Mark Filler, James A. DiGabriele
A Quantitative Approach to Commercial Damages, + Website
Applying Statistics to the Measurement of Lost Profits
Mark Filler, James A. DiGabriele
A Quantitative Approach to Commercial Damages, + Website
Applying Statistics to the Measurement of Lost Profits
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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…mehr
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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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 1W118072590
- 1. Auflage
- Seitenzahl: 352
- Erscheinungstermin: 8. Mai 2012
- Englisch
- Abmessung: 260mm x 183mm x 23mm
- Gewicht: 688g
- ISBN-13: 9781118072592
- ISBN-10: 1118072596
- Artikelnr.: 34754863
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 1W118072590
- 1. Auflage
- Seitenzahl: 352
- Erscheinungstermin: 8. Mai 2012
- Englisch
- Abmessung: 260mm x 183mm x 23mm
- Gewicht: 688g
- ISBN-13: 9781118072592
- ISBN-10: 1118072596
- Artikelnr.: 34754863
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 Regression Models 139
Preliminary Tests of the Data 139 Using the t-Test Two Sample Assuming
Unequal Variances Tool 141 Regression Approach to the Problem 141 A New
Data Set--Different Results 143 Selecting the Appropriate Regression Model
143 Finding the Facts Behind the Figures 148 Conclusion 151 Notes 153
CHAPTER 8 Case Study 8--Interrupted Time Series Analysis, Holdback
Forecasting, and Variable Transformation 155 Graph Your Data 155 Industry
Comparisons 155 Accounting for Seasonality 157 Accounting for Trend 161
Accounting for Interventions 161 Forecasting "Should Be" Sales 164 Testing
the Model 167 Final Sales Forecast 169 Conclusion 169 CHAPTER 9 Case Study
9--An Exercise in Cost Estimation to Determine Saved Expenses 171
Classifying Cost Behavior 171 An Arbitrary Classification 172 Graph Your
Data 172 Testing the Assumption of Significance 174 Expense Drivers 174
Conclusion 177 CHAPTER 10 Case Study 10--Saved Expenses, Bivariate Model
Inadequacy, and Multiple Regression Models 179 Graph Your Data 179
Regression Summary Output of the First Model 181 Search for Other
Independent Variables 183 Regression Summary Output of the Second Model 185
Conclusion 188 CHAPTER 11 Case Study 11--Analysis of and Modification to
Opposing Experts' Reports 189 Background Information 189 Stipulated Facts
and Data 190 The Flaw Common to Both Experts 194 Defendant's Expert's
Report 196 Plaintiff's Expert's Report 199 The Modified-Exponential Growth
Curve 201 Four Damages Models 208 Conclusion 208 CHAPTER 12 Case Study
12--Further Considerations in the Determination of Lost Profits 209 A
Review of Methods of Loss Calculation 210 A Case Study: Dunlap Drive-In
Diner 211 Skeptical Analysis Using the Fraud Theory Approach 212 Revenue
Adjustment 212 Officer's Compensation Adjustment 214 Continuing Salaries
and Wages (Payroll) Adjustment 215 Rent Adjustment 215 Employee Bonus 216
Discussion 216 Conclusion 217 CHAPTER 13 Case Study 13--A Simple Approach
to Forecasting Sales 221 Month Length Adjustment 221 Graph Your Data 221
Worksheet Setup 222 First Forecasting Method 227 Second Forecasting Method
227 Selection of Length of Prior Period 228 Reasonableness Test 228
Conclusion 229 CHAPTER 14 Case Study 14--Data Analysis Tools for
Forecasting Sales 231 Need for Analytical Tests 231 Graph Your Data 231
Statistical Procedures 233 Tests for Randomness 235 Tests for Trend and
Seasonality 240 Testing for Seasonality and Trend with a Regression Model
246 Conclusion 249 Notes 249 CHAPTER 15 Case Study 15--Determining Lost
Sales with Stationary Time Series Data 251 Prediction Errors and Their
Measurement 251 Moving Averages 252 Array Formulas 254 Weighted Moving
Averages 256 Simple Exponential Smoothing 260 Seasonality with Additive
