Preface
Is This A Course In Statistics?
How This Book is Setup
The Job of the Testifying Expert
Spreadsheet Availability
Acknowledgements
Introduction
The Application of Statistics to the Measurement of Damages for Lost Profits
The Three Big Statistical Ideas
Variation
Correlation
The Concept of the Null Hypothesis
Rejection Region, or Area
Introduction to the Idea of Lost Profits
Stage 1. Calculating the Difference Between Those Revenues That Should Have Been Earned and What was Actually Earned During the Period of Interruption
Stage 2. Analyzing Costs and Expenses to Separate Continuing from Non-Continuing
Stage 3. Examining Continuing Expenses Patterns for Extra Expense
Stage 4. Computing the Actual Loss Sustained, or Lost Profits
Choosing a Forecasting Model
Type of Interruption
Length of Period of Interruption
Availability of Historical Data
Regularity of Sales Trends and Patterns
Ease of Explanation
Conventional Forecasting Models
Simple Arithmetic Models
More Complex Arithmetic Models
Trend‑Line and Curve‑Fitting Models
Seasonal Factor Models
Smoothing Methods
Multiple Regression Models
Other Applications of Statistical Models
Conclusion
Notes
Chapter 1 Case Study 1 Uses of the Standard Deviation
The Steps of Data Analysis
Shape
Spread
Conclusion
Notes
Chapter 2 Case Study 2 Trend and Seasonality Analysis
Claim Submitted
Claim Review
Occupancy Percentages
Trend, Seasonality and Noise
Trendline Test
Cycle Testing
Conclusion
Notes
Chapter 3 Case Study 3 An Introduction to Regression Analysis and Its Application to the Measurement of Economic Damages
What Is Regression Analysis and Where Have I Seen It Before?
A Brief Introduction to Simple Linear Regression
I Get Good Results with Average or Median Ratios – Why Should I Switch to Regression Analysis?
How Does One Perform a Regression Analysis Using Microsoft's Excel?
Why Does Simple Linear Regression Rarely Give us the Right Answer, and What Can We Do about It?
Should We Treat the Value Driver Annual Revenue in the Same Manner as We Have Seller's Discretionary Earnings?
What is the Meaning and Function of the Regression Tool's Summary Output?
Regression Statistics
Tests and Analysis of Residuals
Testing the Linearity Assumption
Testing the Normality Assumption
Testing the Constant Variance Assumption
Testing the Independence Assumption
Testing the No Errors-in-Variables Assumption
Testing the No Multicollinearity Assumption
Conclusion
Chapter 4 Case Study 4 Choosing a Sales Forecasting Model: A Trial and Error Process
Correlation with Industry Sales
Conversion to Quarterly Data
Quadratic Regression Model
Problems with the Quarterly Quadratic Model
Substituting a Monthly Quadratic Model
Conclusion
Notes
Chapter 5 Case Study 5 Time Series Analysis with Seasonal Adjustment
Exploratory Data Analysis
Seasonal Indices vs. Dummy Variables
Creation of the Optimized Seasonal Indices
Creation of the Monthly Time Series Model
Creation of the Composite Model
Conclusion
Notes
Chapter 6 Case Study 6 Cross Sectional Regression Combined with Seasonal Indices to Determine Lost Profits
Outline of the Case
Testing for Noise in the Data
Converting to Quarterly Data
Optimizing Seasonal Indices
Exogenous Predictor Variable
Interrupted Time Series Analysis
“But For” Sales Forecast
Transforming the Dependent Variable
Dealing with Mitigation
Computing Saved Costs and Expenses
Conclusion
Notes
Chapter 7 Case Study 7 Measuring Differences in Pre- and Post- Incident Sales Using Two Sample t-Tests versus Regression Models
Preliminary Tests of the Data
Selecting the Appropriate Regression Model
Finding the Facts Behind the Figures
Conclusion
Notes
Chapter 8 Case Study 8 Interrupted Time Series Analysis, Holdback Forecasting and Variable Transformation
Graph Your Data
Industry Comparisons
Accounting for Seasonality
Accounting for Trend
Accounting for Interventions
Forecasting “Should Be” Sales
Testing the Model
Final Sales Forecast
Conclusion
Chapter 9 Case Study 9 An Exercise in Cost Estimation to Determine Saved Expenses
Classifying Cost Behavior
An Arbitrary Classification
Graph Your Data
Testing the Assumption of Significance
Expense Drivers
Conclusion
Chapter 10 Case Study 10 Saved Expenses, Bivariate Model Inadequacy, and Multiple Regression Models
Graph Your Data
Regression Summary Output of the First Model
Search for Other Independent Variables
Regression Summary Output of the Second Model
Conclusion
Chapter 11 Case Study 11 Analysis of and Modification to Opposing Experts' Reports
Background Information
Stipulated Facts and Data
The Flaw Common to Both Experts
Defendant's Expert's Report
Plaintiff's Expert's Report
The Modified-Exponential Growth Curve
Four Damages Models
Conclusion
Chapter 12 Case Study 12 Further Considerations in the Determination of Lost Profits
A Review of Methods of Loss Calculation
A Case Study Dunlap Drive-in-Diner
Skeptical Analysis using the Fraud Theory Approach
Revenue Adjustment
Fraud Theory Approach
Determination
Officer's Compensation Adjustment
Fraud Theory Approach
Determination
Continuing Salaries and Wages (Payroll) Adjustment
Fraud Theory Approach
Determination
Rent Adjustment
Fraud Theory Approach
Determination
Employee Bonus
Fraud Theory Approach
Determination
Discussion
Conclusion
Chapter 13 Case Study 13 A Simple Approach to Forecasting Sales
Month Length Adjustment
Graph Your Data
Worksheet Setup
First Forecasting Method
Second Forecasting Method
Selection of Length of Prior Period
Reasonableness Test
Conclusion
Chapter 14 Case Study 14 Data Analysis Tools for Forecasting Sales
Need for Analytical Tests
Graph Your Data
Statistical Procedures
Tests for Randomness
Tests for Trend and Seasonality
Testing for Seasonality and Trend with a Regression Model
Conclusion
Notes
Chapter 15 Case Study 15 Determining Lost Sales with Stationary Time Series Data
Prediction Errors and Their Measurement
Moving Averages
Array Formulas
Weighted Moving Averages
Simple Exponential Smoothing
Seasonality with Additive Effects
Seasonality with Multiplicative Effects
Conclusion
Chapter 16 Case Study 16 Determining Lost Sales Using Non-Regression Trend Models
When Averaging Techniques Are Not Appropriate
Double Moving Average
Double Exponential Smoothing (Holt's Method)
Triple Exponential Smoothing (Holt-Winter's Method) For Additive Seasonal Effects
Triple Exponential Smoothing (Holt-Winter's Method) For Multiplicative Seasonal Effects
Conclusion
Appendix The Next Frontier in the Application of Statistics
The Technology
EViews
Minitab
NCSS
Sales Ratio Reports
Comparables Reports
Hybrid Appraisal Model
The R Project for Statistical Computing
SAS
SPSS
STATA
WINKS SDA 7 PROFESSIONAL
Conclusion /Discussion
Bibliography of Suggested Statistics Textbooks
Glossary of Statistical Terms
About the Authors
Index
