Data Analysis and Decision Making with Microsoft Excel

DATA ANALYSIS, OPTIMIZATION, AND SIMULATION MODELING, 4e, International Edition emphasizes data analysis, modeling, and spreadsheet use in statistics and management science. Professional Excel software add-ins are included, making this text a market leader in its first edition for its clarity of writing, a teach-by-example approach, and complete Excel integration. This edition is compatible with Excel 2007 and the corresponding add-ins for Excel 2007. Updates regarding Excel 2010 are included where applicable.

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Preface
1. Introduction to Data Analysis and Decision Making.
1.1. Introduction.
1.2. An Overview of the Book.
1.3. Modeling and Models.
1.4. Conclusion.
PART I: EXPLORING DATA.
2. Describing the Distribution of a Single Variable.
2.1 Introduction.
2.2 Basic Concepts.
2.3 Descriptive Measures for Categorical Variables.
2.4 Descriptive Measures for Numerical Variables.
2.5 Time Series Data.
2.6 Outliers and Missing Values.
2.7 Excel Tables for Filtering, Sorting, and Summarizing.
2.8 Conclusion.
3. Finding Relationships Among Variables.
3.1 Introduction.
3.2 Relationships Among Categorical Variables.
3.3 Relationships Among Categorical Variables and a Numerical Variable.
3.4 Relationships Among Numerical Variables.
3.5 Pivot Tables.
3.6 An Extended Example.
3.7 Conclusion.
PART II: PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY
4. Probability and Probability Distributions.
4.1. Introduction.
4.2. Probability Essentials.
4.3. Distribution of a Single Random Variable.
4.4. An Introduction to Simulation.
4.5. Distribution of Two Random Variables: Scenario Approach.
4.6. Distribution of Two Random Variables: Joint Probability Approach.
4.7. Independent Random Variables.
4.8. Weighted Sums of Random Variables.
4.9. Conclusion.
5. Normal, Binomial, Poisson, and Exponential Distributions.
5.1. Introduction.
5.2. The Normal Distribution.
5.3. Applications of the Normal Distribution.
5.4. The Binomial Distribution.
5.5. Applications of the Binomial Distribution.
5.6. The Poisson and Exponential Distributions.
5.7. Fitting a Probability Distribution to Data with @RISK.
5.8. Conclusion.
6. Decision Making Under Uncertainty.
6.1. Introduction.
6.2. Elements of a Decision Analysis.
6.3. The PrecisionTree Add-In.
6.4. Bayes Rule.
6.5. Multistage Decision Problems.
6.6. Incorporating Attitudes Toward Risk.
6.7. Conclusion.
PART III: STATISTICAL INFERENCE.
7. Sampling and Sampling Distributions.
7.1. Introduction.
7.2. Sampling Terminology.
7.3. Methods for Selecting Random Samples.
7.4. An Introduction to Estimation.
7.5. Conclusion.
8. Confidence Interval Estimation.
8.1. Introduction.
8.2. Sampling Distributions.
8.3. Confidence Interval for a Mean.
8.4. Confidence Interval for a Total.
8.5. Confidence Interval for a Proportion.
8.6. Confidence Interval for a Standard Deviation.
8.7. Confidence Interval for the Difference Between Means.
8.8. Confidence Interval for the Difference Between Proportions.
8.9. Controlling Confidence Interval Length.
8.10. Conclusion.
9. Hypothesis Testing.
9.1. Introduction.
9.2. Concepts in Hypothesis Testing.
9.3. Hypothesis Tests for a Population Mean.
9.4. Hypothesis Tests for Other Parameters.
9.5. Tests for Normality.
9.6. Chi-Square Test for Independence.
9.7. One-Way ANOVA.
9.8. Conclusion.
PART IV: REGRESSION ANALYSIS AND TIME SERIES FORECASTING.
10. Regression Analysis: Estimating Relationships.
10.1. Introduction.
10.2. Scatterplots: Graphing Relationships.
10.3. Correlations: Indicators of Linear Relationships.
10....

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