https://ebookmass.com/product/introduction-to-the-practiceof-statistics-9th-edition-ebook-pdf/
Instant digital products (PDF, ePub, MOBI) ready for you
Download now and discover formats that fit your needs...
Introduction to the Practice of Statistics Ninth Edition –Ebook PDF Version
https://ebookmass.com/product/introduction-to-the-practice-ofstatistics-ninth-edition-ebook-pdf-version/ ebookmass.com
An Introduction to Policing 9th Edition, (Ebook PDF)
https://ebookmass.com/product/an-introduction-to-policing-9th-editionebook-pdf/ ebookmass.com
Evidence-Based Statistics: An Introduction to the Evidential Approach - From Likelihood Principle to Statistical Practice Peter M. B. Cahusac
https://ebookmass.com/product/evidence-based-statistics-anintroduction-to-the-evidential-approach-from-likelihood-principle-tostatistical-practice-peter-m-b-cahusac/ ebookmass.com
Game Programming with Unity and C#: A Complete Beginner's Guide,
2nd Edition Casey Hardman
https://ebookmass.com/product/game-programming-with-unity-and-c-acomplete-beginners-guide-2nd-edition-casey-hardman/ ebookmass.com
ISE Abnormal Psychology: Clinical Perspectives on Psychological Disorders 10th Edition Whitbourne Professor
https://ebookmass.com/product/ise-abnormal-psychology-clinicalperspectives-on-psychological-disorders-10th-edition-whitbourneprofessor/
ebookmass.com
Religion and Mental Health, Research and Clinical Applications Harold G. Koenig
https://ebookmass.com/product/religion-and-mental-health-research-andclinical-applications-harold-g-koenig/
ebookmass.com
Programming in Ada 2012 with a Preview of Ada 2022 2nd Edition John Barnes
https://ebookmass.com/product/programming-in-ada-2012-with-a-previewof-ada-2022-2nd-edition-john-barnes/
ebookmass.com
Effective School Interventions, Third Edition: Evidence Based Strategies for Improving Student Outcomes 3rd Edition, (Ebook PDF)
https://ebookmass.com/product/effective-school-interventions-thirdedition-evidence-based-strategies-for-improving-student-outcomes-3rdedition-ebook-pdf/
ebookmass.com
(Un)Expected: Saint Stephen's Lake (Book One) (Saint Stephens Lake 1) K.C. Brooks
https://ebookmass.com/product/unexpected-saint-stephens-lake-book-onesaint-stephens-lake-1-k-c-brooks/
ebookmass.com
1st Edition Phelps
https://ebookmass.com/product/making-better-choices-design-decisionsand-democracy-1st-edition-phelps/
ebookmass.com
Section 1.2 Summary
Section 1.2 Exercises
1.3DescribingDistributionswithNumbers
Measuringcenter:Themean
Measuringcenter:Themedian
Meanversusmedian
Measuringspread:Thequartiles
Thefive-numbersummaryandboxplots
The1.5× IQR ruleforsuspectedoutliers
Measuringspread:Thestandarddeviation
Propertiesofthestandarddeviation
Choosingmeasuresofcenterandspread
Changingtheunitofmeasurement
Section 1.3 Summary
Section 1.3 Exercises
1.4DensityCurvesandNormalDistributions
Densitycurves
Measuringcenterandspreadfordensitycurves
Normaldistributions
The68–95–99.7rule
Standardizingobservations
Normaldistributioncalculations
UsingthestandardNormaltable
InverseNormalcalculations
Normalquantileplots
BeyondtheBasics:Densityestimation
Section 1.4 Summary
Section 1.4 Exercises
Chapter1Exercises
CHAPTER 2 Looking at Data—Relationships
Introduction
2.1Relationships
Examiningrelationships
Section 2.1 Summary
