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Author: Norean Radke Sharpe; Richard D De Veaux; Paul F Velleman
Publisher: Boston : Addison Wesley, ©2012.
Edition/Format:   Print book : CD for computer : English : 2nd edView all editions and formats

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Document Type: Book
All Authors / Contributors: Norean Radke Sharpe; Richard D De Veaux; Paul F Velleman
ISBN: 9780321716095 0321716094 9780321762726 032176272X
OCLC Number: 503307334
Notes: Includes index.
Description: xxix, 879, 92 pages : illustrations (chiefly color) ; 29 cm + 1 CD-ROM (4 3/4 in.)
Contents: EXPLORING AND COLLECTING DATA 1. Statistics and Variation1.1 So, What Is Statistics?1.2 How Will This Book Help? 2. DataAmazon.com2.1 What Are Data?2.2 Variable Types2.3 Data Sources: Where, How, and WhenEthics in ActionTechnology HelpBrief Cases: Credit Card Bank 3. Surveys and SamplingRoper Polls3.1 Three Ideas of Sampling3.2 Populations and Parameters3.3 Other Sample Designs3.4 The Valid Survey3.5 How to Sample BadlyEthics in ActionTechnology Help: Random SamplingBrief Cases: Market Survey ResearchThe GfK Roper Reports Worldwide Survey 4. Displaying and Describing Categorical DataKeen4.1 Summarizing a Categorical Variable4.2 Displaying a Categorical Variable4.3 Exploring Two Categorical Variables: Contingency TablesEthics in ActionTechnology Help: Displaying Categorical Data on the ComputerBrief Cases: KEEN 5. Displaying and Describing Quantitative DataAIG5.1 Displaying Quantitative Variables5.2 Shape5.3 Center5.4 Spread of the Distribution5.5 Shape, Center, and Spread-A Summary5.6 Five-Number Summary and Boxplots5.7 Comparing Groups5.8 Identifying Outliers5.9 Standardizing*5.10 Time Series Plots*5.11 Transforming Skewed DataEthics in ActionTechnology Help: Displaying and SummarizingQuantitative VariablesBrief Cases Hotel Occupancy Rates 122Value and Growth Stock Returns 122 6. Correlation and Linear RegressionLowe's6.1 Looking at Scatterplots6.2 Assigning Roles to Variables in Scatterplots6.3 Understanding Correlation6.4 Lurking Variables and Causation6.5 The Linear Model6.6 Correlation and the Line6.7 Regression to the Mean6.8 Checking the Model6.9 Variation in the Model and R26.10 Reality Check: Is the Regression Reasonable?6.11 Non-linear RelationshipsEthics in ActionTechnology Help: Correlation and RegressionBrief Cases: Fuel EfficiencyThe U.S. Economy and Home Depot Stock PricesCost of LivingMutual Funds Case Study: Paralyzed Veterans of America PART II. MODELING WITH PROBABLITY 7. Randomness and ProbabilityCredit Reports and the Fair Isaacs Corporation7.1 Random Phenomena and Probability7.2 The Nonexistent Law of Averages7.3 Different Types of Probability7.4 Probability Rules7.5 Joint Probability and Contingency Tables7.6 Conditional Probability7.7 Constructing Contingency TablesBrief Case: Market Segmentation 8. Random Variables and Probability ModelsMetropolitan Life Insurance Company8.1 Expected Value of a Random Variable8.2 Standard Deviation of a Random Variable8.3 Properties of Expected Values and Variances8.4 Discrete Probability DistributionsEthics in ActionBrief Case: Investment Options 9. The Normal DistributionThe NYSE9.1 The Standard Deviation as a Ruler9.2 The Normal Distribution9.3 Normal Probability Plots9.4 The Distribution of Sums of Normals9.5 The Normal Approximation for the Binomial9.6 Other Continuous Random VariablesEthics In ActionBrief Cases: The CAPE10Technology Help: Making Normal Probability Plots 10. Sampling DistributionsMarketing Credit Cards: The MBNA Story10.1 The Distribution of Sample Proportions10.2 Sampling Distribution for Proportions10.3 The Central Limit Theorem10.4 The Sampling Distribution of the Mean10.5 How Sampling Distribution Models WorkEthics in ActionBrief Cases Real Estate SimulationPart 1: ProportionsMeans Case Study: Investigating the Central Limit Theorem PART III. INFERENCE FOR DECISION MAKING 11. Confidence Intervals for ProportionsThe Gallup Organization11.1 A Confidence Interval11.2 Margin of Error: Certainty vs. Precision11.3 Assumptions and Conditions11.4 Choosing the Sample Size*11.5 A Confidence Interval for Small SamplesEthics in ActionTechnology Help: Confidence Intervals for ProportionsBrief Cases: InvestmentForecasting Demand 12. Confidence Intervals for MeansGuinness & Co.12.1 The Sampling Distribution for the Mean12.2 A Confidence Interval for Means12.3 Assumptions and Conditions12.4 Cautions About Interpreting Confidence Intervals12.5 Sample Size12.6 Degrees of Freedom - Why (n-1)?Ethics in ActionTechnology Help: Inference for MeansBrief Cases: Real EstateDonor Profiles 13. Testing HypothesesDow Jones Industrial Average13.1 Hypotheses13.2 A Trial as a Hypothesis Test13.3 P-values13.4 The Reasoning of Hypothesis Testing13.5 Alternative Hypotheses13.6 Testing Hypothesis about Means - the One13.7 Alpha Levels and Significance13.8 Critical Values13.9 Confidence Intervals and Hypothesis Tests13.10 Two Types of Errors*13.11 PowerEthics in ActionTechnology HelpBrief Cases: Metal ProductionLoyalty Program 14. Comparing Two