Data Analysis and Decision Making with Microsoft Excel

Part I: GETTING, DESCRIBING, AND SUMMARIZING DATA. Introduction to Data Analysis and Decision Making. 1. Introduction. An Overview of the Book. The Methods. TheSoftware. A Sampling of Examples. Modeling and Models.Conclusion. 2. Describing Data: Graphs and Tables. Introduction. Basic Concepts. Frequency Tables and Histograms.Analyzing Relationships with Scatterplots. Time Series Graphs.Exploring Data with Pivot Tables. Conclusion. 3. Describing Data: Summary Measures. Introduction. Measures of Central Location. Quartiles andPercentiles. Minimum, Maximum, and Range. Measures of Variability:Variance and Standard Deviation. Obtaining Summary Measures withStatTools. Measures of Association: Covariance and Correlation.Describing Data Sets with Boxplots. Applying the Tools. Conclusion. 4. Getting the Right Data. Introduction. Sources ofData. Using Excel'sAutoFilter. Complex Querieswith the Advanced Filter.Importing External Datafrom Access. CreatingPivot Tables fromExternal Data. WebQueries. Other DataSources on the Web.Cleansing the Data. Conclusion. Part II: Probability, Uncertainty, and Decision Making. 5. Probability and Probability Distributions. Introduction. Probability Essentials.Distribution of a Single RandomVariable. An Introduction toSimulation. Distribution of TwoRandom Variables: ScenarioApproach. Distribution ofTwo Random Variables:Joint ProbabilityApproach. IndependentRandom Variables.Weighted Sums ofRandom Variables. Conclusion. 6. Normal, Binomial, Poisson, and Exponential Distributions. Introduction. The Normal Distribution. Applications of the NormalDistribution. The Binomial Distribution. Applications of the BinomialDistribution. The Poisson and Exponential Distributions. Fitting aProbability Distribution to Data: BestFit. Conclusion. 7. Decision Making Under Uncertainty. Introduction. Elements of aDecision Analysis. ThePrecisionTree Add-In.Bayes' Rule. MultistageDecision Problems. Incorporating Attitudes Toward Risk. Conclusion. Part III: Statistical Inference. 8. Sampling and Sampling Distributions. Introduction. Sampling Terminology. Methods for Selecting RandomSamples. An Introduction to Estimation. Conclusion. 9. Confidence Interval Estimation. Introduction. Sampling Distributions. Confidence Interval for a Mean.Confidence Interval for a Total. Confidence Interval for aProportion. Confidence Interval for a Standard Deviation. ConfidenceInterval for the Difference Between Means. Confidence Interval forthe Difference Between Proportions Controlling Confidence IntervalLength. Conclusion. 10. Hypothesis Testing. Introduction. Conceptsin Hypothesis Testing.Hypothesis Tests fora Population Mean.Hypothesis Testsfor OtherParameters.Tests forNormality.Chi-SquareTest forIndependence.One-WayANOVA. Conclusion. Part IV: Regression, Forecasting, and Time Series. 11. Regression Analysis: Estimating Relationships. Introduction. Scatterplots: Graphing Relationships. Correlations:Indicators of Linear Relationships Simple Linear Regression. MultipleRegression. Modeling Possibilities. Validation of the Fit. Conclusion. 12. Regression Analysis: Statistical Inference Introduction. TheStatistical Model. Inferences About the Regression Coefficients.Multicollinearity. Include/Exclude Decisions. Stepwise Regression.ThePartial F Test. Outliers. Violations of Regression Assumptions.Prediction. Conclusion. 13. Time Series Analysis and Forecasting. Introduction.Forecasting Methods:An Overview. Testingfor Randomness.Regression-BasedTrend Models. TheRandom Walk Model.Autoregression Models.Moving Averages.ExponentialSmoothing.SeasonalModels.Winters'Exponential Smoothing Model. Conclusion. Part V: Decision Modeling. 14. Introduction to Optimization Modeling. Introduction. Introduction to Optimization. ATwo-Variable Model. Sensitivity AnalysisProperties of Linear Models. Infeasibility and Unboundedness. AProduct Mix Model. A Multiperiod Production Model. A Comparisonof Algebraic and Spreadsheet Models. A Decision Support System.Conclusion. 15. Optimization Modeling: Applications. Introduction. Workforce SchedulingModels. Blending Models. Logistics Models. Aggregate PlanningModels. Financial Models. Integer Programming Models. NonlinearModels. Conclusion. 16.Introduction to Simulation Modeling. Introduction. Real Applicationsof Simulation. ProbabilityDistributions for InputVariables. Simulationwith Built-In ExcelTools. Introduction to@RISK. The Effects ofInput Distributions on Results. Conclusion. 17.Simulation Models. Introduction. Operations Models. Financial Models. MarketingModels. Simulating Games of Chance. Conclusion.