2 4 homoscedasticity 12 4 homoscedasticity 12 5 homoscedasticity 13 3 independence 4 2 inferentialstatistics GS 2 interaction 13 5 intercept 12 2 intercept 13 1 interpretation 2 1 interpretation 2 2 interpretation 2 3 interpretation 2 4 interpretation 3 5 interpretation 8 1 interpretation 8 2 interpretation 8 3 interpretation 10 5 interpretation 12 2 interpretation 12 3 interpretation 12 4 interpretation 12 7 interpretation 13 1 interpretation 13 2 interpretation 13 4 interpretation 13 5 interquartilerange 3 3 jointprobability 4 1 judgmentsample 1 3

xi Keyword Chapter Section leansixsigma 14 8 leastsquares 12 2 levelofsignificance 9 1 levelofsignificance 9 2 levelofsignificance 9 3 levelofsignificance 9 4 marginalprobability 4 1 marginalprobability 4 2 mean 3 1 mean 6 2 mean 7 2 mean 8 1 mean 8 2 mean 9 1 mean 9 2 mean 9 3 mean(expectedvalue) 5 1 mean(expectedvalue) 5 2 mean(expectedvalue) 5 3 meandifference 10 2 meansquares 10 5 meansquares 13 2 measureofcentraltendency 3 1 measureofvariation 3 2 measurementerror 1 4 median 3 1 mode 3 1 multidimensionalcontingencytable 2 6 multiplicationrule 4 2 mutuallyexclusive 4 1 nonprobabilitysample 1 3 nonresponseerror 1 4 normaldistribution 3 4 normaldistribution 6 2 normaldistribution 6 3 normalprobabilityplot 6 3 normality 13 3 numberofclasses 2 2 one-tailedtest 9 3 one-wayanalysisofvariance 10 5 pchart 14 2 parameter 1 2 parameter 8 1 parameter 8 2 parameter 8 3 parameter 9 1 parameter 9 2 parameter 9 3 parameter 9 4 Paretochart 2 3

xii Keyword Chapter Section percentagedistribution 2 2 percentagepolygon 2 4 permutation 4 4 piechart 2 3 pointestimate 8 1 pointestimate 10 1 pointestimate 10 3 Poissondistribution 5 3 polygon 2 4 pooledvariance 10 1 population 1 2 populationmean 3 4 populationstandarddeviation 3 4 populationvariance 3 4 power 9 1 predictioninterval 12 8 predictionofindividualvalues 12 8 predictionofindividualvalues 13 2 probability 6 2 probability 7 2 probability 7 3 probabilitydistribution 5 1 probabilitydistribution 5 2 probabilitydistribution 5 3 probabilitysample 1 3 probabilitysample 1 4 processcapability 14 6 properties 5 2 properties 5 3 properties 6 2 properties 7 1 properties 8 1 properties 8 2 properties 8 3 properties 8 4 properties 10 4 properties 10 5 properties 11 1 properties 11 2 properties 11 3 properties 12 2 properties 12 3 properties 12 4 properties 12 5 properties 12 6 properties 12 7 properties 12 8 properties 13 2 properties 13 3 properties 13 5

xiii Keyword Chapter Section proportion 7 3 proportion 8 3 proportion 9 4 p-value 9 1 p-value 9 2 p-value 9 3 p-value 9 4 p-value 10 1 p-value 10 2 p-value 10 3 p-value 10 4 p-value 10 5 p-value 11 1 p-value 11 2 p-value 11 3 p-value 12 7 p-value 13 2 p-value 13 4 quartile 3 3 Rchart 14 5 randomnumber 1 3 range 3 2 reasonsforlearningstatistics GS 1 reasonsforsampling 1 3 redbeadexperiment 14 3 rejectionregion 9 1 rejectionregion 9 2 rejectionregion 9 3 rejectionregion 9 4 rejectionregion 10 1 rejectionregion 10 2 rejectionregion 10 3 rejectionregion 10 4 rejectionregion 10 5 relativefrequencydistribution 2 2 residual 12 5 residual 13 3 residualplot 12 5 residualplot 13 3 resistanttooutliers 3 1 resistanttooutliers 3 2 rowpercentages 2 1 sample 1 2 sample 1 3 samplesize 9 1 samplesize 10 1 samplesize 10 2 samplesize 10 3 samplesize 10 4 samplesizedetermination 8 4

