Table Of Content

Practical Business Statistics Sixth Edition Andrew F. Siegel Department of Information Systems and Operations Management Department of Finance and Business Economics Department of Statistics Michael G. Foster School of Business University of Washington AMSTERDAM(cid:129)BOSTON(cid:129)HEIDELBERG(cid:129)LONDON NEWYORK(cid:129)OXFORD(cid:129)PARIS(cid:129)SANDIEGO SANFRANCISCO(cid:129)SINGAPORE(cid:129)SYDNEY(cid:129)TOKYO AcademicPressisanimprintofElsevier AcademicPressisanimprintofElsevier 30CorporateDrive,Suite400,Burlington,MA01803,USA TheBoulevard,LangfordLane,Kidlington,Oxford,OX51GB,UK ©2012AndrewF.Siegel.PublishedbyElsevierInc.Allrightsreserved. Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronicormechanical,includingphotocopying,recording,or anyinformationstorageandretrievalsystem,withoutpermissioninwritingfromthepublisher.Detailsonhowtoseekpermission,furtherinformation aboutthePublisher’spermissionspoliciesandourarrangementswithorganizationssuchastheCopyrightClearanceCenterandtheCopyrightLicensing Agency,canbefoundatourwebsite:www.elsevier.com/permissions. ThisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythePublisher(otherthanasmaybenotedherein). Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperiencebroadenourunderstanding,changesinresearch methods,professionalpractices,ormedicaltreatmentmaybecomenecessary. Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingandusinganyinformation,methods,compounds,or experimentsdescribedherein.Inusingsuchinformationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,includingparties forwhomtheyhaveaprofessionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditorsassumeanyliabilityforanyinjuryand/ordamagetopersons orpropertyasamatterofproductsliability,negligenceorotherwise,orfromanyuseoroperationofanymethods,products,instructions,orideas containedinthematerialherein. LibraryofCongressCataloging-in-PublicationData Siegel,AndrewF. Practicalbusinessstatistics/AndrewF.Siegel.–6thed. p.cm. Includesbibliographicalreferencesandindex. ISBN978-0-12-385208-3(alk.paper) 1.Industrialmanagement–Statisticalmethods.I.Title. HD30.215.S572012 519.5024’65–dc22 2010041182 BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary. ForinformationonallAcademicPresspublications visitourWebsiteatwww.elsevierdirect.com Typesetby:diacriTech,Chennai,India PrintedintheUnitedStatesofAmerica 11121314 987654321 To Ann, Bonnie, Clara, Michael, and Mildred This page intentionally left blank Contents in Brief Part I Part IV Introduction and Regression and Time Series Descriptive Statistics 11.Correlation and Regression: 1. Introduction: Defining the Role of Measuring and Predicting Statistics in Business 3 Relationships 291 2. Data Structures: Classifying the 12.Multiple Regression: Predicting One Various Types of Data Sets 19 Variable from Several Others 347 3. Histograms: Looking at the 13.Report Writing: Communicating the Distribution of Data 35 Results of a Multiple Regression 417 4. Landmark Summaries: Interpreting 14.Time Series: Understanding Typical Values and Percentiles 65 Changes over Time 429 5. Variability: Dealing with Diversity 95 Part V Part II Methods and Applications Probability 15.ANOVA: Testing for Differences among Many Samples, and Much More 467 6. Probability: Understanding Random Situations 125 16.Nonparametrics:TestingwithOrdinal DataorNonnormalDistributions 491 7. Random Variables: Working with Uncertain Numbers 155 17.Chi-Squared Analysis: Testing for Patterns in Qualitative Data 507 Part III 18.Quality Control: Recognizing and Statistical Inference Managing Variation 523 8. Random Sampling: Planning Ahead Appendix A Employee Database 545 for Data Gathering 189 Appendix B Donations Database 547 9. Confidence Intervals: Admitting Appendix