Data Analysis and Decision Making

ISBN-10: 0538476125
ISBN-13: 9780538476126
Edition: 4th 2011
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Book details

List price: $333.95
Edition: 4th
Copyright year: 2011
Publisher: Cengage South-Western
Publication date: 10/12/2010
Binding: Hardcover
Pages: 1080
Size: 8.25" wide x 10.00" long x 1.75" tall
Weight: 4.752
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 successful textbooks, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, and SPREADSHEET MODELING AND APPLICATIONS.

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.

Preface
Introduction to Data Analysis and Decision Making
Introduction
An Overview of the Book
Modeling and Models
Conclusion
Exploring Data
Describing the Distribution of a Single Variable
Introduction
Basic Concepts
Descriptive Measures for Categorical Variables
Descriptive Measures for Numerical Variables
Time Series Data
Outliers and Missing Values
Excel Tables for Filtering, Sorting, and Summarizing
Conclusion
Finding Relationships Among Variables
Introduction
Relationships Among Categorical Variables
Relationships Among Categorical Variables and a Numerical Variable
Relationships Among Numerical Variables
Pivot Tables
An Extended Example
Conclusion
Probability and Decision Making Under Uncertainty
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 with @Risk
Conclusion
Decision Making Under Uncertainty
Introduction
Elements of a Decision Analysis
The Precision Tree Add-In
Bayes' Rule
Multistage Decision Problems
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 Analysis and Time Series Forecasting
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
Optimization and Simulation Modeling
Introduction to Optimization Modeling
Introduction
Introduction to Optimization
A Two-Variable Product Mix Model
Sensitivity Analysis
Properties of Linear Models
Infeasibility and Unboundedness
A Larger Product Mix Model
A Multiperiod Production Model
A Comparison of Algebraic and Spreadsheet Models
A Decision Support System
Conclusion
Optimization Models
Introduction
Worker Scheduling Models
Blending Models
Logistics Models
Aggregate Planning Models
Financial Models
Integer Programming Models
Nonlinear Programming Models
Conclusion
Introduction to Simulation Modeling
Introduction
Probability Distributions for Input Variables
Simulation and the Flaw of Averages
Simulation with Built-In Excel Tools
Introduction to the @Risk Add-in
The Effects of Input Distributions on Results
Conclusion
Simulation Models
Introduction
Operations Models
Financial Models
Marketing Models
Simulating Games of Chance
An Automated Template for @Risk Models
Conclusion
Bonus Online Material 2
Using the Advanced Filter and Database Functions
Importing Data into Excel
Introduction
Rearranging Excel Data
Importing Text Data
Importing Relational Database Data
Web Queries
Cleansing the Data
Conclusion

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