xiv Keyword Chapter Section samplespace 4 1 samplingdistribution 10 2 samplingerror 1 4 samplingerror 8 1 samplingerror 8 4 samplingmethod 1 3 samplingwithreplacement 1 3 samplingwithreplacement 4 2 samplingwithoutreplacement 1 3 samplingwithoutreplacement 4 2 scatterchart 2 5 scatterchart 12 1 scatterchart 12 2 scatterchart 12 6 scatterchart 12 7 scatterchart 12 9 selectionbias 1 3 selectionbias 1 4 shape 3 3 ShewhartDemingcycle 14 7 side-by-sidechart 2 3 simplerandomsample 1 3 sixsigmamanagement 14 8 slope 12 2 slope 12 7 slope 13 1 slope 13 4 sourceofdata 1 2 specialcausesofvariation 14 1 specialcausesofvariation 14 2 specialcausesofvariation 14 3 specialcausesofvariation 14 4 specialcausesofvariation 14 5 specialcausesofvariation 14 6 specialcausesofvariation 14 7 standarddeviation 3 2 standarddeviation 3 4 standarderror 7 2 standarderror 7 3 standarderror 10 1 standarderror 10 2 standarderror 10 3 standarderror 12 7 standarderror 14 2 standarderrorofestimate 12 3 standarderrorofestimate 13 standardizednormaldistribution 6 2 standardizednormaldistribution 8 1 standardizednormaldistribution 8 3 standardizednormaldistribution 8 4

xv Keyword Chapter Section statistic 1 2 statisticalcontrol 14 1 statisticalcontrol 14 2 statisticalcontrol 14 4 statisticalcontrol 14 5 statisticalindependence 4 2 statisticalpackage GS 1 statistics 7 1 stem-and-leafdisplay 2 4 stratifiedsample 1 3 subjectiveprobability 4 1 sumofsquares 10 5 sumofsquares 12 3 sumofsquares 13 2 surveyworthiness 1 4 systematicsample 1 3 tdistribution 8 2 ttest 9 2 ttest 9 3 ttest 10 1 ttest 10 2 ttestforcorrelationcoefficient 12 7 ttestonslope 12 7 ttestonslope 13 4 teststatistic 9 1 teststatistic 9 2 teststatistic 9 3 teststatistic 9 4 teststatistic 10 1 teststatistic 10 2 teststatistic 10 3 teststatistic 10 4 teststatistic 10 5 teststatistic 11 1 teststatistic 11 2 teststatistic 11 3 teststatistic 12 6 teststatistic 12 7 teststatistic 13 2 teststatistic 13 4 teststatistic 13 5 testing 8 3 time-seriesplot 2 5 totalpercentages 2 1 trade-off 8 4 Tukey-Kramerprocedure 10 5 two-tailtest 9 1 two-tailtest 9 2 two-tailtest 9 4 typeIerror 9 1

xvi Keyword Chapter Section typeIerror 9 2 typeIerror 9 3 typeIerror 9 4 typeIIerror 9 1 typesofdata 1 1 unbiased 7 2 unbiased 10 1 value 6 2 value 7 2 value 7 3 variable GS 2 variance 3 2 variance 3 4 variance 6 2 variation 3 2 width 8 4 XBarchart 14 5 Zscores 3 2 Ztest 9 1 Ztest 9 4 Ztest 10 3

GS 1

GS 2

Subsections to Keywords Cross-reference

xvii

Keyword

DCOVAframework

Chapter Section

GS 1

reasonsforlearningstatistics

statisticalpackage

data

GS 2

descriptivestatistics

inferentialstatistics

GS 2

variable

bigdata

3 businessanalytics 1 1 categoricalvariable 1 1 continuousvariable 1 1 discretevariable 1 1 typesofdata 1 2 parameter 1 2 population 1 2 sample 1 2 sourceofdata 1 2 statistic 1 3 clustersample 1 3 conveniencesample 1 3 frame 1 3 judgmentsample 1 3 nonprobabilitysample 1 3 probabilitysample 1 3 randomnumber 1 3 reasonsforsampling 1 3 sample 1 3 samplingmethod 1 3 samplingwithreplacement 1 3 samplingwithoutreplacement 1 3 selectionbias 1 3 simplerandomsample 1 3 stratifiedsample 1 3 systematicsample 1 4 coverageerror 1 4 ethicalissues 1 4 measurementerror 1 4 nonresponseerror 1 4 probabilitysample 1 4 samplingerror 1 4 selectionbias 1 4 surveyworthiness 2 1 columnpercentages 2 1 contingencytable 2 1 interpretation 2 1 rowpercentages