C Self-Test: Solutions to Selected That Estimates Are Not Exact 219 Problems and Database Exercises 551 Appendix D Statistical Tables 565 10.Hypothesis Testing: Deciding Glossary 597 between Reality and Coincidence 249 Index 605 v This page intentionally left blank Contents Preface xv Bivariate Data 20 About the Author xix Multivariate Data 20 2.2 Quantitative Data: Numbers 21 Discrete Quantitative Data 21 Part I Continuous Quantitative Data 21 Introduction and Watch Out for Meaningless Numbers 22 2.3 Qualitative Data: Categories 22 Descriptive Statistics Ordinal Qualitative Data 22 Nominal Qualitative Data 23 1. Introduction: Defining the Role of 2.4 Time-Series and Cross-Sectional Data 23 Statistics in Business 2.5 Sources of Data, Including the Internet 24 2.6 End-of-Chapter Materials 29 1.1 Why Statistics? 3 Summary 29 Why Should You Learn Statistics? 3 Key Words 30 Is Statistics Difficult? 4 Questions 30 Does Learning Statistics Decrease Your Problems 30 Decision-Making Flexibility? 4 Database Exercises 34 1.2 What Is Statistics? 4 Projects 34 Statistics Looks at the Big Picture 4 Statistics Doesn’t Ignore the Individual 4 Looking at Data 4 3. Histograms: Looking at the Statistics in Management 5 Distribution of Data 1.3 The Five Basic Activities of Statistics 5 Designing a Plan for Data Collection 5 3.1 A List of Data 35 Exploring the Data 5 The Number Line 36 Modeling the Data 6 3.2 Using a Histogram to Display the Estimating an Unknown Quantity 6 Frequencies 37 Hypothesis Testing 7 Histograms and Bar Charts 39 1.4 Data Mining 8 3.3 Normal Distributions 40 1.5 What Is Probability? 14 3.4 Skewed Distributions and 1.6 General Advice 14 Data Transformation 43 1.7 End-of-Chapter Materials 14 The Trouble with Skewness 45 Summary 14 Transformation to the Rescue 45 Key Words 15 Interpreting and Computing the Logarithm 46 Questions 15 3.5 Bimodal Distributions 47 Problems 16 Is It Really Bimodal? 47 Project 17 3.6 Outliers 49 Dealing with Outliers 49 3.7 Data Mining with Histograms 53 2. Data Structures: Classifying the 3.8 Histograms by Hand: Stem-and-Leaf 54 Various Types of Data Sets 3.9 End-of-Chapter Materials 55 2.1 How Many Variables? 19 Summary 55 Univariate Data 19 Key Words 56 vii viii Contents Questions 56 Problems 112 Problems 57 Database Exercises 120 Database Exercises 64 Projects 120 Project 64 Case 121 Case 64 Part II 4. Landmark Summaries: Interpreting Typical Values and Percentiles Probability 4.1 What Is the Most Typical Value? 65 6. Probability: Understanding Random The Average: A Typical Value for Situations Quantitative Data 66 The Weighted Average: Adjusting for 6.1 AnExample:IsItbehindDoorNumber1, Importance 68 DoorNumber2,orDoorNumber3? 126 The Median: A Typical Value for 6.2 How Can You Analyze Uncertainty? 127 Quantitative and Ordinal Data 70 The Random Experiment: A Precise The Mode: A Typical Value Even for Definition of a Random Situation 127 Nominal Data 73 The Sample Space: A List of What Which Summary Should You Use? 75 Might Happen 127 4.2 What Percentile Is It? 76 The Outcome: What Actually Happens 128 Extremes, Quartiles, and Box Plots 76 Events: Either They Happen or They Don’t 128 The Cumulative Distribution Function 6.3 How Likely Is an Event? 129 Displays the Percentiles 80 Every Event Has a Probability 129 4.3 End-of-Chapter Materials 83 Where Do Probabilities Come From? 130 Summary 83 Relative Frequency and the Law Key Words 84 of Large Numbers 130 Questions 84 Theoretical Probability 131 Problems 85 The Equally Likely Rule 131 Database Exercises 91 Subjective Probability 132 Projects 91 Bayesian and Non-Bayesian Analysis 132 Case 92 6.4 How Can You Combine Information about More Than One Event? 