Section 2.1 Exercises
2.2Scatterplots
Interpretingscatterplots
Thelogtransformation
Addingcategoricalvariablestoscatterplots
Scatterplotsmoothers
Categoricalexplanatoryvariables
Section 2.2 Summary
Section 2.2 Exercises
2.3Correlation
Thecorrelation r
Propertiesofcorrelation
Section 2.3 Summary
Section 2.3 Exercises
2.4Least-SquaresRegression
Fittingalinetodata
Prediction
Least-squaresregression
Interpretingtheregressionline
Factsaboutleast-squaresregression
Correlationandregression
Anotherviewof r2
Section 2.4 Summary
Section 2.4 Exercises
2.5CautionsaboutCorrelationandRegression Residuals
Outliersandinfluentialobservations
Bewareofthelurkingvariable
Bewareofcorrelationsbasedonaverageddata
Bewareofrestrictedranges
BeyondtheBasics:Datamining
Section 2.5 Summary
Section 2.5 Exercises
2.6DataAnalysisforTwo-WayTables
Thetwo-waytable
Jointdistribution
Marginaldistributions
Describingrelationsintwo-waytables
Conditionaldistributions
Simpson’sparadox
Section 2.6 Summary
Section 2.6 Exercises
2.7TheQuestionofCausation
Explainingassociation
Establishingcausation
Section 2.7 Summary
Section 2.7 Exercises
Chapter2Exercises
CHAPTER 3 Producing Data
Introduction
3.1SourcesofData
Anecdotaldata
Availabledata
Samplesurveysandexperiments
Section 3.1 Summary
Section 3.1 Exercises
3.2DesignofExperiments
Comparativeexperiments
Randomization
Randomizedcomparativeexperiments
Howtorandomize
Randomizationusingsoftware
Randomizationusingrandomdigits
Cautionsaboutexperimentation
Matchedpairsdesigns
Blockdesigns
Section 3.2 Summary
Section 3.2 Exercises
3.3SamplingDesign
Simplerandomsamples
Howtoselectasimplerandomsample
Stratifiedrandomsamples
Multistagerandomsamples
Cautionsaboutsamplesurveys
BeyondtheBasics:Capture-recapturesampling
Section 3.3 Summary
Section 3.3 Exercises
3.4Ethics
Institutionalreviewboards
Informedconsent
Confidentiality
Clinicaltrials
Behavioralandsocialscienceexperiments
Section 3.4 Summary
Section 3.4 Exercises
Chapter3Exercises
PART II Probability and Inference
CHAPTER 4 Probability: The Study of Randomness
Introduction
4.1Randomness
Thelanguageofprobability
Thinkingaboutrandomness
Theusesofprobability
Section 4.1 Summary
Section 4.1 Exercises
4.2ProbabilityModels
Samplespaces
Probabilityrules
Assigningprobabilities:Finitenumberofoutcomes
Assigningprobabilities:Equallylikelyoutcomes
Independenceandthemultiplicationrule
Applyingtheprobabilityrules
Section 4.2 Summary
Section 4.2 Exercises
4.3RandomVariables
Discreterandomvariables
Continuousrandomvariables
Normaldistributionsasprobabilitydistributions
Section 4.3 Summary
Section 4.3 Exercises
4.4MeansandVariancesofRandomVariables
Themeanofarandomvariable
Statisticalestimationandthelawoflargenumbers
Thinkingaboutthelawoflargenumbers
BeyondtheBasics:Morelawsoflargenumbers
Rulesformeans
Thevarianceofarandomvariable
Rulesforvariancesandstandarddeviations
Section 4.4 Summary
Section 4.4 Exercises
4.5GeneralProbabilityRules
Generaladditionrules
Conditionalprobability
Generalmultiplicationrules
Treediagrams
Bayes’srule
Independenceagain
Section 4.5 Summary
Section 4.5 Exercises
Chapter4Exercises
CHAPTER 5 Sampling Distributions
Introduction
5.1TowardStatisticalInference
Samplingvariability
Samplingdistributions
Biasandvariability
Samplingfromlargepopulations
Whyrandomize?
