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by S. Christian Albright, Wayne L. Winston and Christopher Zappe

Cover type: HardbackEdition: 99

Copyright: 1999

Publisher: Duxbury Press

Published: 1999

International: No

List price: $325.00

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In response to the growing market trend in quantitative education, Albright, Winston, and Zappe's integrated business-statistics and management-science text presents core statistics and management-science methods in a modern, unified spreadsheet-oriented approach. With a focus on analyzing, not on techniques, the book covers business statistics with some essential managerial-science topics included. The example-based, Excel spreadsheet approach is useful in courses that combine traditional statistics and management-science topics though can be easily used for a one-term business statistics only course. The modeling and application focus, together with the Excel spreadsheet add-ins, provides a complete learning source for both students and practicing managers.

- Outstanding commercial Excel software add-ins are included for statistics, decision analysis, and simulation. Includes Palisade Corporations' Decision Tools Suite (@Risk, Precision Tree, Best Fit, DataPro {for Statistics}, and SolverTable).
- Over 800 real data-based exercises and case studies are included.
- More than 300 practical examples are included, from finance, marketing, and operations.
- The book offers comprehensive coverage for two major subjects (business statistics and management science) in a unified manner. The text-features an example-based spreadsheet approach to data analysis and modeling, a unique and timely change for many instructors.
- Spreadsheet simulations are used to demonstrate statistical concepts visually throughout the book.
- A pragmatic approach to data analysis is used, with emphasis on only those aspects that will help make students more valuable employees. Each chapter features a decision-making framework with examples designed to help students learn to analyze data with an eye toward effective decision-making as the end result.
- The book shows students how to translate real business problems-expressed in words-into spreadsheets that list the inputs and decision variables. The authors then relate these to outputs by means of appropriate formulas.

**PREFACE. 1. INTRODUCTION TO DATA ANALYSIS & DECISION MAKING.**

Introduction.

An Overview of the Book.

A Sampling of Examples.

Modeling and Models.

Conclusion.

Case Study: Entertainment on a Cruise Ship.

**2. 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.

Case Study: Customer Arrivals at Bank98.

Case Study: Automobile Production and Purchases.

Case Study: Saving, Spending, and Social Climbing.

**3. 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. Case Study: The Dow Jones Averages.

Case Study: Other Market Indexes.

**4. PROBABILITY AND PROBABILITY DISTRIBUTIONS. **

Introduction.

Probability Essentials.

Distribution of a Single Random Variable.

An Introduction to Simulation.

Subjective Versus Objective Probabilities.

Derived Probability Distributions.

Distribution of Two Random Variables: Scenario Approach.

Distribution of Two Random Variables: Joint Probability Approach.

Independent Random Variables.

Weighted Sums of Random Variables.

Conclusion. Case Study: Simpson's Paradox.

**5. NORMAL, BINOMIAL, AND POISSON DISTRIBUTIONS. **

Introduction.

The Normal Distribution.

Applications of the Normal Distribution.

The Binomial Distribution.

Applications of the Binomial Distribution.

The Poisson Distribution.

Fitting a Probability Distribution to Data: BestFit.

Conclusion.

Case Study: EuroWatch Company.

Case Study: Cashing in on the Lottery.

**6. DECISION MAKING UNDER UNCERTAINTY. **

Introduction.

Elements of a Decision Analysis.

The PrecisionTree Add-In.

Introduction to Influence Diagrams.

More Single-Stage Examples.

Multistage Decision Problems.

Bayes' Rule.

Incorporating Attitudes Toward Risk.

Conclusion.

Case Study: Jogger Shoe Company.

**7. SAMPLING AND SAMPLING DISTRIBUTIONS. **

Introduction.

Sampling Terminology.

Methods for Selecting Random Samples.

An Introduction to Estimation. Conclusion.

Case Study: Sampling from Videocassette Renters.

**8. 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 a Difference Between Means.

Confidence Interval for the Difference Between Proportions.

Controlling Confidence Interval Length.

Conclusion.

Case Study: Harrigan University Admissions.

Case Study: Employee Retention at D & Y. Delivery Times at SnowPea Restaurant.

Case Study: The Bodfish Lot Cruise.

**9. HYPOTHESIS TESTING. **

Introduction.

Concepts in Hypothesis Testing.

Hypothesis Tests for a Population Mean.

Hypothesis Tests for Other Parameters.

