DescriptionFor courses in Business Statistics Business Statistics, Third Edition, by Sharpe, De Veaux, and Velleman, narrows the gap between theory and practice–relevant statistical methods empower business students to make effective, data-informed decisions. With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Focusing on statistics in the context of real business issues, with an emphasis on analysis and understanding over computation, the text helps students be analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.This program provides a better teaching and learning experience—for you and your students. Here’s how:Grounded in modern business, this text provides a real-world context for statistical concepts, preparing students to be successful in the business world.Practice and support: Study tools throughout the text prepare students to analyze and interpret data.Integrated technology: Optional coverage helps students use real statistics softwareNEW! Improved organization and a streamlined design make the text more accessible than ever.Key FeaturesGrounded in modern business, this text provides a real-world context for statistical concepts, preparing students to be successful in the business world.The authors’ unique blend of teaching, consulting, and entrepreneurial experiences, along with the casual writing style, which is often infused with humor, add to the text’s appeal to today’s students.NEW! Increased focus on core material. Discussions have been tightened to get students into the material as quickly as possible, focusing increasingly n the central ideas and core material.Real data in hundreds of examples, exercises, and applications tie the concepts to the way statistics is used to make better business decisions.Motivating chapter opening vignettes: scenarios using real data to present a statistical issue in a managerial setting from a well-known company in such areas as marketing, finance, and economicsBrief Cases at the end of each chapter–using real data and asking students to investigate a question or make a business decision.Case Studies at the end of each part–more in-depth than the brief CasesNEW! Updated examples reflect the changing world and the marked changes in the U.S. and world economies.The emphasis on better statistical thinking trains students to apply statistics correctly.A focus on checking assumptions and conditions when using statistical procedures is emphasized throughout the text and the examples.Ethics in Action vignettes illustrate the judgment needed in statistical analysis. Questions are included for study and reflection.NEW! More than half of the Ethics in Action features are updated. Ethically and statistically sound alternative approaches and a link to the American Statistical Association’s Ethical Guidelines are now presented in the Instructor’s Solutions Manual, making the Ethics features suitable for assignment or class discussion.What Can Go Wrong? sections prepare students with the tools they need to detect common statistical errors and offer practice in debunking misuses of statistics.Practice and support: Study tools throughout the text prepare students to analyze and interpret data.Plan/Do/Report Guided Examples provide a model to help students approach and solve any business statistics problem. Reports are frequently presented in the form of a business memo, helping students become familiar with framing and communicating results in a business setting.Helpful Margin Notes include By Hand boxes break apart the calculation of some of the simpler formulas, Notation Alerts that call out special notations, and Math Boxes present the mathematical underpinnings of the statistical methods and concepts.Just Checking questions ask students to stop and think about what they’ve just read. These questions involve little to no calculation, and answers are provided at the end of the chapter so students can check their work.What Have We Learned? chapter summaries provide an overview of the chapter’s concepts through annotated learning objectives and a list of boldface (new) terms and their definitions.Exercises are included within each chapter and progress in difficulty and complexity.Integrated technology: Optional coverage helps students use real statistics softwareNEW! Enhanced Technology Help with expanded Excel® 2013 coverage. We’ve updated Technology Help and added detailed instructions for Excel 2013 to almost every chapter. Increased coverage of Excel includes screenshots and, in Technology Help sections, guidance for using Excel 2010 to demonstrate how to use Excel to perform statistical analysis.Technology Help sections have been updated to reflect the latest technology releases. Technology Help now includes step-by-step guidance for XLSTAT™ for Pearson, an Excel add-in offered (bundled) with the textbook.XLSTAT™ for Pearson is an Excel add-in that enhances the analytical capabilities of Excel. Developed in 1993, XLSTAT is used by leading businesses and