Data Analysis and Decision Making (with

Data Analysis and Decision Making is a teach-by-example approach, learner-friendly writing style, and complete Excel integration focusing on data analysis, modeling, and spreadsheet use in statistics and management science.

The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis. All of the add-ins is revised for Excel 2007 and notes about Excel 2010 are added where applicable.

Review

"I want to commend you and your colleagues on Data Analysis and Decision Making. That textbook is one of the most well organized, well written and useful books I have used in either undergraduate or graduate studies. Any questionI had while going through the course was easy to find and well answered. The use of Excel and Palisades tools was excellent. Thank you for taking the time and effort to publish such a great reference. It will surely be on my bookshelf for many years."

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
Table of Contents provided by Publisher. All Rights Reserved.

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Summary

Table of Contents

Supplemental Materials