GroupsVisa Global Organization14.1 Comparing Two Means14.2 The Two-Sample t-Test14.3 Assumptions and Conditions14.4 A Confidence Interval for the Difference Between Two Means14.5 The Pooled t-Test14.6 Tukey's Quick Test14.7 Paired Data14.8 The Paired t-TestEthics in ActionTechnology Help: Two-Sample MethodsBrief Cases: Real EstateConsumer Spending Patterns (Data Analysis) 15. Inference for Counts: Chi-Square TestsSAC Capital15.1 Goodness-of-Fit Tests15.2 Interpreting Chi-Square Values15.3 Examining the Residuals15.4 The Chi-Square Test of Homogeneity15.5 Comparing Two Proportions15.6 Chi-Square Test of IndependenceEthics in ActionTechnology Help: Chi-SquareBrief Cases: Health InsuranceLoyalty Program Case Study Part IV. MODELS FOR DECISION MAKING 16. Inference for RegressionNambe Mills16.1 The Population and the Sample16.2 Assumptions and Conditions16.3 The Standard Error of the Slope16.4 A Test for the Regression Slope16.5 A Hypothesis Test for Correlation16.6 Standard Errors for Predicted Values16.7 Using Confidence and Prediction IntervalsEthics in ActionTechnology Help: Regression AnalysisBrief Cases: Frozen PizzaGlobal Warming? 17. Understanding ResidualsKellogg's17.1 Examining Residuals for Groups17.2 Extrapolation and Prediction17.3 Unusual and Extraordinary Observations17.4 Working with Summary Values17.5 Autocorrelation17.6 Transforming (Re-expressing) Data17.7 The Ladder of PowersEthics in ActionTechnology HelpBrief Cases: Gross Domestic ProductEnergy Sources 18. Multiple RegressionZillow.com18.1 The Multiple Regression Model18.2 Interpreting Multiple Regression Coefficients18.3 Assumptions and Conditions for the Multiple Regression Model18.4 Testing the Multiple Regression Model18.5 Adjusted R2, and the F-statistic*18.6 The Logistic Regression ModelEthics in ActionTechnology Help: Regression AnalysisBrief Case: Golf Success 19. Building Multiple Regression ModelsBolliger and Mabillard19.1 Indicator (or Dummy) Variables19.2 Adjusting for Different Slopes-Interaction19.3 Multiple Regression Diagnostics19.4 Building Regression Models19.5 Collinearity19.6 QuadraticEthics in ActionTechnology Help: Regression Analysis on the ComputerBrief Cases: Paralyzed Veterans of America 20. Time Series AnalysisWhole Foods Market (R)20.1 What is a Time-Series?20.2 Components of a Time Series20.3 Smoothing Methods20.4 Summarizing Forecast Error20.5 Autoregressive Models20.6 Multiple Regression-based Models20.7 Choosing a Time Series Forecasting Method20.8 Interpreting Time Series Models: The Whole Foods Data RevisitedEthics in ActionTechnology HelpBrief Cases: Intel CorporationTiffany & Co.Case Study: title to come PART V. SELECTED TOPICS IN DECISION MAKING 21. Design and Analysis of Experiments and Observational StudiesCapital One21.1 Observational Studies21.2 Randomized, Comparative Experiments21.3 The Four Principles of Experimental Design21.4 Experimental Designs21.5 Issues in Experimental Design21.6 Analyzing a Completely Randomized Design in One Factor-The One-Way Analysis of Variance21.7 Assumptions and Conditions for ANOVA*21.8 Multiple Comparisons21.9 ANOVA on Observational Data21.10 Analysis of Multi Factor DesignsEthics in ActionTechnology HelpBrief Cases: A Multifactor Experiment 22. Quality ControlSony22.1 A Short History of Quality Control22.2 Control Charts for Individual Observations (Run Charts)22.3 Control Charts for Measurements: X and R Charts22.4 Actions for Out of Control Processes22.5 Control Charts for Attributes: p Charts and c Charts22.6 Philosophies of Quality ControlEthics in ActionTechnology Help: Quality Control ChartsBrief Cases 23. Nonparametric Methodsi4cp23.1 Ranks23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic23.3 Kruskal-Wallace Test23.4 Paired Data: The Wilcoxon Signed-Rank Test*23.5 Friedman Test for a Randomized Block Design23.6 Kendall's Tau: Measuring Monotonicity23.7 Spearman's Rho23.8 When Should You Use Nonparametric Methods?Ethics in ActionBrief Cases: Real Estate Reconsidered 24. Decision Making and RiskData Description, Inc.24.1 Actions, States of Nature, and Outcomes24.2 Payoff Tables and Decision Trees24.3 Minimizing Loss and Maximizing Gain24.4 The Expected Value of an Action24.5 Expected Value with Perfect Information24.6 Decisions Made with Sample Information24.7 Estimating Variation24.8 Sensitivity24.9 Simulation24.10 Probability Trees*24.11 Reversing the Conditioning: Bayes's Rule24.12 More Complex DecisionsEthics in ActionBrief Cases: Texaco-PennzoilInsurance Services, Revisited 25. Introduction to Data MiningParalyzed Veterans of America25.1 Direct Marketing25.2 The Data25.3 The Goals of Data Mining25.4 Data Mining Myths25.5 Successful Data Mining25.6 Data Mining Problems25.7 Data Mining Algorithms25.8 The Data Mining Process25.9 SummaryEthics in Action Case Study *Indicates an optional topic AppendicesA. AnswersB. XLStatC. Photo AcknowledgmentsD. Tables and Selected FormulasE. Index
Responsibility: Norean R. Sharpe, Richard D. De Veaux, Paul F. Velleman ; with contributions from Dave Bock.


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