GS 2

GS 3

xviii

Keyword 2 1 totalpercentages 2 2 classboundaries 2 2 classinterval 2 2 classmidpoint 2 2 cumulativedistribution 2 2 cumulativepercentagedistribution 2 2 frequencydistribution 2 2 interpretation 2 2 numberofclasses 2 2 percentagedistribution 2 2 relativefrequencydistribution 2 3 barchart 2 3 choiceofchart 2 3 interpretation 2 3 Paretochart 2 3 piechart 2 3 side-by-sidechart 2 4 choiceofchart 2 4 cumulativepercentagedistribution 2 4 cumulativepercentagepolygon(ogive) 2 4 cumulativerelativefrequency 2 4 histogram 2 4 interpretation 2 4 percentagepolygon 2 4 polygon 2 4 stem-and-leafdisplay 2 5 choiceofchart 2 5 scatterchart 2 5 time-seriesplot 2 6 contingencytable 2 6 multidimensionalcontingencytable 2 7 challengesinvisualizingdata 3 1 arithmeticmean 3 1 mean 3 1 measureofcentraltendency 3 1 median 3 1 mode 3 1 resistanttooutliers 3 2 coefficientofvariation 3 2 measureofvariation 3 2 range 3 2 resistanttooutliers 3 2 standarddeviation 3 2 variance 3 2 variation 3 2 Zscores 3 3 boxplot 3 3 five-numbersummary 3 3 interquartilerange

Chapter Section

Chapter Section Keyword

xix

3 3 quartile 3 3 shape 3 4 Chebyshevrule 3 4 empiricalrule 3 4 normaldistribution 3 4 populationmean 3 4 populationstandarddeviation 3 4 populationvariance 3 4 standarddeviation 3 4 variance 3 5 covariance 3 5 interpretation 4 1 aprioriprobability 4 1 additionrule 4 1 collectiveexhaustive 4 1 complement 4 1 contingencytable 4 1 empiricalprobability 4 1 jointprobability 4 1 marginalprobability 4 1 mutuallyexclusive 4 1 samplespace 4 1 subjectiveprobability 4 2 conditionalprobability 4 2 independence 4 2 marginalprobability 4 2 multiplicationrule 4 2 samplingwithreplacement 4 2 samplingwithoutreplacement 4 2 statisticalindependence 4 3 Bayes'theorem 4 4 combination 4 4 countingrule 4 4 permutation 5 1 mean(expectedvalue) 5 1 probabilitydistribution 5 2 binomialdistribution 5 2 mean(expectedvalue) 5 2 probabilitydistribution 5 2 properties 5 3 mean(expectedvalue) 5

Poissondistribution 5

probabilitydistribution 5

properties 6

mean 6 2 normaldistribution 6 2 probability 6 2 properties 6 2 standardizednormaldistribution

Keyword 6 2 value 6 2 variance 6 3 normaldistribution 6 3 normalprobabilityplot 7 1 properties 7 1 statistics 7 2 centrallimittheorem 7 2 mean 7 2 probability 7 2 standarderror 7 2 unbiased 7 2 value 7 3 probability 7 3 proportion 7 3 standarderror 7 3 value 8 1 centrallimittheorem 8 1 confidenceinterval 8 1 criticalvalue 8 1 interpretation 8 1 mean 8 1 parameter 8 1 pointestimate 8 1 properties 8 1 samplingerror 8 1 standardizednormaldistribution 8 2 centrallimittheorem 8 2 confidenceinterval 8 2 criticalvalue 8 2 interpretation 8 2 mean 8 2 parameter 8 2 properties 8 2 tdistribution 8 3 centrallimittheorem 8 3 confidenceinterval 8 3 criticalvalue 8 3 interpretation 8 3 parameter 8 3 properties 8 3 proportion 8 3 standardizednormaldistribution 8 3 testing 8 4 properties 8 4 samplesizedetermination 8 4 samplingerror 8 4 standardizednormaldistribution 8 4 trade-off 8 4 width

Chapter Section

xxi Chapter Section Keyword 8 5 ethicalissues 9 1 assumption 9 1 beta-risk 9 1 conclusion 9 1 confidencecoefficient 9 1 confidenceinterval 9 1 criticalvalue 9 1 decision 9 1 formofhypothesis 9 1 levelofsignificance 9 1 mean 9 1 parameter 9 1 power 9 1 p-value 9 1 rejectionregion 9 1 samplesize 9 1 teststatistic 9 1 two-tailtest 9 1 typeIerror 9 1 typeIIerror 9 1 Ztest 9 2 conclusion 9 2 confidenceinterval 9 2 criticalvalue 9 2 decision 9 2 formofhypothesis 9 2 levelofsignificance 9 2 mean 9 2 parameter 9 2 p-value 9 2 rejectionregion 9 2 ttest 9 2 teststatistic 9 2 two-tailtest 9 2 typeIerror 9 3 conclusion 9 3 criticalvalue 9 3 decision 9 3 formofhypothesis 9 3 levelofsignificance 9 3 mean 9 3 one-tailedtest 9 3 parameter 9 3 p-value 9 3 rejectionregion 9 3 ttest 9 3 teststatistic 9 3 typeIerror 9 4 conclusion