Section 5.1 Summary
Section 5.1 Exercises
5.2TheSamplingDistributionofaSampleMean
Themeanandstandarddeviationof x
Thecentrallimittheorem
Afewmorefacts
BeyondtheBasics:Weibulldistributions
Section 5.2 Summary
Section 5.2 Exercises
5.3SamplingDistributionsforCountsand Proportions
Thebinomialdistributionsforsamplecounts
Binomialdistributionsinstatisticalsampling
Findingbinomialprobabilities
Binomialmeanandstandarddeviation
Sampleproportions
Normalapproximationforcountsandproportions
Thecontinuitycorrection
Binomialformula
ThePoissondistributions
Section 5.3 Summary
Section 5.3 Exercises
Chapter5Exercises
CHAPTER 6 Introduction to Inference
Introduction
Overviewofinference
6.1EstimatingwithConfidence
Statisticalconfidence
Confidenceintervals
Confidenceintervalforapopulationmean
Howconfidenceintervalsbehave
Choosingthesamplesize
Somecautions
Section 6.1 Summary
Section 6.1 Exercises
6.2TestsofSignificance
Thereasoningofsignificancetests
Statinghypotheses
Teststatistics
P-values
Statisticalsignificance
Testsforapopulationmean
Two-sidedsignificancetestsandconfidenceintervals
The P-valueversusastatementofsignificance
Section 6.2 Summary
Section 6.2 Exercises
6.3UseandAbuseofTests
Choosingalevelofsignificance
Whatstatisticalsignificancedoesnotmean
Don’tignorelackofsignificance
Statisticalinferenceisnotvalidforallsetsofdata
Bewareofsearchingforsignificance
Section 6.3 Summary
Section 6.3 Exercises
6.4PowerandInferenceasaDecision
Power
Increasingthepower
Inferenceasdecision
Twotypesoferror
Errorprobabilities
Thecommonpracticeoftestinghypotheses
Section 6.4 Summary
Section 6.4 Exercises
Chapter6Exercises
CHAPTER 7 Inference for Means
Introduction
7.1InferencefortheMeanofaPopulation
The t distributions
Theone-sample t confidenceinterval
Theone-sample t test
Matchedpairs t procedures
Robustnessofthe t procedures
BeyondtheBasics:Thebootstrap
Section 7.1 Summary
Section 7.1 Exercises
7.2ComparingTwoMeans
Thetwo-sample z statistic
Thetwo-sample t procedures
Thetwo-sample t confidenceinterval
Thetwo-sample t significancetest
Robustnessofthetwo-sampleprocedures
Inferenceforsmallsamples
Softwareapproximationforthedegreesoffreedom
Thepooledtwo-sample t procedures
Section 7.2 Summary
Section 7.2 Exercises
7.3AdditionalTopicsonInference
Choosingthesamplesize
Inferencefornon-Normalpopulations
Section 7.3 Summary
Section 7.3 Exercises
Chapter7Exercises
CHAPTER 8 Inference for Proportions
Introduction
8.1InferenceforaSingleProportion
Large-sampleconfidenceintervalforasingleproportion
BeyondtheBasics:Theplusfourconfidenceintervalforasingle proportion
Significancetestforasingleproportion
Choosingasamplesizeforaconfidenceinterval
Choosingasamplesizeforasignificancetest
Section 8.1 Summary
Section 8.1 Exercises
8.2ComparingTwoProportions
Large-sampleconfidenceintervalforadifferencein proportions
BeyondtheBasics:Theplusfourconfidenceintervalfora differenceinproportions
Significancetestforadifferenceinproportions
Choosingasamplesizefortwosampleproportions
BeyondtheBasics:Relativerisk
Section 8.2 Summary
Section 8.2 Exercises
Chapter8Exercises
PART III Topics in Inference
CHAPTER 9 Inference for Categorical Data
Introduction
9.1InferenceforTwo-WayTables
Thehypothesis:Noassociation
Expectedcellcounts
Thechi-squaretest
Computations
Computingconditionaldistributions
Thechi-squaretestandthe z test
BeyondtheBasics:Meta-analysis
Section 9.1 Summary
Section 9.1 Exercises
