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A Quantitative Approach to Commercial Damages: Applying Statistics to the Measurement of Lost Profits, + Website

Book Description

How-to guidance for measuring lost profits due to business interruption damages

A Quantitative Approach to Commercial Damages explains the complicated process of measuring business interruption damages, whether they are losses are from natural or man-made disasters, or whether the performance of one company adversely affects the performance of another. Using a methodology built around case studies integrated with solution tools, this book is presented step by step from the analysis damages perspective to aid in preparing a damage claim. Over 250 screen shots are included and key cell formulas that show how to construct a formula and lay it out on the spreadsheet.

  • Includes Excel spreadsheet applications and key cell formulas for those who wish to construct their own spreadsheets

  • Offers a step-by-step approach to computing damages using case studies and over 250 screen shots

  • Often in the course of business, a firm will be damaged by the actions of another individual or company, such as a fire that shuts down a restaurant for two months. Often, this results in the filing of a business interruption claim. Discover how to measure business losses with the proven guidance found in A Quantitative Approach to Commercial Damages.

    Table of Contents

    1. Cover
    2. Series
    3. Title Page
    4. Copyright
    5. Dedication
    6. Preface
      1. Is This a Course in Statistics?
      2. How This Book Is Set Up
      3. The Job of the Testifying Expert
      4. About the Companion Web Site—Spreadsheet Availability
    7. Acknowledgments
    8. INTRODUCTION: The Application of Statistics to the Measurement of Damages for Lost Profits
      1. The Three Big Statistical Ideas
      2. Introduction to the Idea of Lost Profits
      3. Choosing a Forecasting Model
      4. Conventional Forecasting Models
      5. Other Applications of Statistical Models
      6. Conclusion
      7. Notes
    9. CHAPTER 1: Case Study 1—Uses of the Standard Deviation
      1. The Steps of Data Analysis
      2. Conclusion
      3. Notes
    10. CHAPTER 2: Case Study 2—Trend and Seasonality Analysis
      1. Claim Submitted
      2. Claim Review
      3. Occupancy Percentages
      4. Trend, Seasonality, and Noise
      5. Trendline Test
      6. Cycle Testing
      7. Conclusion
      8. Note
    11. CHAPTER 3: Case Study 3—An Introduction to Regression Analysis and Its Application to the Measurement of Economic Damages
      1. What Is Regression Analysis and Where Have I Seen It Before?
      2. A Brief Introduction to Simple Linear Regression
      3. I Get Good Results with Average or Median Ratios—Why Should I Switch to Regression Analysis?
      4. Regression Statistics
      5. Tests and Analysis of Residuals
      6. Testing the Linearity Assumption
      7. Testing the Normality Assumption
      8. Testing the Constant Variance Assumption
      9. Testing the Independence Assumption
      10. Testing the No Errors-in-Variables Assumption
      11. Testing the No Multicollinearity Assumption
      12. Conclusion
      13. Note
    12. CHAPTER 4: Case Study 4—Choosing a Sales Forecasting Model: A Trial and Error Process
      1. Correlation with Industry Sales
      2. Conversion to Quarterly Data
      3. Quadratic Regression Model
      4. Problems with the Quarterly Quadratic Model
      5. Substituting a Monthly Quadratic Model
      6. Conclusion
      7. Note
    13. CHAPTER 5: Case Study 5—Time Series Analysis with Seasonal Adjustment
      1. Exploratory Data Analysis
      2. Seasonal Indexes versus Dummy Variables
      3. Creation of the Optimized Seasonal Indexes
      4. Creation of the Monthly Time Series Model
      5. Creation of the Composite Model
      6. Conclusion
      7. Notes
    14. CHAPTER 6: Case Study 6—Cross-Sectional Regression Combined with Seasonal Indexes to Determine Lost Profits