Effects 263 Seasonality with Multiplicative Effects 268 Conclusion 272
CHAPTER 16 Case Study 16--Determining Lost Sales Using Nonregression Trend
Models 273 When Averaging Techniques Are Not Appropriate 273 Double Moving
Average 275 Double Exponential Smoothing (Holt's Method) 277 Triple
Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects
279 Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative
Seasonal Effects 285 Conclusion 288 APPENDIX The Next Frontier in the
Application of Statistics 291 The Technology 291 EViews 291 Minitab 292
NCSS 292 The R Project for Statistical Computing 293 SAS 294 SPSS 295 Stata
296 WINKS SDA 7 Professional 298 Conclusion 299 Bibliography of Suggested
Statistics Textbooks 301 Glossary of Statistical Terms 303 About the
Authors 317 Index 319
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 Regression Models 139
Preliminary Tests of the Data 139 Using the t-Test Two Sample Assuming
Unequal Variances Tool 141 Regression Approach to the Problem 141 A New
Data Set--Different Results 143 Selecting the Appropriate Regression Model
143 Finding the Facts Behind the Figures 148 Conclusion 151 Notes 153
CHAPTER 8 Case Study 8--Interrupted Time Series Analysis, Holdback
Forecasting, and Variable Transformation 155 Graph Your Data 155 Industry
Comparisons 155 Accounting for Seasonality 157 Accounting for Trend 161
Accounting for Interventions 161 Forecasting "Should Be" Sales 164 Testing
the Model 167 Final Sales Forecast 169 Conclusion 169 CHAPTER 9 Case Study
9--An Exercise in Cost Estimation to Determine Saved Expenses 171
Classifying Cost Behavior 171 An Arbitrary Classification 172 Graph Your
Data 172 Testing the Assumption of Significance 174 Expense Drivers 174
Conclusion 177 CHAPTER 10 Case Study 10--Saved Expenses, Bivariate Model
Inadequacy, and Multiple Regression Models 179 Graph Your Data 179
Regression Summary Output of the First Model 181 Search for Other
Independent Variables 183 Regression Summary Output of the Second Model 185
Conclusion 188 CHAPTER 11 Case Study 11--Analysis of and Modification to
Opposing Experts' Reports 189 Background Information 189 Stipulated Facts
and Data 190 The Flaw Common to Both Experts 194 Defendant's Expert's
Report 196 Plaintiff's Expert's Report 199 The Modified-Exponential Growth
Curve 201 Four Damages Models 208 Conclusion 208 CHAPTER 12 Case Study
12--Further Considerations in the Determination of Lost Profits 209 A
Review of Methods of Loss Calculation 210 A Case Study: Dunlap Drive-In
Diner 211 Skeptical Analysis Using the Fraud Theory Approach 212 Revenue
Adjustment 212 Officer's Compensation Adjustment 214 Continuing Salaries
and Wages (Payroll) Adjustment 215 Rent Adjustment 215 Employee Bonus 216
Discussion 216 Conclusion 217 CHAPTER 13 Case Study 13--A Simple Approach
to Forecasting Sales 221 Month Length Adjustment 221 Graph Your Data 221
Worksheet Setup 222 First Forecasting Method 227 Second Forecasting Method
227 Selection of Length of Prior Period 228 Reasonableness Test 228
Conclusion 229 CHAPTER 14 Case Study 14--Data Analysis Tools for
Forecasting Sales 231 Need for Analytical Tests 231 Graph Your Data 231
Statistical Procedures 233 Tests for Randomness 235 Tests for Trend and
Seasonality 240 Testing for Seasonality and Trend with a Regression Model
246 Conclusion 249 Notes 249 CHAPTER 15 Case Study 15--Determining Lost
Sales with Stationary Time Series Data 251 Prediction Errors and Their
Measurement 251 Moving Averages 252 Array Formulas 254 Weighted Moving
Averages 256 Simple Exponential Smoothing 260 Seasonality with Additive
Effects 263 Seasonality with Multiplicative Effects 268 Conclusion 272
CHAPTER 16 Case Study 16--Determining Lost Sales Using Nonregression Trend
Models 273 When Averaging Techniques Are Not Appropriate 273 Double Moving
Average 275 Double Exponential Smoothing (Holt's Method) 277 Triple
Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects
279 Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative
Seasonal Effects 285 Conclusion 288 APPENDIX The Next Frontier in the
Application of Statistics 291 The Technology 291 EViews 291 Minitab 292
NCSS 292 The R Project for Statistical Computing 293 SAS 294 SPSS 295 Stata
296 WINKS SDA 7 Professional 298 Conclusion 299 Bibliography of Suggested
Statistics Textbooks 301 Glossary of Statistical Terms 303 About the
Authors 317 Index 319
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 Regression Models 139
Preliminary Tests of the Data 139 Using the t-Test Two Sample Assuming
Unequal Variances Tool 141 Regression Approach to the Problem 141 A New
Data Set--Different Results 143 Selecting the Appropriate Regression Model
143 Finding the Facts Behind the Figures 148 Conclusion 151 Notes 153
CHAPTER 8 Case Study 8--Interrupted Time Series Analysis, Holdback
Forecasting, and Variable Transformation 155 Graph Your Data 155 Industry
Comparisons 155 Accounting for Seasonality 157 Accounting for Trend 161
Accounting for Interventions 161 Forecasting "Should Be" Sales 164 Testing
the Model 167 Final Sales Forecast 169 Conclusion 169 CHAPTER 9 Case Study
9--An Exercise in Cost Estimation to Determine Saved Expenses 171
Classifying Cost Behavior 171 An Arbitrary Classification 172 Graph Your
Data 172 Testing the Assumption of Significance 174 Expense Drivers 174
Conclusion 177 CHAPTER 10 Case Study 10--Saved Expenses, Bivariate Model
Inadequacy, and Multiple Regression Models 179 Graph Your Data 179
Regression Summary Output of the First Model 181 Search for Other
Independent Variables 183 Regression Summary Output of the Second Model 185
Conclusion 188 CHAPTER 11 Case Study 11--Analysis of and Modification to
Opposing Experts' Reports 189 Background Information 189 Stipulated Facts
and Data 190 The Flaw Common to Both Experts 194 Defendant's Expert's
Report 196 Plaintiff's Expert's Report 199 The Modified-Exponential Growth
Curve 201 Four Damages Models 208 Conclusion 208 CHAPTER 12 Case Study
12--Further Considerations in the Determination of Lost Profits 209 A
Review of Methods of Loss Calculation 210 A Case Study: Dunlap Drive-In
Diner 211 Skeptical Analysis Using the Fraud Theory Approach 212 Revenue
Adjustment 212 Officer's Compensation Adjustment 214 Continuing Salaries
and Wages (Payroll) Adjustment 215 Rent Adjustment 215 Employee Bonus 216
Discussion 216 Conclusion 217 CHAPTER 13 Case Study 13--A Simple Approach
to Forecasting Sales 221 Month Length Adjustment 221 Graph Your Data 221
Worksheet Setup 222 First Forecasting Method 227 Second Forecasting Method
227 Selection of Length of Prior Period 228 Reasonableness Test 228
Conclusion 229 CHAPTER 14 Case Study 14--Data Analysis Tools for
Forecasting Sales 231 Need for Analytical Tests 231 Graph Your Data 231
Statistical Procedures 233 Tests for Randomness 235 Tests for Trend and
Seasonality 240 Testing for Seasonality and Trend with a Regression Model
246 Conclusion 249 Notes 249 CHAPTER 15 Case Study 15--Determining Lost
Sales with Stationary Time Series Data 251 Prediction Errors and Their
Measurement 251 Moving Averages 252 Array Formulas 254 Weighted Moving
Averages 256 Simple Exponential Smoothing 260 Seasonality with Additive
Effects 263 Seasonality with Multiplicative Effects 268 Conclusion 272
CHAPTER 16 Case Study 16--Determining Lost Sales Using Nonregression Trend
Models 273 When Averaging Techniques Are Not Appropriate 273 Double Moving
Average 275 Double Exponential Smoothing (Holt's Method) 277 Triple
Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects
279 Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative
Seasonal Effects 285 Conclusion 288 APPENDIX The Next Frontier in the
Application of Statistics 291 The Technology 291 EViews 291 Minitab 292