9.2GoodnessofFit
Section 9.2 Summary
Section 9.2 Exercises
Chapter9Exercises
CHAPTER 10 Inference for Regression
Introduction
10.1SimpleLinearRegression
Statisticalmodelforlinearregression
Preliminarydataanalysisandinferenceconsiderations
Estimatingtheregressionparameters
Checkingmodelassumptions
Confidenceintervalsandsignificancetests
Confidenceintervalsformeanresponse
Predictionintervals
Transformingvariables
BeyondtheBasics:Nonlinearregression
Section 10.1 Summary
Section 10.1 Exercises
10.2MoreDetailaboutSimpleLinearRegression
Analysisofvarianceforregression
TheANOVA F test
Calculationsforregressioninference
Inferenceforcorrelation
Section 10.2 Summary
Section 10.2 Exercises
Chapter10Exercises
CHAPTER 11 Multiple Regression
Introduction
11.1InferenceforMultipleRegression
Populationmultipleregressionequation
Dataformultipleregression
Multiplelinearregressionmodel
Estimationofthemultipleregressionparameters
Confidenceintervalsandsignificancetestsforregression coefficients
ANOVAtableformultipleregression
Squaredmultiplecorrelation R2
Section 11.1 Summary
Section 11.1 Exercises
11.2ACaseStudy
Preliminaryanalysis
Relationshipsbetweenpairsofvariables
Regressiononhighschoolgrades
Interpretationofresults
Examiningtheresiduals
Refiningthemodel
RegressiononSATscores
Regressionusingallvariables
Testforacollectionofregressioncoefficients
BeyondtheBasics:Multiplelogisticregression
Section 11.2 Summary
Section 11.2 Exercises
Chapter11Exercises
CHAPTER 12 One-Way Analysis of Variance
Introduction
12.1InferenceforOne-WayAnalysisofVariance
Dataforone-wayANOVA
Comparingmeans
Thetwo-sample t statistic
AnoverviewofANOVA
TheANOVAmodel
Estimatesofpopulationparameters
Testinghypothesesinone-wayANOVA
TheANOVAtable
The F test
Software
BeyondtheBasics:Testingtheequalityofspread
Section 12.1 Summary
Section 12.1 Exercises
12.2ComparingtheMeans
Contrasts
Multiplecomparisons
Power
Section 12.2 Summary
Section 12.2 Exercises
Chapter12Exercises
CHAPTER 13 Two-Way Analysis of Variance
Introduction
13.1TheTwo-WayANOVAModel
Advantagesoftwo-wayANOVA
Thetwo-wayANOVAmodel
Maineffectsandinteractions
13.2InferenceforTwo-WayANOVA
TheANOVAtablefortwo-wayANOVA
Chapter 13 Summary
Chapter13Exercises
Tables
AnswerstoOdd-NumberedExercises
NotesandDataSources
Index
To Teachers: About This Book
Statistics is the science of data. Introduction to the Practice of Statistics (IPS) is an introductory text based on this principle. We present methods of basic statistics in a way that emphasizes working with data and mastering statistical reasoning. IPS is elementary in mathematical level but conceptually rich in statistical ideas. After completing a course based on our text, we would like students to be able to think objectively about conclusionsdrawnfromdataandusestatisticalmethodsintheirownwork. In IPS, we combine attention to basic statistical concepts with a comprehensive presentation of the elementary statistical methods that students will find useful in their work. IPS has been successful for several reasons:
1. IPS examines the nature of modern statistical practice at a level suitable forbeginners.Wefocusontheproductionandanalysis ofdata aswellasthetraditionaltopicsofprobabilityandinference.
2. IPS has a logical overall progression, so data production and data analysis are a major focus, while inference is treated as a tool that helpsusdrawconclusionsfromdatainanappropriateway.