xxii Chapter Section Keyword 9 4 confidenceinterval 9 4 criticalvalue 9 4 decision 9 4 formofhypothesis 9 4 levelofsignificance 9 4 parameter 9 4 proportion 9 4 p-value 9 4 rejectionregion 9 4 teststatistic 9 4 two-tailtest 9 4 typeIerror 9 4 Ztest 9 5 ethicalissues 10 1 assumption 10 1 conclusion 10 1 confidenceinterval 10 1 criticalvalue 10 1 decision 10 1 degreesoffreedom 10 1 differencebetweentwomeans 10 1 formofhypothesis 10 1 pointestimate 10 1 pooledvariance 10 1 p-value 10 1 rejectionregion 10 1 samplesize 10 1 standarderror 10 1 ttest 10 1 teststatistic 10 1 unbiased 10 2 assumption 10 2 conclusion 10 2 confidenceinterval 10 2 criticalvalue 10 2 decision 10 2 degreesoffreedom 10 2 differencebetweentwomeans 10 2 formofhypothesis 10 2 meandifference 10 2 p-value 10 2 rejectionregion 10 2 samplesize 10 2 samplingdistribution 10 2 standarderror 10 2 ttest 10 2 teststatistic 10 3 conclusion 10 3 confidenceinterval

xxiii Chapter Section Keyword 10 3 criticalvalue 10 3 decision 10 3 differencebetweentwoproportions 10 3 formofhypothesis 10 3 pointestimate 10 3 p-value 10 3 rejectionregion 10 3 samplesize 10 3 standarderror 10 3 teststatistic 10 3 Ztest 10 4 assumption 10 4 conclusion 10 4 criticalvalue 10 4 decision 10 4 degreesoffreedom 10 4 differencebetweentwovariances 10 4 Fdistribution 10 4 Ftest 10 4 formofhypothesis 10 4 properties 10 4 p-value 10 4 rejectionregion 10 4 samplesize 10 4 teststatistic 10 5 analysisofmeans 10 5 assumption 10 5 completelyrandomizeddesign 10 5 conclusion 10 5 criticalvalue 10 5 decision 10 5 degreesoffreedom 10 5 differenceamongmorethantwomeans 10 5 Ftestforfactor 10 5 formofhypothesis 10 5 interpretation 10 5 meansquares 10 5 one-wayanalysisofvariance 10 5 properties 10 5 p-value 10 5 rejectionregion 10 5 sumofsquares 10 5 teststatistic 10 5 Tukey-Kramerprocedure 11 1 chi-squaretest 11 1 Chi-squaretestfordifferenceintwoproportions 11 1 conclusion 11 1 contingencytable 11 1 criticalvalue

xxiv Chapter Section Keyword 11 1 decision 11 1 degreesoffreedom 11 1 formofhypothesis 11 1 properties 11 1 p-value 11 1 teststatistic 11 2 analysisofproportions 11 2 assumption 11 2 chi-squaretest 11 2 conclusion 11 2 contingencytable 11 2 criticalvalue 11 2 decision 11 2 degreesoffreedom 11 2 formofhypothesis 11 2 properties 11 2 p-value 11 2 teststatistic 11 3 assumption 11 3 chi-squaretest 11 3 Chi-squaretestofindependence 11 3 conclusion 11 3 contingencytable 11 3 criticalvalue 11 3 decision 11 3 degreesoffreedom 11 3 formofhypothesis 11 3 properties 11 3 p-value 11 3 teststatistic 12 1 scatterchart 12 2 intercept 12 2 interpretation 12 2 leastsquares 12 2 properties 12 2 scatterchart 12 2 slope 12 3 coefficientofdetermination 12 3 interpretation 12 3 properties 12 3 standarderrorofestimate 12 3 sumofsquares 12 4 assumption 12 4 autocorrelation 12 4 homoscedasticity 12 4 interpretation 12 4 properties 12 5 assumption 12 5 autocorrelation

xxv Chapter Section Keyword 12 5 homoscedasticity 12 5 properties 12 5 residual 12 5 residualplot 12 6 autocorrelation 12 6 Durbin-Watsonstatistic 12 6 properties 12 6 scatterchart 12 6 teststatistic 12 7 coefficientofcorrelation 12 7 conclusion 12 7 confidenceinterval 12 7 criticalvalue 12 7 decision 12 7 degreesoffreedom 12 7 estimation 12 7 Ftestonslope 12 7 formofhypothesis 12 7 interpretation 12 7 properties 12 7 p-value 12 7 scatterchart 12 7 slope 12 7 standarderror 12 7 ttestforcorrelationcoefficient 12 7 ttestonslope 12 7 teststatistic 12 8 confidenceinterval 12 8 estimation 12 8 estimationofmeanvalues 12 8 predictioninterval 12 8 predictionofindividualvalues 12 8 properties 12 9 scatterchart 13 1 estimation 13 1 estimationofmeanvalues 13 1 intercept 13 1 interpretation 13 1 slope 13 2 adjustedcoefficientofdetermination 13 2 adjustedr-square 13 2 coefficientofmultipledetermination 13 2 conclusion 13 2 criticalvalue 13 2 decision 13 2 degreesoffreedom 13 2 Ftestontheentireregression 13 2 formofhypothesis 13 2 interpretation

