Data Analysis for Managers with Microsoft Excel with CD-ROM and InfoTrac / Edition 2

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Overview

This text presents statistical concepts and methods in a unified, modern, spreadsheet-oriented approach. Featuring a wealth of business applications, this examples-based text illustrates a variety of statistical methods to help students analyze data sets and uncover important information to aid decision-making. DATA ANALYSIS FOR MANAGERS contains professional StatPro add-ins for Microsoft Excel from Palisade, valued at one hundred fifty dollars packaged at no additional cost with every new text.

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Product Details

  • ISBN-13: 9780534383664
  • Publisher: Cengage Learning
  • Publication date: 4/1/2003
  • Edition description: REV
  • Edition number: 2
  • Pages: 984
  • Product dimensions: 8.18 (w) x 10.36 (h) x 1.36 (d)

Meet the Author

S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. He has published over twenty articles in leading operations research journals in the area of applied probability, and he has authored other successful South-Western titles, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, PRACTICAL MANAGEMENT SCIENCE, and SPREADSHEET MODELING AND APPLICATIONS. His current interest is in spreadsheet modeling, including development of VBA applications in Excel®.

Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 1975. Wayne received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, AND FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. His current interest is in showing how spreadsheet models can be used to solvebusiness problems in all disciplines, particularly in finance and marketing.

Chris Zappe earned his BA in mathematics from DePauw University in 1983 and his MBA and Ph.D. in Decision Sciences from Indiana University in 1987 and 1988, respectively. Between 1988 and 1993, he performed research and taught various courses in the decision sciences area at the University of Florida in the College of Business Administration. Since 1993, Chris has been serving as an associate professor of decision sciences in the Department of Management at Bucknell University. He currently teaches undergraduate courses in business statistics, decision analysis, and computer simulation. Moreover, Chris teaches advanced seminars in applied game theory, system dynamics, risk assessment, and mathematical economics. He has published articles in various journals including Managerial and Decision Economics, OMEGA, Naval Research Logistics, and Interfaces, and is co-author of DATA ANALYSIS FOR MANAGERS. His current scholarly interests focus on mathematical programming models of performance appraisal processes and innovative pedagogies in operations research/management science.

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Table of Contents

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.

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