      1. Outline of the Case
      2. Testing for Noise in the Data
      3. Converting to Quarterly Data
      4. Optimizing Seasonal Indexes
      5. Exogenous Predictor Variable
      6. Interrupted Time Series Analysis
      7. “But For” Sales Forecast
      8. Transforming the Dependent Variable
      9. Dealing with Mitigation
      10. Computing Saved Costs and Expenses
      11. Conclusion
      12. Note
    15. CHAPTER 7: Case Study 7—Measuring Differences in Pre- and Postincident Sales Using Two Sample t-Tests versus Regression Models
      1. Preliminary Tests of the Data
      2. Selecting the Appropriate Regression Model
      3. Finding the Facts Behind the Figures
      4. Conclusion
      5. Notes
    16. CHAPTER 8: Case Study 8—Interrupted Time Series Analysis, Holdback Forecasting, and Variable Transformation
      1. Graph Your Data
      2. Industry Comparisons
      3. Accounting for Seasonality
      4. Accounting for Trend
      5. Accounting for Interventions
      6. Forecasting “Should Be” Sales
      7. Testing the Model
      8. Final Sales Forecast
      9. Conclusion
    17. CHAPTER 9: Case Study 9—An Exercise in Cost Estimation to Determine Saved Expenses
      1. Classifying Cost Behavior
      2. An Arbitrary Classification
      3. Graph Your Data
      4. Testing the Assumption of Significance
      5. Expense Drivers
      6. Conclusion
    18. CHAPTER 10: Case Study 10—Saved Expenses, Bivariate Model Inadequacy, and Multiple Regression Models
      1. Graph Your Data
      2. Regression Summary Output of the First Model
      3. Search for Other Independent Variables
      4. Regression Summary Output of the Second Model
      5. Conclusion
    19. CHAPTER 11: Case Study 11—Analysis of and Modification to Opposing Experts' Reports
      1. Background Information
      2. Stipulated Facts and Data
      3. The Flaw Common to Both Experts
      4. Defendant's Expert's Report
      5. Plaintiff's Expert's Report
      6. The Modified-Exponential Growth Curve
      7. Four Damages Models
      8. Conclusion
    20. CHAPTER 12: Case Study 12—Further Considerations in the Determination of Lost Profits
      1. A Review of Methods of Loss Calculation
      2. A Case Study: Dunlap Drive-In Diner
      3. Skeptical Analysis Using the Fraud Theory Approach
      4. Discussion
      5. Conclusion
    21. CHAPTER 13: Case Study 13—A Simple Approach to Forecasting Sales
      1. Month Length Adjustment
      2. Graph Your Data
      3. Worksheet Setup
      4. Selection of Length of Prior Period
      5. Reasonableness Test
      6. Conclusion
    22. CHAPTER 14: Case Study 14—Data Analysis Tools for Forecasting Sales
      1. Need for Analytical Tests
      2. Graph Your Data
      3. Statistical Procedures
      4. Tests for Randomness
      5. Tests for Trend and Seasonality
      6. Testing for Seasonality and Trend with a Regression Model
      7. Conclusion
      8. Notes
    23. CHAPTER 15: Case Study 15—Determining Lost Sales with Stationary Time Series Data
      1. Prediction Errors and Their Measurement
      2. Moving Averages
      3. Array Formulas
      4. Weighted Moving Averages
      5. Simple Exponential Smoothing
      6. Seasonality with Additive Effects
      7. Seasonality with Multiplicative Effects
      8. Conclusion
    24. CHAPTER 16: Case Study 16—Determining Lost Sales Using Nonregression Trend Models
      1. When Averaging Techniques Are Not Appropriate
      2. Double Moving Average
      3. Double Exponential Smoothing (Holt's Method)
      4. Triple Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects
      5. Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative Seasonal Effects
      6. Conclusion
    25. APPENDIX: The Next Frontier in the Application of Statistics
      1. The Technology
      2. Conclusion
    26. Bibliography of Suggested Statistics Textbooks
    27. Glossary of Statistical Terms
    28. About the Authors
    29. Index