Data analysis, optimization, and simulation modeling

DATA ANALYSIS, OPTIMIZATION, AND SIMULATION MODELING, 4e, International Edition 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.

Preface

1 Introduction to Data Analysis and Decision Making

1.1 Introduction

1.2 An Overview of the Book

1.3 Modeling and Models

1.4 Conclusion

Part I Exploring Data

2 Describing the Distribution of a Single Variable

2.1 Introduction

2.2 Basic Concepts

2.3 Descriptive Measures for Categorical Variables

2.4 Descriptive Measures for Numerical Variables

2.5 Time Series Data

2.6 Outliers and Missing Values

2.7 Excel Tables for Filtering, Sorting, and Summarizing

2.8 Conclusion

3 Finding Relationships Among Variables

3.1 Introduction

3.2 Relationships Among Categorical Variables

3.3 Relationships Among Categorical Variables and a Numerical Variable

3.4 Relationships Among Numerical Variables

3.5 Pivot Tables

3.6 An Extended Example

3.7 Conclusion

Part II Probability and Decision Making Under Uncertainty

4 Probability and Probability Distributions

4.1 Introduction

4.2 Probability Essentials

4.3 Distribution of a Single Random Variable

4.4 An Introduction to Simulation

4.5 Distribution of Two Random Variables: Scenario Approach

4.6 Distribution of Two Random Variables: Joint Probability Approach

4.7 Independent Random Variables

4.8 Weighted Sums of Random Variables

4.9 Conclusion

5 Normal, Binomial, Poisson, and Exponential Distributions

5.1 Introduction

5.2 The Normal Distribution

5.3 Applications of the Normal Distribution

5.4 The Binomial Distribution

5.5 Applications of the Binomial Distribution

5.6 The Poisson and Exponential Distributions

5.7 Fitting a Probability Distribution to Data with @RISK

5.8 Conclusion

6 Decision Making Under Uncertainty

6.1 Introduction

6.2 Elements of a Decision Analysis

6.3 The PrecisionTree Add-In

6.4 BayesÆ Rule

6.5 Multistage Decision Problems

6.6 Incorporating Attitudes Toward Risk

6.7 Conclusion

Part III Statistical Inference

7 Sampling and Sampling Distributions

7.1 Introduction

7.2 Sampling Terminology

7.3 Methods for Selecting Random Samples

7.4 An Introduction to Estimation

7.5 Conclusion

8 Confidence Interval Estimation

8.1 Introduction

8.2 Sampling Distributions

8.3 Confidence Interval for a Mean

8.4 Confidence Interval for a Total

8.5 Confidence Interval for a Proportion

8.6 Confidence Interval for a Standard Deviation

8.7 Confidence Interval for the Difference Between Means

8.8 Confidence Interval for the Difference Between Proportions

8.9 Controlling Confidence Interval Length

8.10 Conclusion

9 Hypothesis Testing

9.1 Introduction

9.2 Concepts in Hypothesis Testing

9.3 Hypothesis Tests for a Population Mean

9.4 Hypothesis Tests for Other Parameters

9.5 Tests for Normality

9.6 Chi-Square Test for Independence

9.7 One-Way ANOVA

9.8 Conclusion

Part IV Regression Analysis and Time Series Forecasting

10 Regression Analysis: Estimating Relationships

10.1 Introduction

10.2 Scatterplots: Graphing Relationships

10.3 Correlations: Indicators of Linear Relationships

10.4 Simple Linear Regression

10.5 Multiple Regression

10.6 Modeling Possibilities

10.7 Validation of the Fit

10.8 Conclusion

11 Regression Analysis: Statistical Inference

11.1 Introduction

11.2 The Statistical Model

11.3 Inferences About the Regression Coefficients

11.4 Multicollinearity

11.5 Include/Exclude Decisions

11.6 Stepwise Regression

11.7 The Partial F Test

11.8 Outliers

11.9 Violations of Regression Assumptions

11.10 Prediction

11.11 Conclusion

12 Time Series Analysis and Forecasting

12.1 Introduction

12.2 Forecasting Methods: An Overview

12.3 Testing for Randomness

12.4 Regression-Based Trend Models

12.5 The Random Walk Model

12.6 Autoregression Models

12.7 Moving Averages

12.8 Exponential Smoothing

12.9 Seasonal Models

12.10 Conclusion

Part V Optimization and Simulation Modeling

13 Introduction to Optimization Modeling

13.1 Introduction

13.2 Introduction to Optimization

13.3 A Two-Variable Product Mix Model

13.4 Sensitivity Analysis

13.5 Properties of Linear Models

13.6 Infeasibility and Unboundedness

13.7 A Larger Product Mix Model

13.8 A Multiperiod Production Model

13.9 A Comparison of Algebraic and Spreadsheet Models

13.10 A Decision Support System

13.11 Conclusion

14 Optimization Models

14.1 Introduction

14.2 Worker Scheduling Models

14.3 Blending Models

14.4 Logistics Models

14.5 Aggregate Planning Models

14.6 Financial Models

14.7 Integer Programming Models

14.8 Nonlinear Programming Models

14.9 Conclusion

15 Introduction to Simulation Modeling

15.1 Introduction

15.2 Probability Distributions for Input Variables

15.3 Simulation and the Flaw of Averages

15.4 Simulation with Built-In Excel Tools

15.5 Introduction to the @RISK Add-in

15.6 The Effects of Input Distributions on Results

15.7 Conclusion

16 Simulation Models

16.1 Introduction

16.2 Operations Models

16.3 Financial Models

16.4 Marketing Models

16.5 Simulating Games of Chance

16.6 An Automated Template for @RISK Models

16.7 Conclusion

Part VI Bonus Online Material

2 Using the Advanced Filter and Database Functions

17 Importing Data into Excel

17.1 Introduction

17.2 Rearranging Excel Data

17.3 Importing Text Data

17.4 Importing Relational Database Data

17.5 Web Queries

17.6 Cleansing the Data

17.7 Conclusion

On Order

Summary

Summary

Table of Contents