3. IPS presents data analysis as more than a collection of techniques for exploring data. We emphasize systematic ways of thinking about data. Simple principles guide the analysis: always plot your data; look for overall patterns and deviations from them; when looking at the overall pattern of a distribution for one variable, consider shape, center, and spread; for relations between two variables, consider form, direction, and strength; always ask whether a relationship between variables is influenced by other variables lurking in the background. We warn studentsaboutpitfallsinclearcautionarydiscussions.
4. IPS uses real examples to drive the exposition. Students learn the technique of least-squares regression and how to interpret the regression slope. But they also learn the conceptual ties between regressionandcorrelationandtheimportanceoflookingforinfluential observations.
5. IPS is aware of current developments both in statistical science and in teaching statistics. Brief, optional Beyond the Basics sections give quick overviews of topics such as density estimation, scatterplot smoothers, data mining, nonlinear regression, and meta-analysis. Chapter 16 gives an elementary introduction to the bootstrap and other computer-intensivestatisticalmethods.
The title of the book expresses our intent to introduce readers to statistics as it is used in practice. Statistics in practice is concerned with drawingconclusionsfromdata.Wefocusonproblemsolvingratherthanon methodsthatmaybeusefulinspecificsettings.
GAISE The College Report of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project (www.amstat.org/education/gaise/) was funded by the American Statistical Association to make recommendations for how introductory statistics courses should be taught. This report and its update contain many interesting teaching suggestions, and we strongly recommend that you read it. The philosophy and approach of IPS closely reflect the GAISE recommendations. Let’s examine each of the latest recommendations in the contextof IPS.
1. Teach statistical thinking. Through our experiences as applied statisticians, we are very familiar with the components that are needed for the appropriate use of statistical methods. We focus on formulating questions, collecting and finding data, evaluating the quality of data, exploring the relationships among variables, performing statistical analyses, and drawing conclusions. In examples and exercises throughout the text, we emphasize putting the analysis in the proper context and translating numerical and graphical summaries into conclusions.
2. Focus on conceptual understanding. Withthesoftwareavailabletoday, it is very easy for almost anyone to apply a wide variety of statistical procedures, both simple and complex, to a set of data. Without a firm grasp of the concepts, such applications are frequently meaningless. By using the methods that we present on real sets of data, we believe that students will gain an excellent understanding of these concepts. Our emphasis is on the input (questions of interest, collecting or finding data, examining data) and the output (conclusions) for a statistical analysis. Formulas are given only where they will provide someinsightintoconcepts.
3. Integrate real data with a context and a purpose. Many of the examplesandexercisesin IPS include data thatwehaveobtainedfrom collaborators or consulting clients. Other data sets have come from research related to these activities. We have also used the Internet as a data source, particularly for data related to social media and other topics of interest to undergraduates. Our emphasis on real data, rather than artificial data chosen to illustrate a calculation, serves to motivate students and help them see the usefulness of statistics in everyday life. We also frequently encounter interesting statistical issues that we explore. These include outliers and nonlinear relationships. All data setsareavailablefromthetextwebsite.
4. Foster active learning in the classroom. As we mentioned earlier, we believe that statistics is exciting as something to do rather than something to talk about. Throughout the text, we provide exercises in Use Your Knowledge sections that ask the students to perform some relatively simple tasks that reinforce the material just presented. Other exercises are particularly suited to being worked on and discussed withinaclassroomsetting.
5. Use technology for developing concepts and analyzing data. Technology has altered statistical practice in a fundamental way. In the past, some of the calculations that we performed were particularly difficult and tedious. In other words, they were not fun. Today, freed from the burden of computation by software, we can concentrate our efforts on the big picture: what questions are we trying to address with astudyandwhatcanweconcludefromouranalysis?
6. Use assessments to improve and evaluate student learning. Our goal for students who complete a course based on IPS is that they are able todesign andcarry outastatistical studyforaproject intheir capstone course or other setting. Our exercises are oriented toward this goal. Many ask about the design of a statistical study and the collection of data. Others ask for a paragraph summarizing the results of an analysis. This recommendation includes the use of projects, oral presentations, article critiques, and written reports. We believe that students using this text will be well prepared to undertake these kinds of activities. Furthermore, we view these activities not only as assessmentsbutalsoasvaluabletoolsforlearningstatistics.
