{"product_id":"a-quantitative-approach-to-commercial-damages-website-isbn-9781118072592","title":"A Quantitative Approach to Commercial Damages, + Website","description":"\u003cb\u003eHow-to guidance for measuring lost profits due to business interruption damages\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIncludes Excel spreadsheet applications and key cell formulas for those who wish to construct their own spreadsheets\u003c\/li\u003e \u003cli\u003eOffers a step-by-step approach to computing damages using case studies and over 250 screen shots\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eOften 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 \u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIs This a Course in Statistics? xvii\u003c\/p\u003e \u003cp\u003eHow This Book is Set Up xviii\u003c\/p\u003e \u003cp\u003eThe Job of the Testifying Expert xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Web Site—Spreadsheet Availability xix\u003c\/p\u003e \u003cp\u003eNote xx\u003c\/p\u003e \u003cp\u003eAcknowledgments xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction \u003c\/b\u003e\u003cb\u003eThe Application of Statistics to the Measurement of Damages for Lost Profits 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Three Big Statistical Ideas 1\u003c\/p\u003e \u003cp\u003eVariation 1\u003c\/p\u003e \u003cp\u003eCorrelation 2\u003c\/p\u003e \u003cp\u003eRejection Region or Area 4\u003c\/p\u003e \u003cp\u003eIntroduction to the Idea of Lost Profits 6\u003c\/p\u003e \u003cp\u003eStage 1. Calculating the Difference Between Those Revenues That Should Have Been Earned and What Was Actually Earned During the Period of Interruption 7\u003c\/p\u003e \u003cp\u003eStage 2. Analyzing Costs and Expenses to Separate Continuing from Noncontinuing 8\u003c\/p\u003e \u003cp\u003eStage 3. Examining Continuing Expenses Patterns for Extra Expense 8\u003c\/p\u003e \u003cp\u003eStage 4. Computing the Actual Loss Sustained or Lost Profits 8\u003c\/p\u003e \u003cp\u003eChoosing a Forecasting Model 9\u003c\/p\u003e \u003cp\u003eType of Interruption 9\u003c\/p\u003e \u003cp\u003eLength of Period of Interruption 10\u003c\/p\u003e \u003cp\u003eAvailability of Historical Data 10\u003c\/p\u003e \u003cp\u003eRegularity of Sales Trends and Patterns 10\u003c\/p\u003e \u003cp\u003eEase of Explanation 10\u003c\/p\u003e \u003cp\u003eConventional Forecasting Models 11\u003c\/p\u003e \u003cp\u003eSimple Arithmetic Models 11\u003c\/p\u003e \u003cp\u003eMore Complex Arithmetic Models 11\u003c\/p\u003e \u003cp\u003eTrendline and Curve-Fitting Models 12\u003c\/p\u003e \u003cp\u003eSeasonal Factor Models 12\u003c\/p\u003e \u003cp\u003eSmoothing Methods 12\u003c\/p\u003e \u003cp\u003eMultiple Regression Models 13\u003c\/p\u003e \u003cp\u003eOther Applications of Statistical Models 14\u003c\/p\u003e \u003cp\u003eConclusion 14\u003c\/p\u003e \u003cp\u003eNotes 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 \u003c\/b\u003e\u003cb\u003eCase Study 1—Uses of the Standard Deviation 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Steps of Data Analysis 17\u003c\/p\u003e \u003cp\u003eShape 18\u003c\/p\u003e \u003cp\u003eSpread 19\u003c\/p\u003e \u003cp\u003eConclusion 23\u003c\/p\u003e \u003cp\u003eNotes 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 \u003c\/b\u003e\u003cb\u003eCase Study 2—Trend and Seasonality Analysis 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClaim Submitted 25\u003c\/p\u003e \u003cp\u003eClaim Review 26\u003c\/p\u003e \u003cp\u003eOccupancy Percentages 26\u003c\/p\u003e \u003cp\u003eTrend, Seasonality, and Noise 28\u003c\/p\u003e \u003cp\u003eTrendline Test 33\u003c\/p\u003e \u003cp\u003eCycle Testing 33\u003c\/p\u003e \u003cp\u003eConclusion 34\u003c\/p\u003e \u003cp\u003eNote 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 \u003c\/b\u003e\u003cb\u003eCase Study 3—An Introduction to Regression Analysis and Its Application to the Measurement of Economic Damages 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is Regression Analysis and Where Have I Seen It Before? 37\u003c\/p\u003e \u003cp\u003eA Brief Introduction to Simple Linear Regression 38\u003c\/p\u003e \u003cp\u003eI Get Good Results with Average or Median Ratios—Why Should I Switch to Regression Analysis? 40\u003c\/p\u003e \u003cp\u003eHow Does One Perform a Regression Analysis Using Microsoft Excel? 43\u003c\/p\u003e \u003cp\u003eWhy Does Simple Linear Regression Rarely Give Us the Right Answer, and What Can We Do about It? 51\u003c\/p\u003e \u003cp\u003eShould We Treat the Value Driver Annual Revenue in the Same Manner as We Have Seller’s Discretionary Earnings? 60\u003c\/p\u003e \u003cp\u003eWhat are the Meaning and Function of the Regression Tool’s Summary Output? 68\u003c\/p\u003e \u003cp\u003eRegression Statistics 69\u003c\/p\u003e \u003cp\u003eTests and Analysis of Residuals 75\u003c\/p\u003e \u003cp\u003eTesting the Linearity Assumption 77\u003c\/p\u003e \u003cp\u003eTesting the Normality Assumption 78\u003c\/p\u003e \u003cp\u003eTesting the Constant Variance Assumption 80\u003c\/p\u003e \u003cp\u003eTesting the Independence Assumption 83\u003c\/p\u003e \u003cp\u003eTesting the No Errors-in-Variables Assumption 84\u003c\/p\u003e \u003cp\u003eTesting the No Multicollinearity Assumption 84\u003c\/p\u003e \u003cp\u003eConclusion 87\u003c\/p\u003e \u003cp\u003eNote 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 \u003c\/b\u003e\u003cb\u003eCase Study 4—Choosing a Sales Forecasting Model: A Trial and Error Process 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCorrelation with Industry Sales 89\u003c\/p\u003e \u003cp\u003eConversion to Quarterly Data 89\u003c\/p\u003e \u003cp\u003eQuadratic Regression Model 92\u003c\/p\u003e \u003cp\u003eProblems with the Quarterly Quadratic Model 92\u003c\/p\u003e \u003cp\u003eSubstituting a Monthly Quadratic Model 94\u003c\/p\u003e \u003cp\u003eConclusion 95\u003c\/p\u003e \u003cp\u003eNote 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 \u003c\/b\u003e\u003cb\u003eCase Study 5—Time Series Analysis with Seasonal Adjustment 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploratory Data Analysis 101\u003c\/p\u003e \u003cp\u003eSeasonal Indexes versus Dummy Variables 102\u003c\/p\u003e \u003cp\u003eCreation of the Optimized Seasonal Indexes 103\u003c\/p\u003e \u003cp\u003eCreation of the Monthly Time Series Model 108\u003c\/p\u003e \u003cp\u003eCreation of the Composite Model 108\u003c\/p\u003e \u003cp\u003eConclusion 115\u003c\/p\u003e \u003cp\u003eNotes 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 \u003c\/b\u003e\u003cb\u003eCase Study 6—Cross-Sectional Regression Combined with Seasonal Indexes to Determine Lost Profits 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOutline of the Case 117\u003c\/p\u003e \u003cp\u003eTesting for Noise in the Data 119\u003c\/p\u003e \u003cp\u003eConverting to Quarterly Data 119\u003c\/p\u003e \u003cp\u003eOptimizing Seasonal Indexes 119\u003c\/p\u003e \u003cp\u003eExogenous Predictor Variable 