A Quantitative Approach to Commercial Damages, + Website

MARK G. FILLER, CPA/ABV, CBA, AM, CVA, is President of Filler & Associates, a valuation and litigation support practice. He recently was also chair of the editorial board of NACVA's The Valuation Examiner and coauthor of NACVA's quarterly marketing newsletter Insights on Valuation. Filler has published various articles and is recognized as a qualified expert witness, testifying frequently on business valuation, commercial damages, and personal injury matters at depositions and in state and federal courts. JAMES A. DIGABRIELE, PHD/DPS, CPA/ABV, CFF, CFE, CFSA, CR.FA, CVA, is a professor of accounting at Montclair State University and has been published in various journals, including Journal of Forensic Accounting, Journal of Business Valuation and Economic Loss Analysis, and The Value Examiner. Dr. DiGabriele is also Managing Director of DiGabriele, McNulty, Campanella & Co., LLC, an accounting firm specializing in forensic/investigative accounting and litigation support.

Preface xvii Is This a Course in Statistics? xvii How This Book is Set Up xviii The Job of the Testifying Expert xix About the Companion Web Site-Spreadsheet Availability xix Note xx Acknowledgments xxi Introduction The Application of Statistics to the Measurement of Damages for Lost Profits 1 The Three Big Statistical Ideas 1 Variation 1 Correlation 2 Rejection Region or Area 4 Introduction to the Idea of Lost Profits 6 Stage 1. Calculating the Difference Between Those Revenues That Should Have Been Earned and What Was Actually Earned During the Period of Interruption 7 Stage 2. Analyzing Costs and Expenses to Separate Continuing from Noncontinuing 8 Stage 3. Examining Continuing Expenses Patterns for Extra Expense 8 Stage 4. Computing the Actual Loss Sustained or Lost Profits 8 Choosing a Forecasting Model 9 Type of Interruption 9 Length of Period of Interruption 10 Availability of Historical Data 10 Regularity of Sales Trends and Patterns 10 Ease of Explanation 10 Conventional Forecasting Models 11 Simple Arithmetic Models 11 More Complex Arithmetic Models 11 Trendline and Curve-Fitting Models 12 Seasonal Factor Models 12 Smoothing Methods 12 Multiple Regression Models 13 Other Applications of Statistical Models 14 Conclusion 14 Notes 15 Chapter 1 Case Study 1-Uses of the Standard Deviation 17 The Steps of Data Analysis 17 Shape 18 Spread 19 Conclusion 23 Notes 23 Chapter 2 Case Study 2-Trend and Seasonality Analysis 25 Claim Submitted 25 Claim Review 26 Occupancy Percentages 26 Trend, Seasonality, and Noise 28 Trendline Test 33 Cycle Testing 33 Conclusion 34 Note 36 Chapter 3 Case Study 3-An Introduction to Regression Analysis and Its Application to the Measurement of Economic Damages 37 What is Regression Analysis and Where Have I Seen It Before? 37 A Brief Introduction to Simple Linear Regression 38 I Get Good Results with Average or Median Ratios-Why Should I Switch to Regression Analysis? 40 How Does One Perform a Regression Analysis Using Microsoft Excel? 43 Why Does Simple Linear Regression Rarely Give Us the Right Answer, and What Can We Do about It? 51 Should We Treat the Value Driver Annual Revenue in the Same Manner as We Have Seller's Discretionary Earnings? 60 What are the Meaning and Function of the Regression Tool's Summary Output? 68 Regression Statistics 69 Tests and Analysis of Residuals 75 Testing the Linearity Assumption 77 Testing the Normality Assumption 78 Testing the Constant Variance Assumption 80 Testing the Independence Assumption 83 Testing the No Errors-in-Variables Assumption 84 Testing the No Multicollinearity Assumption 84 Conclusion 87 Note 87 Chapter 4 Case Study 4-Choosing a Sales Forecasting Model: A Trial and Error Process 89 Correlation with Industry Sales 89 Conversion to Quarterly Data 89 Quadratic Regression Model 92 Problems with the Quarterly Quadratic Model 92 Substituting a Monthly Quadratic Model 94 Conclusion 95 Note 99 Chapter 5 Case Study 5-Time Series Analysis with Seasonal Adjustment 101 Exploratory Data Analysis 101 Seasonal Indexes versus Dummy Variables 102 Creation of the Optimized Seasonal Indexes 103 Creation of the Monthly Time Series Model 108 Creation of the Composite Model 108 Conclusion 115 Notes 115 Chapter 6 Case Study 6-Cross-Sectional Regression Combined with Seasonal Indexes to Determine Lost Profits 117 Outline of the Case 117 Testing for Noise in the Data 119 Converting to Quarterly Data 119 Optimizing Seasonal Indexes 119 Exogenous Predictor Variable 124 Interrupted Time Series Analysis 124 "But For" Sales Forecast 126 Transforming the Dependent Variable 130 Dealing with Mitigation 130 Computing Saved Costs and Expenses 133 Conclusion 137 Note 138 Chapter 7 Case Study 7-Measuring Differences in Pre- and Postincident Sales Using Two Sample t-Tests versus Regr