Teaching Recommendations We have used IPS in courses taught to a variety of student audiences. For general undergraduates from mixed disciplines, we recommend covering Chapters 1 through 8 and Chapters 9, 10, or 12. For a quantitatively strong audience—sophomores planning to major in actuarial science or statistics—we recommend moving more quickly. Add Chapters10 and 11 to the core material in Chapters 1 through 8. In general, we recommend deemphasizing the material on probability because these students will take a probability course later in their program. For beginning graduate students in such fields as education, family studies, and retailing, we recommend that the students read the entire text (Chapters 11 and 13 lightly), again with reduced emphasis on Chapter 4 and some parts of Chapter 5. In all cases, beginning with data analysis and data production (Part I) helps students overcome their fear of statistics and builds a sound base for studying inference. We believe that IPS can easily beadaptedtoawidevarietyofaudiences.
The Ninth Edition: What’s New?
Chapter1nowbeginswithashortsectiongivinganoverviewofdata. “TowardStatisticalInference”(previouslySection3.3),which introducestheconceptsofstatisticalinferenceandsampling distributions,hasbeenmovedtoSection5.1tobetterassistwiththe transitionfromasingledatasettosamplingdistributions.
Coverageofmosaicplotsasavisualtoolforrelationshipsbetweentwo categoricalvariableshasbeenaddedtoChapters2and9.
Chapter3nowbeginswithashortsectiongivingabasicoverviewof datasources.
Coverageofequivalencetestinghasbeenaddedto Chapter7. Thereisagreateremphasisonsamplesizedeterminationusing softwareinChapters7and8.
ResamplingandbootstrappingarenowintroducedinChapter7rather thanChapter6.
“InferenceforCategoricalData”isthenewtitlefor Chapter9,which includesgoodnessoffitaswellasinferencefortwo-waytables. TherearemoreJMPscreenshotsandupdatedscreenshotsofMinitab, Excel,andSPSSoutputs.
DesignAnewdesignincorporatescolorful,revisedfiguresthroughout toaidthestudents’understandingoftextmaterial.Photographsrelated tochapterexamplesandexercisesmakeconnectionstoreal-life applicationsandprovideavisualcontextfortopics.Morefigureswith softwareoutputhavebeenincluded.
ExercisesandExamplesMorethan30%oftheexercisesarenewor revised,andtherearemorethan1700exercisestotal.Exercisesets havebeenaddedattheendofsectionsinChapters9through12.To maintaintheattractivenessoftheexamplestostudents,wehave replacedorupdatedalargenumberofthem.Morethan30%ofthe430 examplesareneworrevised.Alistofexercisesandexamples categorizedbyapplicationareaisprovidedontheinsideofthefront cover.
In addition to the new ninth edition enhancements, IPS has retained the successfulpedagogicalfeaturesfrompreviouseditions:
LookBackAtkeypointsinthetext,LookBackmarginnotesdirect thereadertothefirstexplanationofatopic,providingpagenumbers foreasyreference.
CautionWarningsinthetext,signaledbyacautionicon,helpstudents avoidcommonerrorsandmisconceptions.
ChallengeExercisesMorechallengingexercisesaresignaledwithan icon.Challengeexercisesarevaried:somearemathematical,some requireopen-endedinvestigation,andothersrequiredeeperthought aboutthebasicconcepts.
AppletsAppleticonsareusedthroughoutthetexttosignalwhere relatedinteractivestatisticalappletscanbefoundonthe IPS website andinLaunchPad.
UseYourKnowledgeExercisesWehavefoundtheseexercisestobe averyusefullearningtool.Theyappearthroughouteachsectionand arelisted,withpagenumbers,beforethesection-endingexercises.