xxvi Chapter Section Keyword 13 2 meansquares 13 2 predictionofindividualvalues 13 2 properties 13 2 p-value 13 2 sumofsquares 13 2 teststatistic 13 3 assumption 13 3 homoscedasticity 13 3 normality 13 3 properties 13 3 residual 13 3 residualplot 13 4 conclusion 13 4 confidenceinterval 13 4 criticalvalue 13 4 decision 13 4 degreesoffreedom 13 4 formofhypothesis 13 4 interpretation 13 4 p-value 13 4 slope 13 4 ttestonslope 13 4 teststatistic 13 5 conclusion 13 5 criticalvalue 13 5 decision 13 5 dummyvariable 13 5 formofhypothesis 13 5 interaction 13 5 interpretation 13 5 properties 13 5 teststatistic 13 standarderrorofestimate 14 1 commoncausesofvariation 14 1 controlchart 14 1 controllimit 14 1 specialcausesofvariation 14 1 statisticalcontrol 14 2 centerline 14 2 commoncausesofvariation 14 2 controlchart 14 2 controllimit 14 2 pchart 14 2 specialcausesofvariation 14 2 standarderror 14 2 statisticalcontrol 14 3 commoncausesofvariation 14 3 redbeadexperiment 14 3 specialcausesofvariation

xxvii

Section Keyword 14 4 cchart 14 4 centerline 14 4 controlchart 14 4 controllimit 14 4 specialcausesofvariation 14 4 statisticalcontrol 14 5 A2factor 14 5 centerline 14 5 controlchart 14 5 controllimit 14 5 d2factor 14 5 d3factor 14 5 D3factor 14 5 D4factor 14 5 Rchart 14 5 specialcausesofvariation 14 5 statisticalcontrol 14 5 XBarchart 14 6 capabilityindex 14 6 commoncausesofvariation 14 6 Cpindex 14 6 Cpkindex 14 6 CPLindex 14 6 CPUindex 14 6 processcapability 14 6 specialcausesofvariation 14 7 Deming's14points 14 7 ShewhartDemingcycle 14 7 specialcausesofvariation 14 8 leansixsigma 14 8 sixsigmamanagement

Chapter

GETTING STARTED: IMPORTANT THINGS TO LEARN FIRST

1. The process of using data collected from a small group to reach conclusions about a large group is called

a) statistical inference.

b) DCOVA framework.

c) operational definition.

d) descriptive statistics.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: inferential statistics

2. Those methods involving the collection, presentation, and characterization of a set of data in order to properly describe the various features of that set of data are called

a) statistical inference.

b) DCOVA framework.

c) operational definition.

d) descriptive statistics.

ANSWER: d

TYPE: MC DIFFICULTY: Easy

KEYWORDS: descriptive statistics

3. The collection and summarization of the socioeconomic and physical characteristics of the employees of a particular firm is an example of

a) inferential statistics.

b) descriptive statistics.

c) operational definition.

d) DCOVA framework.

ANSWER: b

TYPE: MC DIFFICULTY: Easy

KEYWORDS: descriptive statistics

4. The estimation of the population average family expenditure on food based on the sample average expenditure of 1,000 families is an example of

a) inferential statistics.

b) descriptive statistics.

c) DCOVA framework.

d) operational definition.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: inferential statistics

Important Things to Learn First GS-1

5. Which of the following is not an element of descriptive statistical problems?

a) An inference made about the population based on the sample.

b) The population or sample of interest.

c) Tables, graphs, or numerical summary tools.

d) Identification of patterns in the data.

ANSWER: a

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: descriptive statistics

6. A study is under way in Yosemite National Forest to determine the adult height of American pine trees. Specifically, the study is attempting to determine what factors aid a tree in reaching heights greater than 60 feet tall. It is estimated that the forest contains 25,000 adult American pines. The study involves collecting heights from 250 randomly selected adult American pine trees and analyzing the results. Identify the variable of interest in the study.

a) The age of an American pine tree in Yosemite National Forest.

b) The height of an American pine tree in Yosemite National Forest.

c) The number of American pine trees in Yosemite National Forest.

d) The species of trees in Yosemite National Forest.

