Data Analysis and Decision Making

Name: Data Analysis and Decision Making
Availability: OutOfStock
ISBN: 9780534391706

ISBN-10: 0534391702

ISBN-13: 9780534391706

Edition: 2nd 2003

Authors: S. Christian Albright, Wayne Winston, Christopher Zappe

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Description:

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.

Book details

Edition: 2nd
Copyright year: 2003
Publisher: Cengage Learning
Binding: Hardcover
Pages: 1032
Weight: 4.532
Language: English

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. His current interest is in spreadsheet modeling, including development of VBA applications in Excel. Dr. Albright has published more than 20 articles in leading operations research journals in the area of applied probability as well as a number of…

Wayne L. Winston is a professor of Decision Sciences at Indiana University's Kelley School of Business and the recipient of more than 30 teaching awards. For the past 20 years, Wayne has also taught Fortune 500 companies how to use Excel to make smarter business decisions. He has written 15 books on Excel, management science, and mathematics in sports.




Introduction to Data Analysis and Decision Making


Introduction


An Overview of the Book


The Methods


A Sampling of Examples


Modeling and Models


Conclusion



Getting, Describing, and Summarizing Data



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



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



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



Probability, Uncertainty, and Decision Making



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



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



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



Statistical Inference



Sampling and Sampling Distributions


Introduction


Sampling Terminology


Methods for Selecting Random Samples


An Introduction to Estimation


Conclusion



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



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



Regression, Forecasting, and Time Series



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



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



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



Decision Modeling



Introduction to Optimization Modeling


Introduction


A Brief History of Linear Programming


Introduction to LP Modeling


Sensitivity Analysis and the SolverTable Add-In


The Linear Assumptions


Graphical Solution Method


Infeasibility and Unboundedness


A Multiperiod Production Problem


A Decision Support System


Conclusion



Optimization Modeling: Applications


Introduction


Workforce Scheduling Models


Blending Models


Logistics Models


Aggregate Planning Models


Dynamic Financial Models


Integer Programming Models


Nonlinear Models


Conclusion



Simulation Models


Introduction


Random Numbers


Introduction to Spreadsheet Simulation


Selecting Probability Distributions


Simulating with @Risk


Financial Planning Models


Cash Balance Models


Simulating Stock Prices and Options


Market Share Models


Simulating Correlated Values


Using TopRank with @Risk for Powerful Modeling


Conclusion