TechnologyoutputscreenshotsMoststatisticalanalysesrelyheavily onstatisticalsoftware.Inthisbook,wediscusstheuseofExcel2013, JMP12,Minitab17,SPSS23,CrunchIt,R,andaTI-83/-84calculator forconductingstatisticalanalysis.Asspecializedstatisticalpackages, JMP,Minitab,andSPSSarethemostpopularsoftwarechoicesbothin industryandincollegesandschoolsofbusiness.Risanextremely powerfulstatisticalenvironmentthatisfreetoanyone;itreliesheavily onmembersoftheacademicandgeneralstatisticalcommunitiesfor
support.Asanall-purposespreadsheetprogram,Excelprovidesa limitedsetofstatisticalanalysisoptionsincomparison.However, givenitspervasivenessandwideacceptanceinindustryandthe computerworldatlarge,webelieveitisimportanttogiveExcel properattention.Itshouldbenotedthatforuserswhowantmore statisticalcapabilitiesbutwanttoworkinanExcelenvironment,there areanumberofcommerciallyavailableadd-onpackages(ifyouhave JMP,forinstance,itcanbeinvokedfromwithinExcel).Finally, instructionsareprovidedfortheTI-83/-84calculators.
Even though basic guidance is provided in the book, it should be emphasized that IPS is not bound to any of these programs. Computer output from statistical packages is very similar, so you can feel quite comfortableusinganyonethesepackages.
Acknowledgments
We are pleased that the first eight editions of Introduction to the Practice of Statistics have helped to move the teaching of introductory statistics in a direction supported by most statisticians. We are grateful to the many colleagues andstudents whohave provided helpful comments, andwehope that they will find this new edition another step forward. In particular, we would like to thank the following colleagues who offered specific commentsonthenewedition:
AliArab, Georgetown University
TessemaAstatkie, Dalhousie University
FouziaBaki, McMaster University
LyndaBallou, New Mexico Institute of Mining and Technology
SanjibBasu, Northern Illinois University
DavidBosworth, Hutchinson Community College
MaxBuot, Xavier University
NadjibBouzar, University of Indianapolis
MattCarlton, California Polytechnic State University–San Luis Obispo
GustavoCepparo, Austin Community College
PinyuenChen, Syracuse University
DennisL.Clason, University of Cincinnati–Blue Ash College
TaddColver, Purdue University
ChrisEdwards, University of Wisconsin–Oshkosh
IrinaGaynanova, Texas A&M University
BrianT.Gill, Seattle Pacific University
MaryGray, American University
GaryE.Haefner, University of Cincinnati
SusanHerring, Sonoma State University
LifangHsu, Le Moyne College
TiffanyKolba, Valparaiso University
LiaLiu, University of Illinois at Chicago
XuewenLu, University of Calgary
AntoinetteMarquard, Cleveland State University
FrederickG.Schmitt, College of Marin
JamesD.Stamey, Baylor University
EnginSungur, University of Minnesota–Morris
AnatoliySwishchuk, University of Calgary
RichardTardanico, Florida International University
MelaneeThomas, University of Calgary
TerriTorres, Oregon Institute of Technology
MahbobehVezvaei, Kent State University
YishiWang, University of North Carolina–Wilmington
JohnWard, Jefferson Community and Technical College
DebraWiens, Rocky Mountain College
VictorWilliams, Paine College
ChristopherWilson, Butler University
AnneYust, Birmingham-Southern College
BiaoZhang, The University of Toledo
MichaelL.Zwilling, University of Mount Union
The professionals at Macmillan, in particular, Terri Ward, Karen Carson, Jorge Amaral, Emily Tenenbaum, Ed Dionne, Blake Logan, and Susan Wein, have contributed greatly to the success of IPS. In addition, we would like to thank Tadd Colver at Purdue University for his valuable contributions to the ninth edition, including authoring the back-of-book
answers, solutions, and Instructor’s Guide. We’d also like to thank Monica Jackson at American University for accuracy reviewing the back-of-book answers and solutions and for authoring the test bank. Thanks also to Michael Zwilling at University of Mount Union for accuracy reviewing the test bank, Christopher Edwards at University of Wisconsin Oshkosh for authoring the lecture slides, and James Stamey at Baylor University for authoringtheClickerslides.
