The emphasis of the text is on data analysis, modeling, and spreadsheet use in statistics and management science. This text contains professional Excel software add-ins. The authors maintain the elements that have made this text a market leader in its first edition: clarity of writing, a teach-by-example approach, and complete Excel integration.
About The Author
S. Christian Albright
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.
Wayne Winston
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, PRATICAL 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.
Christopher Zappe
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.
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Table of Contents
Part I: GETTING, DESCRIBING, AND SUMMARIZING DATA.
Introduction to Data Analysis and Decision Making.
1. Introduction. An Overview of the Book. The Methods. The Software. 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 and Percentiles. Minimum, Maximum, and Range. Measures of Variability: Variance and Standard Deviation. Obtaining Summary Measures with StatTools. 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. Bayes' Rule. Multistage Decision Problems. 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. Winters' Exponential Smoothing Model. Conclusion.
Part V: Decision Modeling.
14. Introduction to Optimization Modeling.
Introduction. Introduction to Optimization. A Two-Variable Model. Sensitivity Analysis Properties of Linear Models. Infeasibility and Unboundedness. A Product Mix Model. A Multiperiod Production Model. A Comparison of Algebraic and Spreadsheet Models. A Decision Support System. Conclusion.
15. Optimization Modeling: Applications.
Introduction. Workforce Scheduling Models. Blending Models. Logistics Models. Aggregate Planning Models. Financial Models. Integer Programming Models. Nonlinear Models. Conclusion.
16.Introduction to Simulation Modeling.
Introduction. Real Applications of Simulation. Probability Distributions for Input Variables. Simulation with Built-In Excel Tools. Introduction to @RISK. The Effects of Input Distributions on Results. Conclusion.
17.Simulation Models.
Introduction. Operations Models. Financial Models. Marketing Models. Simulating Games of Chance. Conclusion.
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New to the Edition
Updated CD-ROM packaged with every new copy of the textbook. The most recent versions of StatTools, @Risk, Precision Tree, Best Fit, RISKView, Top Rank, and Solver Table along with data files, and appendices are included on the CD-ROM.
New pedagogical features explain concepts even more clearly at the outset, especially in the statistics chapters. New features include: margin notes, boxed-in definitions (and formulas) in the text, enhanced explanations in the text itself, and stated objectives for the examples.
In this edition 142 problems have been revised with more current data to incoporate 'real world' learning. These problems, based on newer data, give this edition an enhanced utility to the students and instructors.
New content is featured for selected topics such as Decision Making Under Uncertainty, Optimization and Simulation Modeling. Expanding these concepts allow students to gain a broader understanding of these important topics.
The data for many of the problems have been updated to be as timely as possible. This is specifically true for time series data.
The conceptual exercises at the end of each chapter wither test the concepts or ask more open-ended questions - as opposed to the many number-crunching problems already in the book.
Chapter 17, Simulation Models, is a new chapter in this edition. The models included in this chapter are operation, financial and marketing models. Chapters 16 & 17 expand the simulation coverage form a single chapter to two chapters.
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Features
The authors employ a teach-by-example approach with this text. There are deep applications in each functional area of business. The examples are all titled with a stated objective. They enable students to see the relevance of the material to their futures as business leaders.
Extensive use of spreadsheet technology. The text uses Excel extensively throughout, including screen shots and step-by-step instructions for performing analyses using Excel.
Practical coverage of relevant topics and useful technology, such as Pivot Tables, and Getting the Right Data.
A list of key terms and formulas appears at the end of each chapter to make it easier for students to study and for instructors to teach.
Covers both statistics and core topics of management science in one book. There is no longer a need to customize from other texts-this text covers it all in one volume!
Problem sets and cases are exceptional. They challenge students by providing realistic scenarios and ask them to evaluate and analyze data as if they were a manager making the decision.