universities around the world. XLSTAT is compatible with all Excel versions (except Mac 2008) and is compatible with both Windows® and Mac® systems.NEW! Improved organization and a streamlined design make the text more accessible than ever. While retaining the text’s successful “data first” presentation of topics, the authors have improved the way the book is organized.Chapters 1—4 are streamlined to cover collecting, displaying, summarizing, and understanding data in four chapters, giving students a solid foundation to launch their study of probability and statistics.Chapters 5—9 introduce students to randomness and probability, and then apply these new concepts to sampling as a gateway to the core material on statistical inference. Discussion of probability tees and Bayes’ rule is now in these chapters.Chapters 10—14 cover inference for both proportions and means.Chapters 15—19 cover regression-based models for decision making.Chapters 20—25 discuss special topics that can be selected according to the needs of the course and the instructor’s preferences.New to this EditionNew and Updated in the TextIncreased focus on core material. Discussions have been tightened to get students into the material as quickly as possible, focusing increasingly n the central ideas and core material.Updated examples reflect the changing world and the marked changes in the U.S. and world economies.NEW! More than half of the Ethics in Action features are updated. Ethically and statistically sound alternative approaches and a link to the American Statistical Association’s Ethical Guidelines are now presented in the Instructor’s Solutions Manual, making the Ethics features suitable for assignment or class discussion.Enhanced Technology Help with expanded Excel® 2013 coverage. We’ve updated Technology Help and added detailed instructions for Excel 2013 to almost every chapter. Increased coverage of Excel includes screenshots and, in Technology Help sections, guidance for using Excel 2010 to demonstrate how to use Excel to perform statistical analysis.Improved organization and a streamlined design make the text more accessible than ever. While retaining the text’s successful “data first” presentation of topics, the authors have improved the way the book is organized.Chapters 1—4 are streamlined to cover collecting, displaying, summarizing, and understanding data in four chapters, giving students a solid foundation to launch their study of probability and statistics.Chapters 5—9 introduce students to randomness and probability, and then apply these new concepts to sampling as a gateway to the core material on statistical inference. Discussion of probability tees and Bayes’ rule is now in these chapters.Chapters 10—14 cover inference for both proportions and means.Chapters 15—19 cover regression-based models for decision making.Chapters 20—25 discuss special topics that can be selected according to the needs of the course and the instructor’s preferences.Table of ContentsPrefaceIndex of ApplicationsData and Decisions (E-Commerce)Data and DecisionsVariable TypesData Sources: Where, How, and WhenEthics in ActionTechnology Help: Data on the ComputerBrief Case: Credit Card BankDisplaying and Describing Categorical Data (Keen, Inc.)Summarizing a Categorical VariableDisplaying a Categorical VariableExploring Two Categorical Variables: Contingency TablesSegmented Bar Charts and Mosaic PlotsSimpson’s ParadoxEthics in ActionTechnology Help: Displaying Categorical Data on the ComputerBrief Case: Credit Card Bank. Displaying and Describing Quantitative Data (AIG)Displaying Quantitative VariablesShapeCenterSpread of the DistributionShape, Center, and Spread–A SummaryStandardizing VariablesFive-Number Summary and BoxplotsComparing Groups,Identifying Outliers,Time Series PlotsTransforming Skewed DataEthics in ActionTechnology Help: Displaying and Summarizing Quantitative VariablesBrief Cases: Detecting the Housing Bubble and Socio-Economic Data on States. Correlation and Linear Regression (Amazon.com)Looking at ScatterplotsAssigning Roles to Variables in ScatterplotsUnderstanding CorrelationLurking Variables and CausationThe Linear ModelCorrelation and the LineRegression to the MeanChecking the ModelVariation in the Model and R2Reality Check: Is the Regression Reasonable?Nonlinear RelationshipsEthics in ActionTechnology Help: Correlation and RegressionBrief Cases: Fuel Efficiency, Cost of Living, and Mutual FundsCase Study I: Paralyzed Veterans of America. Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)Random Phenomena and ProbabilityThe Nonexistent Law of AveragesDifferent Types of ProbabilityProbability RulesJoint Probability and Contingency TablesConditional ProbabilityConstructing Contingency TablesProbability TreesReversing the Conditioning: Bayes’ RuleEthics in ActionTechnology Help: Generating Random NumbersBrief Case. Random Variables and Probability Models (Metropolitan Life Insurance Company)Expected Value of a Random VariableStandard Deviation of a Random VariableProperties of Expected Values and VariancesBernoulli TrialsDiscrete Probability ModelsEthics in ActionTechnology Help: Random Variables and Probability ModelsBrief Case: Investment Options. The Normal and other Continuous Distributions (The NYSE)The Standard Deviation as a RulerThe Normal DistributionNormal Probability PlotsThe Distribution of Sums of NormalsThe Normal Approximation for the BinomialThe Other Continuous Random VariablesEthics in ActionTechnology Help: Probability Calculations and PlotsBrief Case. Surveys and Sampling (Roper Polls)Three Ideas of SamplingPopulations and ParametersCommon Sampling DesignsThe Valid SurveyHow to Sample BadlyEthics in ActionTechnology Help: Random SamplingBrief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)The Distribution of Sample ProportionsA Confidence IntervalMargin of Error: Certainty vs. PrecisionChoosing and Sample SizeEthics in ActionTechnology Help: Confidence Intervals for ProportionsBrief Case: Real Estate SimulationCase Study II. Testing Hypotheses about Proportions (Dow Jones Industrial Average)HypothesesA Trial as a Hypothesis TestP-ValuesThe Reasoning of Hypothesis TestingAlternative Hypothesesp-Values and Decisions: What to Tell About a Hypothesis TestEthics in ActionTechnology Help: Hypothesis TestsBrief Cases: Metal Production and Loyalty ProgramConfidence Intervals and Hypothesis Tests for Means (Guinness & Co.)The Central Limit TheoremThe Sampling Distribution of the MeanHow Sampling Distribution Models WorkGossett and the t-DistributionA Confidence Interval for MeansAssumptions and ConditionsTesting Hypothesis about Means–the One-Sample t-TestEthics in ActionTechnology Help: Inference for MeansBrief Cases: Real Estate and Donor ProfilesMore About Tests and Intervals (Traveler’s Insurance)How to Think About P-ValuesAlpha Levels and SignificanceCritical ValuesConfidence Intervals and Hypothesis TestsTwo Types of ErrorsPowerEthics in ActionTechnology Help: Hypothesis TestsBrief CaseComparing Two Means (Visa Global Organization)Comparing Two MeansThe Two-Sample t-TestAssumptions and ConditionsA Confidence Interval for the Difference Between Two MeansThe Pooled t-TestPaired DataPaired MethodsEthics in ActionTechnology Help: Two-Sample MethodsTechnology Help: Paired tBrief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)Inference for Counts: Chi-Square Tests (SAC Capital)Goodness-of-Fit TestsInterpreting Chi-Square ValuesExamining the ResidualsThe Chi-Square Test of HomogeneityComparing Two ProportionsChi-Square Test of IndependenceEthics in ActionTechnology Help: Chi-SquareBrief Cases: Health Insurance and Loyalty ProgramCase Study III: Investment Strategy SegmentationInference for Regression (Nambé Mills)A Hypothesis Test and Confidence Interval for the SlopeAssumptions and ConditionsStandard Errors for Predicted ValuesUsing Confidence and Prediction IntervalsEthics in ActionTechnology Help: Regression AnalysisBrief Cases: Frozen Pizza and Global Warming?Understanding Residuals (Kellogg’s)Examining Residuals for GroupsExtrapolation and PredictionUnusual and Extraordinary ObservationsWorking with Summary ValuesAutocorrelationTransforming (Re-expressing) DataThe Ladder of PowersEthics in ActionTechnology Help: Examining ResidualsBrief Cases: Gross Domestic Product and Energy SourcesMultiple Regression (Zillow.com)The Multiple Regression ModelInterpreting Multiple Regression CoefficientsAssumptions and Conditions for the Multiple Regression ModelTesting the Multiple Regression ModelAdjusted R2 and the F-statisticThe Logistic Regression ModelEthics in ActionTechnology Help: Regression AnalysisBrief Case: Golf SuccessBuilding Multiple Regression Models (Bolliger and Mabillard)Indicator (or Dummy) VariablesAdjusting for Different Slopes–Interaction TermsMultiple Regression DiagnosticsBuilding Regression ModelsCollinearityQuadratic TermsEthics in ActionTechnology Help: Building Multiple Regression ModelsBrief CaseTime Series Analysis (Whole Food Market)What Is a Time Series?Components of a Time SeriesSmoothing MethodsSummarizing Forecast ErrorAutoregressive ModelsMultiples Regression-based ModelsChoosing a Time Series Forecasting MethodInterpreting Time Series Models: The Whole Foods Data RevisitedEthics in ActionTechnology HelpBrief Cases: Intel Corporation and Tiffany & Co.Case Study IV: Health Care CostsDesign and Analysis of Experiments and Observational Studies (Capital One)Observational StudiesRandomized Comparative ExperimentsThe Four Principles of Experimental DesignExperimental DesignsIssues in Experimental DesignAnalyzing a Design in One Factor–The One-Way Analysis of VarianceAssumptions and Conditions for ANOVAMultiple ComparisonsANOVA on Observational DataAnalysis of Multifactor DesignsEthics in ActionTechnology Help: Analysis of VarianceBrief Case: Multifactor Experiment DesignQuality Control (Sony)A Short History of Quality ControlControl Charts for Individual Observations (Run Charts)Control