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

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