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Data Analysis and Decision Making

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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.
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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