Data Analysis for Managers with Microsoft Excel

1. Introduction to Data Analysis for Managers. Introduction. An Overview of the Book. Excel versus Standalone Statistical Software. A Sampling of Examples. Conclusion. Part I: GETTING, DESCRIBING, AND SUMMARIZING DATA. 2. Describing Data: Graphs and Tables. Introduction. Basic Concepts. Frequency Tables and Histograms. Analyzing Relationships with Scatterplots. Time Series Plots. Exploring Data with Pivot Tables. Conclusion. 3. Describing Data: Summary Measures. Introduction. Measures of Central Location. Quartiles and Percentiles. Minimum, Maximum, and Range. Measures of Variability: Variance and Standard Deviation. Obtaining Summary Measures with Add-Ins. Measures of Association: Covariance and Correlation. Describing Data Sets with Boxplots. Applying the Tools. Conclusion. 4. Getting the Right Data. Introduction. Sources of Data. Using Excel's AutoFilter. Complex Queries with the Advanced Filter. Importing External Data from Access. Creating Pivot Tables from External Data. Web Queries. Other Data Sources 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 Random Variable. An Introduction to Simulation. Distribution of Two Random Variables: Scenario Approach. Distribution of Two Random Variables: Joint Probability Approach. Independent Random Variables. Weighted Sums of Random Variables. Conclusion. 6. Normal, Binomial, Poisson, and Exponential Distributions. Introduction. The Normal Distribution. Applications of the Normal Distribution. The Binomial Distribution. Applications of the Binomial Distribution. The Poisson and Exponential Distributions. Fitting a Probability Distribution to Data: BestFit. Conclusion. 7. Decision Making Under Uncertainty. Introduction. Elements of a Decision Analysis. The PrecisionTree Add-In. More Single-Stage Examples. Multistage Decision Problems. Bayes' Rule. Incorporating Attitudes Toward Risk. Conclusion. Part III: STATISTICAL INFERENCE. 8. Sampling and Sampling Distributions. Introduction. Sampling Terminology. Methods for Selecting Random Samples. 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 a Proportion. Confidence Interval for a Standard Deviation. Confidence Interval for the Difference between Means. Confidence Interval for the Difference between Proportions. Controlling Confidence Interval Length. Conclusion. 10. Hypothesis Testing. Introduction. Concepts in Hypothesis Testing. Hypothesis Tests for a Population Mean. Hypothesis Tests for Other Parameters. Tests for Normality. Chi-Square Test for Independence. One-Way ANOVA. 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. Multiple Regression. Modeling Possibilities. Validation of the Fit. Conclusion. 12. Regression Analysis: Statistical Inference. Introduction. The Statistical Model. Inferences about the Regression Coefficients. Multicollinearity. Include/Exclude Decisions. Stepwise Regression. The Partial F Test. Outliers. Violations of Regression Assumptions. Prediction. Conclusion. 13. Time Series Analysis and Forecasting. Introduction. Forecasting Methods: An Overview. Testing for Randomness. Regression-Based Trend Models. The Random Walk Model. Autoregression Models. Moving Averages. Exponential Smoothing. Seasonal Models. Conclusion. Part V: OTHER STATISTICAL TOOLS. 14. Analysis of Variance and Experimental Design. Introduction. One-Way ANOVA. Using Regression to Perform ANOVA. The Multiple Comparison Problem. Two-Way ANOVA. More About Experimental Design. Conclusion. 15. Data Mining Techniques: Discriminant Analysis, Logistic Regression, and OLAP. Introduction. Discriminant Analysis. Logistic Regression. Online Analytical Processing (OLAP). Conclusion. 16. Statistical Process Control. Introduction. Deming's 14 Points. Basic Ideas Behind Control Charts. Control Charts for Variables. Control Charts for Attributes. Process Capability. Conclusion. Appendix A: Statistical Reporting. Index.