Charts for Measurements: (x-bar) and R ChartsActions for Out-of-Control ProcessesControl Charts for Attributes: p Charts and c ChartsPhilosophies of Quality ControlEthics in ActionTechnology Help: Quality Control ChartsBrief Case: Laptop Touchpad QualityNonparametric Methods (i4cp)RanksThe Wilcoxon Rank-Sum/Mann-Whitney StatisticKruskal-Wallace TestPaired Data: The Wilcoxon Signed-Rank TestFriedman Test for a Randomized Block DesignKendall’s Tau: Measuring MonotonicitySpearman’s RhoWhen Should You Use Nonparametric Methods?Ethics in ActionTechnology HelpBrief Case: Real Estate ReconsideredDecision Making and Risk (Data Description, Inc.)Actions, States of Nature, and OutcomesPayoff Tables and Decisions TreesMinimizing Loss and Maximizing GainThe Expected Value of an ActionExpected Value with Perfect InformationDecisions Made with Sample InformationEstimating VariationSensitivitySimulationMore Complex DecisionsEthics in ActionTechnology HelpBrief Cases: Texaco-Pennzoil and Insurance Services, RevisitedIntroduction to Data Mining (Paralyzed Veterans of America)The Big Data RevolutionDirect MarketingThe Goals of Data MiningData Mining MythsSuccessful Data MiningData Mining ProblemsData Mining AlgorithmsThe Data Mining ProcessSummaryEthics in ActionCase Study V Marketing ExperimentAppendicesA. AnswersB. Photo AcknowledgmentsC. Tables and Selected FormulasIndexAuthors BiographyAs a researcher of statistical problems in business and a professor of Statistics at a business school, Norean Radke Sharpe (Ph.D. University of Virginia) understands the challenges and specific needs of the business student. She is currently teaching at the McDonough School of Business at Georgetown University, where she is also Senior Associate Dean and Director of Undergraduate Programs. Prior to joining Georgetown, she taught business statistics and operations research courses to both undergraduate and MBA students for fourteen years at Babson College. Before moving into business education, she taught statistics for several years at Bowdoin College and conducted research at Yale University. Norean is coauthor of the recent text, A Casebook for Business Statistics: Laboratories for Decision Making, and she has authored more than 30 articles―primarily in the areas of statistics education and women in science. Norean currently serves as Associate Editor for the journal Cases in Business, Industry, and Government Statistics. Her research focuses on business forecasting and statistics education. She is also co-founder of DOME Foundation, Inc., a nonprofit foundation that works to increase Diversity and Outreach in Mathematics and Engineering for the greater Boston area. She has been active in increasing the participation of women and underrepresented students in science and mathematics for several years and has two children of her own.Richard D. De Veaux (Ph.D. Stanford University) is an internationally known educator, consultant, and lecturer. Dick has taught statistics at a business school (Wharton), an engineering school (Princeton), and a liberal arts college (Williams). While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching. Since 1994, he has taught at Williams College, although he returned to Princeton for the academic year 2006–2007 as the William R. Kenan Jr. Visiting Professor of Distinguished Teaching. He is currently the C. Carlisle and Margaret Tippit Professor of Statistics at Williams College. Dick holds degrees from Princeton University in Civil Engineering and Mathematics and from Stanford University in Dance Education and Statistics, where(he studied with Persi Diaconis. His research focuses on the analysis of large data sets(and data mining in science and industry. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is an elected member of the International Statistics Institute (ISI) and a Fellow of the American Statistical Association (ASA). He currently serves on the Board of Directors of the ASA. Dick is well known in industry, having consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. He was named the “Statistician of the Year” for 2008 by the Boston Chapter of the American Statistical Association for his contributions to teaching, research, and consulting. In his spare time he is an avid cyclist and swimmer. He also is the founder and bass for the doo-wop group, the Diminished Faculty, and is a frequent singer and soloist with various local choirs including the Choeur Vittoria of Paris, France. Dick is the father of four children.Paul F. Velleman (Ph.D. Princeton University) has an international reputation for innovative statistics education. He designed the Data Desk® software package and is also the author and designer of the award-winning ActivStats® multimedia software, for which he received the EDUCOM Medal for innovative uses of computers in teaching statistics and the ICTCM Award for Innovation in Using Technology in College Mathematics. He is the founder and CEO of Data Description, Inc.