ANSWER: b

TYPE: MC DIFFICULTY: Easy

KEYWORDS: variable

7. Most analysts focus on the cost of tuition as the way to measure the cost of a college education. But incidentals, such as textbook costs, are rarely considered. A researcher at Drummand University wishes to estimate the textbook costs of first-year students at Drummand. To do so, she monitored the textbook cost of 250 first-year students and found that their average textbook cost was $600 per semester. Identify the variable of interest to the researcher.

a) The textbook cost of first-year Drummand University students.

b) The year in school of Drummand University students.

c) The age of Drummand University students.

d) The cost of incidental expenses of Drummand University students.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: variable

8. True or False: Problems may arise when statistically unsophisticated users who do not understand the assumptions behind the statistical procedures or their limitations are misled by results obtained from computer software.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: statistical package

GS-2 Important Things to Learn First

9. True or False: Managers need an understanding of statistics to be able to present and describe information accurately, draw conclusions about large populations based on small samples, improve processes, and make reliable forecasts.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: reasons for learning statistics

10. True or False: A professor computed the sample average exam score of 20 students and used it to estimate the average exam score of the 1,500 students taking the exam. This is an example of inferential statistics.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: descriptive statistics, inferential statistics

11. True or False: Using the number of registered voters who turned out to vote for the primary in Iowa to predict the number of registered voters who will turn out to vote in Vermont’s primary is an example of descriptive statistics.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: descriptive statistics, inferential statistics

12. True or False: Compiling the number of registered voters who turned out to vote for the primary in Iowa is an example of descriptive statistics.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: descriptive statistics, inferential statistics

13. The Human Resources Director of a large corporation wishes to develop an employee benefits package and decides to select 500 employees from a list of all (N = 40,000) workers in order to study their preferences for the various components of a potential package. In this study, methods involving the collection, presentation, and characterization of the data are called _______.

ANSWER:

descriptive statistics/methods

TYPE: FI DIFFICULTY: Easy

KEYWORDS: descriptive statistics

Important Things to Learn First GS-3

14. The Human Resources Director of a large corporation wishes to develop an employee benefits package and decides to select 500 employees from a list of all (N = 40,000) workers in order to study their preferences for the various components of a potential package. In this study, methods that result in decisions concerning population characteristics based only on the sample results are called _______.

ANSWER:

inferential statistics/methods

TYPE: FI DIFFICULTY: Easy

KEYWORDS: inferential statistics

15. The oranges grown in corporate farms in an agricultural state were damaged by some unknown fungi a few years ago. Suppose the manager of a large farm wanted to study the impact of the fungi on the orange crops on a daily basis over a 6-week period. On each day a random sample of orange trees was selected from within a random sample of acres. The daily average number of damaged oranges per tree and the proportion of trees having damaged oranges were calculated. In this study, drawing conclusions on any one day about the true population characteristics based on information obtained from the sample is called _______.

ANSWER:

inferential statistics/methods

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: inferential statistics

16. The oranges grown in corporate farms in an agricultural state were damaged by some unknown fungi a few years ago. Suppose the manager of a large farm wanted to study the impact of the fungi on the orange crops on a daily basis over a 6-week period. On each day a random sample of orange trees was selected from within a random sample of acres. The daily average number of damaged oranges per tree and the proportion of trees having damaged oranges were calculated. In this study, the presentation and characterization of the two main measures calculated each day (i.e., average number of damaged oranges per tree and proportion of trees having damaged oranges) is called _______ .

ANSWER:

descriptive statistics/methods

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: descriptive statistics

17. The Commissioner of Health in New York State wanted to study malpractice litigation in New York. A sample of 31 thousand medical records was drawn from a population of 2.7 million patients who were discharged during 2010. Using the information obtained from the sample to predict population characteristics with respect to malpractice litigation is an example of _______.

ANSWER:

inferential statistics

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: inferential statistics

GS-4 Important Things to Learn First

18. The Commissioner of Health in New York State wanted to study malpractice litigation in New York. A sample of 31 thousand medical records was drawn from a population of 2.7 million patients who were discharged during 2010. The collection, presentation, and characterization of the data from patient medical records are examples of _______.

ANSWER:

descriptive statistics/methods

TYPE: FI DIFFICULTY: Easy

KEYWORDS: descriptive statistics

19. True or False: Business analytics combine “traditional” statistical methods with methods and techniques from management science and information systems to form an interdisciplinary tool that supports fact-based management decision making.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: business analytics

20. Which of the following is not true about business analytics?

a) It enables you to use statistical methods to analyze and explore data to uncover unforeseen relationships.

b) It enables you to use management science methods to develop optimization models that impact an organization’s strategy, planning, and operations.

c) It enables you to use complex mathematics to replace the need for organizational decision making and problem solving.

d) It enables you to use information systems methods to collect and process data sets of all sizes.