One-Way ANOVA.

Tests for Normality.

Conclusion.

Case Study: Regression Toward the Mean.

Case Study: Baseball Statistics.

Case Study: The Wichita Anti-Drunk Driving Advertising Campaign.

**10. STATISTICAL PROCESS CONTROL. **

Introduction.

Deming's 14 Points.

Basic Ideas Behind Control Charts.

Control Charts for Variables.

Control Charts for Attributes.

Process Capability.

Conclusion.

Case Study: The Lamination Process at Intergalactica.

Case Study: Paper Production for Fornax at the Pluto Mill.

**11. 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.

Case Study: Quantity Discounts at the FirmChair Company.

Case Study: Housing Price Structure in MidCity.

Case Study: Demand for French Bread at Howie's.

Case Study: Investing for Retirement.

**12. REGRESSION ANALYSIS: STATISTICAL INFERENCE. **

Introduction.

The Statistical Model.

Inferences About the Regression Coefficients.

Multicollinearity.

Include/Exclude Decisions.

Stepwise Regression.

A Test for the Overall Fit: The ANOVA Table.

The Partial F Test.

Outliers.

Violations of Regression Assumptions.

Prediction.

Conclusion.

Case Study: The Artsy Corporation.

Case Study: Heating Oil at Dupree Fuels Company.

Case Study: Developing a Flexible Budget at the Gunderson Plant.

Case Study: Forecasting Overhead at Wagner Printers.

**13. TIME SERIES ANALYSIS AND FORECASTING. **

Introduction.

Forecasting Methods: An Overview.

Random Series.

The Random Walk Model.

Autoregression Models.

Regression-Based Trend Models.

Moving Averages.

Exponential Smoothing.

Deseasonalizing: The Ratio-to-Moving-Averages Method.

Estimating Seasonality with Regression.

Econometric Models.

Conclusion.

Case Study: Arrivals at the Credit Union.

Case Study: Forecasting Weekly Sales at Amanta.

**14. 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.

Case Study: Shelby Shelving.

**15. OPTIMIZATION MODELING: APPLICATIONS. **

Introduction.

Static Workforce Scheduling.

Blending Models.

Logistics Models.

Aggregate Planning Models.

A Dynamic Financial Model.

Integer Programming Models.

Nonlinear Models.

Conclusion.

Case Study: Giant Motor Company.

Case Study: GMS Stock Hedging.

Case Study: Durham Asset Management.

**16. SIMULATION MODELS. **

Introduction.

Random Numbers.

Introduction to Spreadsheet Simulation.

Simulating from Other Probability Distributions.

Simulating with @Risk.

A Financial Planning Model.

A Cash Balance Model.

Simulating Stock Prices and Options.

A Market Share Model.

Simulating Correlated Values.

Using TopRank with @Risk for Powerful Modeling.

Conclusion.

Case Study: Ski Jacket Production.

Case Study: The College Fund Investment Decision.

Case Study: Ebony Bath Soap.

Case Study: Bond Investment Strategy.

REFERENCES.

INDEX.

Summary

In response to the growing market trend in quantitative education, Albright, Winston, and Zappe's integrated business-statistics and management-science text presents core statistics and management-science methods in a modern, unified spreadsheet-oriented approach. With a focus on analyzing, not on techniques, the book covers business statistics with some essential managerial-science topics included. The example-based, Excel spreadsheet approach is useful in courses that combine traditional statistics and management-science topics though can be easily used for a one-term business statistics only course. The modeling and application focus, together with the Excel spreadsheet add-ins, provides a complete learning source for both students and practicing managers.

- Outstanding commercial Excel software add-ins are included for statistics, decision analysis, and simulation. Includes Palisade Corporations' Decision Tools Suite (@Risk, Precision Tree, Best Fit, DataPro {for Statistics}, and SolverTable).
- Over 800 real data-based exercises and case studies are included.
- More than 300 practical examples are included, from finance, marketing, and operations.
- The book offers comprehensive coverage for two major subjects (business statistics and management science) in a unified manner. The text-features an example-based spreadsheet approach to data analysis and modeling, a unique and timely change for many instructors.
- Spreadsheet simulations are used to demonstrate statistical concepts visually throughout the book.
- A pragmatic approach to data analysis is used, with emphasis on only those aspects that will help make students more valuable employees. Each chapter features a decision-making framework with examples designed to help students learn to analyze data with an eye toward effective decision-making as the end result.
- The book shows students how to translate real business problems-expressed in words-into spreadsheets that list the inputs and decision variables. The authors then relate these to outputs by means of appropriate formulas.

