Summary: 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 additio...show morenal cost with every new text. New to the Edition
Updated CD-ROM is packaged with every new textbook. The CD-ROM includes the most recent version of StatPro, the professional add-in for Microsoft Excel containing 36 wide-ranging statistical procedures and 5 built-in data utilities covers the most widely used statistical analyses. These add-ins allow students to perform every analysis in the book using Excel.
The authors place greater emphasis on presenting and summarizing key concepts throughout the text. New features include margin notes, boxed definitions and formulas in the text, key terms and formulas at the end of every chapter, and enhanced explanations in the text.
More conceptual exercises appear within each chapter of the text. These exercises either test concepts or ask more open-ended questions--as opposed to the many number-crunching problems already in the text.
Objectives have been added to each example to clearly state the purpose of the example and show which statistical concepts are being employed.
The data for many of the problems have been updated to be as timely as possible. This is especially true of the time series data.
Chapter 8, "Sampling and Sampling Distributions," has been significantly reorganized, increasing the strength and logic of the conceptual development.
Chapter 13, "Time Series Analysis and Forecasting," has been entirely reorganized. It includes an introductory section on the various components of time series (level, trend, seasonality, and noise), and places all the techniques for handling seasonality in a single section.
Chapter 15, "Data Mining: Discriminant Analysis, Logistic Regression and OLAP," contains a new section on OLAP (online analytical processing) that shows how this powerful technique can be implemented in Excel.
Concepts presented in the context of examples enable students to see why they are conducting an analysis and what it means. Software is used to create graphical and numerical outputs, eliminating the need for hand calculation, and freeing students for in-depth interpretation of output.
Excel instructions and output appear throughout. The instructions are compatible with Excel XP, 2000, and 97.
Practical coverage throughout including pivot tables, data sources and manipulation in chapter 4, and data mining topics (logistic regression, discriminant analysis, and OLAP) in chapter 15.
Solid instructor resources, anchored by the Instructor's Suite CD, provide electronic solutions in Excel format, test items in Microsoft Word format, and Microsoft PowerPoint slides.
A large number of realistic exercises and end-of-chapter cases provide opportunities for learners to reinforce and extend the knowledge that they have gained in working with this text.
Material on writing professional business reports based on statistical analyses is included in Appendix A.
Chris 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 20 articles in leading operations research journals in the area of applied probability, and he has authored other successful Duxbury titles including PRACTICAL MANAGEMENT SCIENCE, Second Edition, VBA FOR MODELERS, and DATA ANALYSIS AND DECISION MAKING, Second Edition. His current interest is in spreadsheet modeling, including development of VBA applications in Excel.
Winston, Wayne L. : Indiana University
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, PRACTICAL MANAGEMENT SCIENCE, 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 solve business problems in all disciplines, particularly in finance and marketing.
Zappe, Christopher : Bucknell University
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 AND DECISION MAKING. His current scholarly interests focus on mathematical programming models of performance appraisal processes and innovative pedagogies in operations research/management science.
View Table of Contents
1. Introduction to Data Analysis for Managers.
Introduction. An Overview of the Book. Excel versus Standalone Statistical Softw are. 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 Relatio nships with Scatterplots. Time Series Plots. Exploring Data with Pivot Tables. C onclusion.
3. Describing Data: Summary Measures.
Introduction. Measures of Central Location. Quartiles and Percentiles. Minimum, Maximum, and Range. Measures of Variability: Variance and Standard Deviation. Ob taining Summary Measures with Add-Ins. Measures of Association: Covariance and C orrelation. 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 th e Advanced Filter. Importing External Data from Access. Creating Pivot Tables fr om 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 Ap proach. Distribution of Two Random Variables: Joint Probability Approach. Indepe ndent 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 Poisso n and Exponential Distributions. Fitting a Probability Distribution to Data: Bes tFit. Conclusion.
7. Decision Making Under Uncertainty.
Introduction. Elements of a Decision Analysis. The PrecisionTree Add-In. More Si ngle-Stage Examples. Multistage Decision Problems. Bayes' Rule. Incorporating At titudes Toward Risk. Conclusion.
Part III: STATISTICAL INFERENCE. 8. Sampling and Sampling Distributions.
Introduction. Sampling Terminology. Methods for Selecting Random Samples. An Int roduction 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 Confide nce 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 Tes t 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 Po ssibilities. Validation of the Fit. Conclusion.
12. Regression Analysis: Statistical Inference.
Introduction. The Statistical Model. Inferences about the Regression Coefficient s. Multicollinearity. Include/Exclude Decisions. Stepwise Regression. The Partia l F Test. Outliers. Violations of Regression Assumptions. Prediction. Conclusion .
13. Time Series Analysis and Forecasting.
Introduction. Forecasting Methods: An Overview. Testing for Randomness. Regressi on-Based Trend Models. The Random Walk Model. Autoregression Models. Moving Aver ages. 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 Com parison Problem. Two-Way ANOVA. More About Experimental Design. Conclusion.
15. Data Mining Techniques: Discriminant Analysis, Logistic Regression, and OLAP .
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