ANSWER: c

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: business analytics

21. True or False: “Big data” is a concrete concept with a precise operational definition.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: big data

22. True or False: “Big data” are data being collected in huge volumes and at very fast rates, and they typically arrive in a variety of forms, organized and unorganized.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: big data

Important Things to Learn First GS-5

23. True or False: In the current data-driven environment of business, the decisions you make will be increasingly based on gut or intuition supported by personal experience.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: business analytics

24. True or False: The D in the DCOVA framework stands for “data”.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

25. True or False: The D in the DCOVA framework stands for “define”.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

26. True or False: The C in the DCOVA framework stands for “categorize”.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

27. True or False: The C in the DCOVA framework stands for “collect”.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

28. True or False: The O in the DCOVA framework stands for “operationalize”.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

29. True or False: The O in the DCOVA framework stands for “organize”.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

GS-6 Important Things to Learn First

30. True or False: The V in the DCOVA framework stands for “verify”.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

31. True or False: The V in the DCOVA framework stands for “visualize”.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

32. True or False: The A in the DCOVA framework stands for “apply”.

ANSWER: False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

33. True or False: The V in the DCOVA framework stands for “analyze”.

ANSWER: True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: DCOVA framework

Important Things to Learn First GS-7

CHAPTER 1: DEFINING AND COLLECTING DATA

1. Which of the following is a discrete quantitative (numerical) variable?

a) The Dow Jones Industrial average

b) The volume of water released from a dam

c) The distance you drove yesterday.

d) The number of employees of an insurance company

ANSWER: d

TYPE: MC DIFFICULTY: Easy

KEYWORDS: discrete variable, types of data

2. Which of the following is a continuous quantitative (numerical) variable?

a) The color of a student’s eyes

b) The number of employees of an insurance company

c) The amount of milk in a 2-liter carton.

d) The number of gallons of milk sold at the local grocery store yesterday

ANSWER: c

TYPE: MC DIFFICULTY: Easy

KEYWORDS: continuous variable, types of data

3. To monitor campus security, the campus police office is taking a survey of the number of students in a parking lot each 30 minutes of a 24-hour period with the goal of determining when patrols of the lot would serve the most students. If X is the number of students in the lot each period of time, then X is an example of

a) a categorical variable.

b) a discrete variable.

c) a continuous variable.

d) a statistic.

ANSWER: b

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: discrete variable, types of data

Defining and Collecting Data 1-1

4. Researchers are concerned that the weight of the average American school child is increasing implying, among other things, that children’s clothing should be manufactured and marketed in larger sizes. If X is the weight of school children sampled in a nationwide study, then X is an example of

a) a categorical variable.

b) a discrete variable.

c) a continuous variable.

d) a table of random numbers.

ANSWER:

c TYPE: MC DIFFICULTY: Moderate

KEYWORDS: continuous variable, types of data

5. The classification of student class designation (freshman, sophomore, junior, senior) is an example of

a) a categorical variable.

b) a discrete variable.

c) a continuous variable.

d) a table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

6. The classification of student major (accounting, economics, management, marketing, other) is an example of

a) a categorical variable.

b) a discrete variable.

c) a continuous variable.

d) a table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

1-2 Defining

and Collecting Data

7. The chancellor of a major university was concerned about alcohol abuse on her campus and wanted to find out the proportion of students at her university who visited campus bars on the weekend before the final exam week. Her assistant took a random sample of 250 students. The answer on “whether you visited campus bars on the weekend before the final exam week” from students in the sample is an example of __________.

a) a categorical variable.

b) a discrete variable.

c) a continuous variable.

d) a table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

SCENARIO 1-1

The manager of the customer service division of a major consumer electronics company is interested in determining whether the customers who have purchased a Blu-ray player made by the company over the past 12 months are satisfied with their products.

8. Referring to Scenario 1-1, the possible responses to the question "How many Blu-ray players made by other manufacturers have you used?" are values from a

a) discrete variable.

b) continuous variable.

c) categorical variable.

d) table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: discrete variable, types of data

9. Referring to Scenario 1-1, the possible responses to the question "Are you happy, indifferent, or unhappy with the performance per dollar spent on the Blu-ray player?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: c

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

Defining and Collecting Data 1-3

10. Referring to Scenario 1-1, the possible responses to the question "What is your annual income rounded to the nearest thousands?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Difficult

KEYWORDS: discrete variable, types of data

EXPLANATION: Even though money is usually considered as a continuous variable, it should be considered as a discrete variable when rounded to the nearest thousands.