Mostofall,wearegratefultothemanyfriendsandcollaborators whose data and research questions have enabled us to gain a deeper understanding of the science of data. Finally, we would like to acknowledge the contributions of John W. Tukey, whose contributions to data analysis have had such a great influence on us as well as a whole generation of applied statisticians.
Media and Supplements
LaunchPad, our online course space, combines an interactive e-Book with high-quality multimedia content and ready-made assessment options, including LearningCurve adaptive quizzing. Content is easy to assign or adapt with your own material, such as readings, videos, quizzes, discussion groups, and more. LaunchPad also provides access to a Gradebook that offers a window into your students’ performance—either individually or as a whole. Use LaunchPad on its own or integrate it with your school’s learning management system so your class is always on the same page. To learn more about LaunchPad for Introduction to the Practice of Statistics, NinthEdition,ortorequestaccess,gotolaunchpadworks.com.
AssetsintegratedintoLaunchPadinclude:
Interactive e-Book. Every LaunchPad e-Book comes with powerful study tools for students, video and multimedia content, and easy customization for instructors. Students can search, highlight, and bookmark, making it
easier to study and access key content. And teachers can ensure that their classes get just the book they want to deliver: customize and rearrange chapters; add and share notes and discussions; and link to quizzes, activities,andotherresources.
LearningCurve provides students and instructors with powerful adaptive quizzing, a game-like format, direct links to the e-Book, and instant feedback.Thequizzingsystemfeaturesquestionstailoredspecificallytothe text and adapts to students’ responses, providing material at different difficultylevelsandtopicsbasedonstudentperformance.
JMP Student Edition (developed by SAS) is easy to learn and contains all the capabilities required for introductory statistics. JMP is the leading commercial data analysis software of choice for scientists, engineers, and analysts at companies throughout the world (for Windows and Mac). RegisterinsideLaunchPadatnoadditionalcost.
CrunchIt!® is a Web-based statistical program that allows users to perform all the statistical operations and graphing needed for an introductory statistics course and more. It saves users time by automatically loading data from IPS, 9e, and it provides the flexibility to edit and import additional data.
StatBoards Videos are brief whiteboard videos that illustrate difficult topics through additional examples, written and explained by a select group ofstatisticseducators.
Stepped Tutorials are centered on algorithmically generated quizzing with step-by-step feedback to help students work their way toward the correct solution. These exercise tutorials (two to three per chapter) are easily assignableandassessable.
Statistical Video Series consists of StatClips, StatClips Examples, and Statistically Speaking “Snapshots.” View animated lecture videos, whiteboard lessons, and documentary-style footage that illustrate key statistical concepts and help students visualize statistics in real-world scenarios.
Video Technology Manuals, available for TI-83/84 calculators, Minitab, Excel, JMP, SPSS, R, Rcmdr, and CrunchIt!® , provide brief instructions for usingspecificstatisticalsoftware.
StatTutor Tutorials offer multimedia tutorials that explore important concepts and procedures in a presentation that combines video, audio, and interactive features. The newly revised format includes built-in, assignable assessmentsandabrightnewinterface.
Statistical Applets give students hands-on opportunities to familiarize themselves with important statistical concepts and procedures in an interactive setting that allows them to manipulate variables and see the results graphically. Icons in the textbook indicate when an applet is available for the material being covered. Applets are assessable and assignableinLaunchPad.
Stats@Work Simulations put students in the role of the statistical consultant, helping them better understand statistics interactively within the contextofreal-lifescenarios.
EESEECaseStudies(ElectronicEncyclopediaofStatisticalExamplesand Exercises), developed by The Ohio State University Statistics Department, teach students to apply their statistical skills by exploring actual case studiesusingrealdata.