Table of Contents

**PREFACE. 1. INTRODUCTION TO DATA ANALYSIS & DECISION MAKING.**

Introduction.

An Overview of the Book.

A Sampling of Examples.

Modeling and Models.

Conclusion.

Case Study: Entertainment on a Cruise Ship.

**2. 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.

Case Study: Customer Arrivals at Bank98.

Case Study: Automobile Production and Purchases.

Case Study: Saving, Spending, and Social Climbing.

**3. 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. Case Study: The Dow Jones Averages.

Case Study: Other Market Indexes.

**4. PROBABILITY AND PROBABILITY DISTRIBUTIONS. **

Introduction.

Probability Essentials.

Distribution of a Single Random Variable.

An Introduction to Simulation.

Subjective Versus Objective Probabilities.

Derived Probability Distributions.

Distribution of Two Random Variables: Scenario Approach.

Distribution of Two Random Variables: Joint Probability Approach.

Independent Random Variables.

Weighted Sums of Random Variables.

Conclusion. Case Study: Simpson's Paradox.

**5. NORMAL, BINOMIAL, AND POISSON DISTRIBUTIONS. **

Introduction.

The Normal Distribution.

Applications of the Normal Distribution.

The Binomial Distribution.

Applications of the Binomial Distribution.

The Poisson Distribution.

Fitting a Probability Distribution to Data: BestFit.

Conclusion.

Case Study: EuroWatch Company.

Case Study: Cashing in on the Lottery.

**6. DECISION MAKING UNDER UNCERTAINTY. **

Introduction.

Elements of a Decision Analysis.

The PrecisionTree Add-In.

Introduction to Influence Diagrams.

More Single-Stage Examples.

Multistage Decision Problems.

Bayes' Rule.

Incorporating Attitudes Toward Risk.

Conclusion.

Case Study: Jogger Shoe Company.

**7. SAMPLING AND SAMPLING DISTRIBUTIONS. **

Introduction.

Sampling Terminology.

Methods for Selecting Random Samples.

An Introduction to Estimation. Conclusion.

Case Study: Sampling from Videocassette Renters.

**8. 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 a Difference Between Means.

Confidence Interval for the Difference Between Proportions.

Controlling Confidence Interval Length.

Conclusion.

Case Study: Harrigan University Admissions.

Case Study: Employee Retention at D & Y. Delivery Times at SnowPea Restaurant.

Case Study: The Bodfish Lot Cruise.

**9. HYPOTHESIS TESTING. **

Introduction.

Concepts in Hypothesis Testing.

Hypothesis Tests for a Population Mean.

Hypothesis Tests for Other Parameters.

One-Way ANOVA.

Tests for Normality.

Conclusion.

Case Study: Regression Toward the Mean.

Case Study: Baseball Statistics.

Case Study: The Wichita Anti-Drunk Driving Advertising Campaign.

**10. STATISTICAL PROCESS CONTROL. **

Introduction.

Deming's 14 Points.

Basic Ideas Behind Control Charts.

Control Charts for Variables.

Control Charts for Attributes.

Process Capability.

Conclusion.

Case Study: The Lamination Process at Intergalactica.

Case Study: Paper Production for Fornax at the Pluto Mill.

**11. 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.

Case Study: Quantity Discounts at the FirmChair Company.

Case Study: Housing Price Structure in MidCity.

Case Study: Demand for French Bread at Howie's.

Case Study: Investing for Retirement.

**12. REGRESSION ANALYSIS: STATISTICAL INFERENCE. **

Introduction.

The Statistical Model.

Inferences About the Regression Coefficients.

Multicollinearity.

Include/Exclude Decisions.

Stepwise Regression.

A Test for the Overall Fit: The ANOVA Table.

The Partial F Test.

Outliers.

Violations of Regression Assumptions.

Prediction.

Conclusion.

Case Study: The Artsy Corporation.

Case Study: Heating Oil at Dupree Fuels Company.

Case Study: Developing a Flexible Budget at the Gunderson Plant.

Case Study: Forecasting Overhead at Wagner Printers.

**13. TIME SERIES ANALYSIS AND FORECASTING. **

Introduction.

Forecasting Methods: An Overview.