11. Referring to Scenario 1-1, the possible responses to the question "How much time do you use the Blu-ray player every week on the average?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: b

TYPE: MC DIFFICULTY: Easy

KEYWORDS: continuous variable, types of data

12. Referring to Scenario 1-1, the possible responses to the question "How many people are there in your household?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Easy

KEYWORDS: discrete variable, types of data

13. Referring to Scenario 1-1, the possible responses to the question "How would you rate the quality of your purchase experience with 1 = excellent, 2 = good, 3 = decent, 4 = poor, 5 = terrible?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: c

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

1-4 Defining and Collecting Data

14. Referring to Scenario 1-1, the possible responses to the question "What brand of Blu-ray player did you purchase?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: c

TYPE: MC DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

15. Referring to Scenario 1-1, the possible responses to the question "Out of a 100 point score with 100 being the highest and 0 being the lowest, what is your satisfaction level on the videocassette recorder that you purchased?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: discrete variable, types of data

16. Referring to Scenario 1-1, the possible responses to the question "In which year were you born?" are values from a

a) discrete numerical variable.

b) continuous numerical variable.

c) categorical variable.

d) table of random numbers.

ANSWER: a

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: discrete variable, types of data

17. True or False: The possible responses to the question “How long have you been living at your current residence?” are values from a continuous variable.

ANSWER: True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: continuous variable, types of data

Defining and Collecting Data 1-5

18. True or False: The possible responses to the question “How many times in the past three months have you visited a city park?” are values from a discrete variable.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: discrete variable, types of data

19. True or False: A continuous variable may take on any value within its relevant range even though the measurement device may not be precise enough to record it.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: continuous variable, types of data

20. True or False: Faculty rank (professor to lecturer) is an example of discrete numerical data.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

21. True or False: Student grades (A to F) are an example of continuous numerical data.

ANSWER:

False

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: categorical variables, types of data

22. True or False: The amount of coffee consumed by an individual in a day is an example of a discrete numerical variable.

ANSWER: False

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: continuous variables, types of data

23. An insurance company evaluates many numerical variables about a person before deciding on an appropriate rate for automobile insurance. The number of claims a person has made in the last 3 years is an example of a _______ numerical variable.

ANSWER: discrete

TYPE: FI DIFFICULTY: Easy

KEYWORDS: discrete variable, types of data

1-6 Defining and Collecting Data

24. An insurance company evaluates many numerical variables about a person before deciding on an appropriate rate for automobile insurance. The distance a person drives in a year is an example of a _______ variable.

ANSWER: continuous

TYPE: FI DIFFICULTY: Easy

KEYWORDS: continuous variable, types of data

25. An insurance company evaluates many numerical variables about a person before deciding on an appropriate rate for automobile insurance. A person's age is an example of a _______ numerical variable.

ANSWER: continuous

TYPE: FI DIFFICULTY: Easy

KEYWORDS: continuous variable, types of data

26. An insurance company evaluates many numerical variables about a person before deciding on an appropriate rate for automobile insurance. How long a person has been a licensed driver is an example of a _______ numerical variable.

ANSWER: continuous

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: continuous variable, types of data

27. An insurance company evaluates many numerical variables about a person before deciding on an appropriate rate for automobile insurance. The number of tickets a person has received in the last 3 years is an example of a _______ numerical variable.

ANSWER: discrete

TYPE: FI DIFFICULTY: Easy

KEYWORDS: discrete variable, types of data

28. In purchasing an automobile, there are a number of variables to consider. The body style of the car (sedan, coupe, wagon, etc.) is an example of a _______ variable.

ANSWER: categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

29. In purchasing an automobile, there are a number of variables to consider. The classification of the car as a subcompact, compact, standard, or luxury size is an example of a _______ variable.

ANSWER: categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

Defining and Collecting Data 1-7

30. In purchasing an automobile, there are a number of variables to consider. The color of the car is an example of a _______ variable.

ANSWER:

categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

31. Most colleges admit students based on their achievements in a number of different areas. Whether a student has taken any advanced placement courses is an example of a _______ variable.

ANSWER:

categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

32. Most colleges admit students based on their achievements in a number of different areas. The grade obtained in senior level English. (A, B, C, D, or F) is an example of a _______ variable.

ANSWER:

categorical

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: categorical variable, types of data

33. Most colleges admit students based on their achievements in a number of different areas. The total SAT score achieved by a student is an example of a _______ numerical variable.

ANSWER:

discrete

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: discrete variable, types of data

34. The Dean of Students conducted a survey on campus. The gender of the student is an example of a _______ variable.

ANSWER:

categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

35. The Dean of Students conducted a survey on campus. Class designation (Freshman, Sophomore, Junior, Senior) is an example of a _______ variable.

ANSWER:

categorical

TYPE: FI DIFFICULTY: Easy

KEYWORDS: categorical variable, types of data

1-8 Defining and Collecting Data