NCSS 292 The R Project for Statistical Computing 293 SAS 294 SPSS 295 Stata
296 WINKS SDA 7 Professional 298 Conclusion 299 Bibliography of Suggested
Statistics Textbooks 301 Glossary of Statistical Terms 303 About the
Authors 317 Index 319
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 Regression Models 139
Preliminary Tests of the Data 139 Using the t-Test Two Sample Assuming
Unequal Variances Tool 141 Regression Approach to the Problem 141 A New
Data Set--Different Results 143 Selecting the Appropriate Regression Model
143 Finding the Facts Behind the Figures 148 Conclusion 151 Notes 153
CHAPTER 8 Case Study 8--Interrupted Time Series Analysis, Holdback
Forecasting, and Variable Transformation 155 Graph Your Data 155 Industry
Comparisons 155 Accounting for Seasonality 157 Accounting for Trend 161
Accounting for Interventions 161 Forecasting "Should Be" Sales 164 Testing
the Model 167 Final Sales Forecast 169 Conclusion 169 CHAPTER 9 Case Study
9--An Exercise in Cost Estimation to Determine Saved Expenses 171
Classifying Cost Behavior 171 An Arbitrary Classification 172 Graph Your
Data 172 Testing the Assumption of Significance 174 Expense Drivers 174
Conclusion 177 CHAPTER 10 Case Study 10--Saved Expenses, Bivariate Model
Inadequacy, and Multiple Regression Models 179 Graph Your Data 179
Regression Summary Output of the First Model 181 Search for Other
Independent Variables 183 Regression Summary Output of the Second Model 185
Conclusion 188 CHAPTER 11 Case Study 11--Analysis of and Modification to
Opposing Experts' Reports 189 Background Information 189 Stipulated Facts
and Data 190 The Flaw Common to Both Experts 194 Defendant's Expert's
Report 196 Plaintiff's Expert's Report 199 The Modified-Exponential Growth
Curve 201 Four Damages Models 208 Conclusion 208 CHAPTER 12 Case Study
12--Further Considerations in the Determination of Lost Profits 209 A
Review of Methods of Loss Calculation 210 A Case Study: Dunlap Drive-In
Diner 211 Skeptical Analysis Using the Fraud Theory Approach 212 Revenue
Adjustment 212 Officer's Compensation Adjustment 214 Continuing Salaries
and Wages (Payroll) Adjustment 215 Rent Adjustment 215 Employee Bonus 216
Discussion 216 Conclusion 217 CHAPTER 13 Case Study 13--A Simple Approach
to Forecasting Sales 221 Month Length Adjustment 221 Graph Your Data 221
Worksheet Setup 222 First Forecasting Method 227 Second Forecasting Method
227 Selection of Length of Prior Period 228 Reasonableness Test 228
Conclusion 229 CHAPTER 14 Case Study 14--Data Analysis Tools for
Forecasting Sales 231 Need for Analytical Tests 231 Graph Your Data 231
Statistical Procedures 233 Tests for Randomness 235 Tests for Trend and
Seasonality 240 Testing for Seasonality and Trend with a Regression Model
246 Conclusion 249 Notes 249 CHAPTER 15 Case Study 15--Determining Lost
Sales with Stationary Time Series Data 251 Prediction Errors and Their
Measurement 251 Moving Averages 252 Array Formulas 254 Weighted Moving
Averages 256 Simple Exponential Smoothing 260 Seasonality with Additive
Effects 263 Seasonality with Multiplicative Effects 268 Conclusion 272
CHAPTER 16 Case Study 16--Determining Lost Sales Using Nonregression Trend
Models 273 When Averaging Techniques Are Not Appropriate 273 Double Moving
Average 275 Double Exponential Smoothing (Holt's Method) 277 Triple
Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects
279 Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative
Seasonal Effects 285 Conclusion 288 APPENDIX The Next Frontier in the
Application of Statistics 291 The Technology 291 EViews 291 Minitab 292
NCSS 292 The R Project for Statistical Computing 293 SAS 294 SPSS 295 Stata
296 WINKS SDA 7 Professional 298 Conclusion 299 Bibliography of Suggested
Statistics Textbooks 301 Glossary of Statistical Terms 303 About the
Authors 317 Index 319