Random Series.

The Random Walk Model.

Autoregression Models.

Regression-Based Trend Models.

Moving Averages.

Exponential Smoothing.

Deseasonalizing: The Ratio-to-Moving-Averages Method.

Estimating Seasonality with Regression.

Econometric Models.

Conclusion.

Case Study: Arrivals at the Credit Union.

Case Study: Forecasting Weekly Sales at Amanta.

**14. 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.

Case Study: Shelby Shelving.

**15. OPTIMIZATION MODELING: APPLICATIONS. **

Introduction.

Static Workforce Scheduling.

Blending Models.

Logistics Models.

Aggregate Planning Models.

A Dynamic Financial Model.

Integer Programming Models.

Nonlinear Models.

Conclusion.

Case Study: Giant Motor Company.

Case Study: GMS Stock Hedging.

Case Study: Durham Asset Management.

**16. SIMULATION MODELS. **

Introduction.

Random Numbers.

Introduction to Spreadsheet Simulation.

Simulating from Other Probability Distributions.

Simulating with @Risk.

A Financial Planning Model.

A Cash Balance Model.

Simulating Stock Prices and Options.

A Market Share Model.

Simulating Correlated Values.

Using TopRank with @Risk for Powerful Modeling.

Conclusion.

Case Study: Ski Jacket Production.

Case Study: The College Fund Investment Decision.

Case Study: Ebony Bath Soap.

Case Study: Bond Investment Strategy.

REFERENCES.

INDEX.

Publisher Info

Publisher: Duxbury Press

Published: 1999

International: No

Published: 1999

International: No

In response to the growing market trend in quantitative education, Albright, Winston, and Zappe's integrated business-statistics and management-science text presents core statistics and management-science methods in a modern, unified spreadsheet-oriented approach. With a focus on analyzing, not on techniques, the book covers business statistics with some essential managerial-science topics included. The example-based, Excel spreadsheet approach is useful in courses that combine traditional statistics and management-science topics though can be easily used for a one-term business statistics only course. The modeling and application focus, together with the Excel spreadsheet add-ins, provides a complete learning source for both students and practicing managers.

- Outstanding commercial Excel software add-ins are included for statistics, decision analysis, and simulation. Includes Palisade Corporations' Decision Tools Suite (@Risk, Precision Tree, Best Fit, DataPro {for Statistics}, and SolverTable).
- Over 800 real data-based exercises and case studies are included.
- More than 300 practical examples are included, from finance, marketing, and operations.
- The book offers comprehensive coverage for two major subjects (business statistics and management science) in a unified manner. The text-features an example-based spreadsheet approach to data analysis and modeling, a unique and timely change for many instructors.
- Spreadsheet simulations are used to demonstrate statistical concepts visually throughout the book.
- A pragmatic approach to data analysis is used, with emphasis on only those aspects that will help make students more valuable employees. Each chapter features a decision-making framework with examples designed to help students learn to analyze data with an eye toward effective decision-making as the end result.
- The book shows students how to translate real business problems-expressed in words-into spreadsheets that list the inputs and decision variables. The authors then relate these to outputs by means of appropriate formulas.

**PREFACE. 1. INTRODUCTION TO DATA ANALYSIS & DECISION MAKING.**

Introduction.

An Overview of the Book.

A Sampling of Examples.

Modeling and Models.

Conclusion.

Case Study: Entertainment on a Cruise Ship.

**2. 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.

Case Study: Customer Arrivals at Bank98.

Case Study: Automobile Production and Purchases.

Case Study: Saving, Spending, and Social Climbing.

**3. 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. Case Study: The Dow Jones Averages.

Case Study: Other Market Indexes.

**4. PROBABILITY AND PROBABILITY DISTRIBUTIONS. **

Introduction.

Probability Essentials.

Distribution of a Single Random Variable.

An Introduction to Simulation.

Subjective Versus Objective Probabilities.

Derived Probability Distributions.

Distribution of Two Random Variables: Scenario Approach.

Distribution of Two Random Variables: Joint Probability Approach.

Independent Random Variables.

Weighted Sums of Random Variables.

Conclusion. Case Study: Simpson's Paradox.

**5. NORMAL, BINOMIAL, AND POISSON DISTRIBUTIONS. **

Introduction.

The Normal Distribution.

Applications of the Normal Distribution.

The Binomial Distribution.

Applications of the Binomial Distribution.