133 Venn Diagrams Help You See All 5. Variability: Dealing with Diversity the Possibilities 133 5.1 The Standard Deviation: The Traditional Not an Event 133 Choice 96 The Complement (Not) Rule 134 Definition and Formula for the Standard One Event and Another 134 Deviation and the Variance 97 What If Both Events Can’t Happen at Using a Calculator or a Computer 98 Once? 134 Interpreting the Standard Deviation 98 The Intersection (and) Rule for Mutually Interpreting the Standard Deviation for Exclusive Events 135 a Normal Distribution 99 One Event or Another 135 The Sample and the Population Standard The Union (or) Rule for Mutually Deviations 106 Exclusive Events 135 5.2 The Range: Quick and Superficial 107 Finding or from and and Vice Versa 135 5.3 The Coefficient of Variation: One Event Given Another: Reflecting A Relative Variability Measure 108 Current Information 136 5.4 Effects of Adding to or Rescaling the Data 109 The Rule for Finding a Conditional 5.5 End-of-Chapter Materials 110 Probability Given Certain Information 137 Summary 110 Conditional Probabilities for Mutually Key Words 112 Exclusive Events 138 Questions 112 Independent Events 138 Contents ix Part III The Intersection (and) Rule for Independent Events 139 Statistical Inference The Relationship between Independent and Mutually Exclusive Events 139 8. Random Sampling: Planning Ahead 6.5 What’s the Best Way to Solve Probability for Data Gathering Problems? 139 Probability Trees 139 8.1 Populations and Samples 190 Rules for Probability Trees 141 What Is a Representative Sample? 190 Joint Probability Tables 145 ASampleStatisticandaPopulationParameter 191 6.6 End-of-Chapter Materials 145 8.2 The Random Sample 192 Summary 145 Selecting a Random Sample 192 Key Words 147 Sampling by Shuffling the Population 195 Questions 147 8.3 TheSamplingDistributionandtheCentral Problems 148 LimitTheorem 196 Database Exercises 153 8.4 A Standard Error Is an Estimated Standard Projects 153 Deviation 200 Case 153 How Close Is the Sample Average to the Population Mean? 201 Correcting for Small Populations 203 7. Random Variables: Working with The Standard Error of the Binomial Uncertain Numbers Proportion 204 8.5 Other Sampling Methods 205 7.1 Discrete Random Variables 156 The Stratified Random Sample 205 Finding the Mean and Standard Deviation 156 The Systematic Sample 208 7.2 The Binomial Distribution 158 8.6 End-of-Chapter Materials 209 Definition of Binomial Distribution Summary 209 and Proportion 159 Key Words 210 Finding the Mean and Standard Deviation Questions 211 the Easy Way 161 Problems 211 Finding the Probabilities 162 Database Exercises 217 7.3 The Normal Distribution 164 Projects 217 Visualize Probabilities as the Area under Case 218 the Curve 165 The Standard Normal Distribution Z and Its Probabilities 166 9. Confidence Intervals: Admitting That Solving Word Problems for Normal Estimates Are Not Exact Probabilities 166 The Four Different Probability Calculations 172 9.1 The Confidence Interval for a Population Be Careful: Things Need Not Be Normal! 173 Mean or a Population Percentage 220 7.4 The Normal Approximation to The t Table and the t Distribution 222 the Binomial 173 The Widely Used 95% Confidence Interval 225 7.5 TwoOtherDistributions:ThePoisson Other Confidence Levels 228 andtheExponential 176 9.2 Assumptions Needed for Validity 230 The Poisson Distribution 176 Random Sampling 230 The Exponential Distribution 177 Normal Distribution 232 7.6 End-of-Chapter Materials 178 9.3 Interpreting a Confidence Interval 234 Summary 178 Which Event Has a 95% Probability? 234 Key Words 180 Your Lifetime Track Record 235 Questions 180 9.4 One-Sided Confidence Intervals 235 Problems 180 BeCareful!YouCan’t AlwaysUse Database Exercises 185 a One-SidedInterval 235 Projects 185 Computing the One-Sided Interval 235 Case 186 9.5 Prediction Intervals 237