The Poisson Distribution.

Fitting a Probability Distribution to Data: BestFit.

Conclusion.

Case Study: EuroWatch Company.

Case Study: Cashing in on the Lottery.

**6. DECISION MAKING UNDER UNCERTAINTY. **

Introduction.

Elements of a Decision Analysis.

The PrecisionTree Add-In.

Introduction to Influence Diagrams.

More Single-Stage Examples.

Multistage Decision Problems.

Bayes' Rule.

Incorporating Attitudes Toward Risk.

Conclusion.

Case Study: Jogger Shoe Company.

**7. SAMPLING AND SAMPLING DISTRIBUTIONS. **

Introduction.

Sampling Terminology.

Methods for Selecting Random Samples.

An Introduction to Estimation. Conclusion.

Case Study: Sampling from Videocassette Renters.

**8. 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 a Difference Between Means.

Confidence Interval for the Difference Between Proportions.

Controlling Confidence Interval Length.

Conclusion.

Case Study: Harrigan University Admissions.

Case Study: Employee Retention at D & Y. Delivery Times at SnowPea Restaurant.

Case Study: The Bodfish Lot Cruise.

**9. HYPOTHESIS TESTING. **

Introduction.

Concepts in Hypothesis Testing.

Hypothesis Tests for a Population Mean.

Hypothesis Tests for Other Parameters.

One-Way ANOVA.

Tests for Normality.

Conclusion.

Case Study: Regression Toward the Mean.

Case Study: Baseball Statistics.

Case Study: The Wichita Anti-Drunk Driving Advertising Campaign.

**10. STATISTICAL PROCESS CONTROL. **

Introduction.

Deming's 14 Points.

Basic Ideas Behind Control Charts.

Control Charts for Variables.

Control Charts for Attributes.

Process Capability.

Conclusion.

Case Study: The Lamination Process at Intergalactica.

Case Study: Paper Production for Fornax at the Pluto Mill.

**11. 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.

Case Study: Quantity Discounts at the FirmChair Company.

Case Study: Housing Price Structure in MidCity.

Case Study: Demand for French Bread at Howie's.

Case Study: Investing for Retirement.

**12. REGRESSION ANALYSIS: STATISTICAL INFERENCE. **

Introduction.

The Statistical Model.

Inferences About the Regression Coefficients.

Multicollinearity.

Include/Exclude Decisions.

Stepwise Regression.

A Test for the Overall Fit: The ANOVA Table.

The Partial F Test.

Outliers.

Violations of Regression Assumptions.

Prediction.

Conclusion.

Case Study: The Artsy Corporation.

Case Study: Heating Oil at Dupree Fuels Company.

Case Study: Developing a Flexible Budget at the Gunderson Plant.

Case Study: Forecasting Overhead at Wagner Printers.

**13. TIME SERIES ANALYSIS AND FORECASTING. **

Introduction.

Forecasting Methods: An Overview.

Random Series.

The Random Walk Model.

Autoregression Models.

Regression-Based Trend Models.

Moving Averages.

Exponential Smoothing.

Deseasonalizing: The Ratio-to-Moving-Averages Method.

Estimating Seasonality with Regression.

Econometric Models.

Conclusion.

Case Study: Arrivals at the Credit Union.

Case Study: Forecasting Weekly Sales at Amanta.

**14. 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.

Case Study: Shelby Shelving.

**15. OPTIMIZATION MODELING: APPLICATIONS. **

Introduction.

Static Workforce Scheduling.

Blending Models.

Logistics Models.

Aggregate Planning Models.

A Dynamic Financial Model.

Integer Programming Models.

Nonlinear Models.

Conclusion.

Case Study: Giant Motor Company.

Case Study: GMS Stock Hedging.

Case Study: Durham Asset Management.

**16. SIMULATION MODELS. **

Introduction.

Random Numbers.

Introduction to Spreadsheet Simulation.

Simulating from Other Probability Distributions.

Simulating with @Risk.

A Financial Planning Model.

A Cash Balance Model.

Simulating Stock Prices and Options.

A Market Share Model.

Simulating Correlated Values.

Using TopRank with @Risk for Powerful Modeling.

Conclusion.

Case Study: Ski Jacket Production.

Case Study: The College Fund Investment Decision.

Case Study: Ebony Bath Soap.

Case Study: Bond Investment Strategy.

REFERENCES.

INDEX.