124\u003c\/p\u003e \u003cp\u003eInterrupted Time Series Analysis 124\u003c\/p\u003e \u003cp\u003e “But For” Sales Forecast 126\u003c\/p\u003e \u003cp\u003eTransforming the Dependent Variable 130\u003c\/p\u003e \u003cp\u003eDealing with Mitigation 130\u003c\/p\u003e \u003cp\u003eComputing Saved Costs and Expenses 133\u003c\/p\u003e \u003cp\u003eConclusion 137\u003c\/p\u003e \u003cp\u003eNote 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 \u003c\/b\u003e\u003cb\u003eCase Study 7—Measuring Differences in Pre- and Postincident Sales Using Two Sample t-Tests versus Regression Models 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreliminary Tests of the Data 139\u003c\/p\u003e \u003cp\u003eUsing the t-Test Two Sample Assuming Unequal Variances Tool 141\u003c\/p\u003e \u003cp\u003eRegression Approach to the Problem 141\u003c\/p\u003e \u003cp\u003eA New Data Set—Different Results 143\u003c\/p\u003e \u003cp\u003eSelecting the Appropriate Regression Model 143\u003c\/p\u003e \u003cp\u003eFinding the Facts Behind the Figures 148\u003c\/p\u003e \u003cp\u003eConclusion 151\u003c\/p\u003e \u003cp\u003eNotes 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 \u003c\/b\u003e\u003cb\u003eCase Study 8—Interrupted Time Series Analysis, Holdback Forecasting, and Variable Transformation 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraph Your Data 155\u003c\/p\u003e \u003cp\u003eIndustry Comparisons 155\u003c\/p\u003e \u003cp\u003eAccounting for Seasonality 157\u003c\/p\u003e \u003cp\u003eAccounting for Trend 161\u003c\/p\u003e \u003cp\u003eAccounting for Interventions 161\u003c\/p\u003e \u003cp\u003eForecasting “Should Be” Sales 164\u003c\/p\u003e \u003cp\u003eTesting the Model 167\u003c\/p\u003e \u003cp\u003eFinal Sales Forecast 169\u003c\/p\u003e \u003cp\u003eConclusion 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 \u003c\/b\u003e\u003cb\u003eCase Study 9—An Exercise in Cost Estimation to Determine Saved Expenses 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassifying Cost Behavior 171\u003c\/p\u003e \u003cp\u003eAn Arbitrary Classification 172\u003c\/p\u003e \u003cp\u003eGraph Your Data 172\u003c\/p\u003e \u003cp\u003eTesting the Assumption of Significance 174\u003c\/p\u003e \u003cp\u003eExpense Drivers 174\u003c\/p\u003e \u003cp\u003eConclusion 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 \u003c\/b\u003e\u003cb\u003eCase Study 10—Saved Expenses, Bivariate Model Inadequacy, and Multiple Regression Models 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraph Your Data 179\u003c\/p\u003e \u003cp\u003eRegression Summary Output of the First Model 181\u003c\/p\u003e \u003cp\u003eSearch for Other Independent Variables 183\u003c\/p\u003e \u003cp\u003eRegression Summary Output of the Second Model 185\u003c\/p\u003e \u003cp\u003eConclusion 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 \u003c\/b\u003e\u003cb\u003eCase Study 11—Analysis of and Modification to Opposing Experts’ Reports 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBackground Information 189\u003c\/p\u003e \u003cp\u003eStipulated Facts and Data 190\u003c\/p\u003e \u003cp\u003eThe Flaw Common to Both Experts 194\u003c\/p\u003e \u003cp\u003eDefendant’s Expert’s Report 196\u003c\/p\u003e \u003cp\u003ePlaintiff’s Expert’s Report 199\u003c\/p\u003e \u003cp\u003eThe Modified-Exponential Growth Curve 201\u003c\/p\u003e \u003cp\u003eFour Damages Models 208\u003c\/p\u003e \u003cp\u003eConclusion 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 \u003c\/b\u003e\u003cb\u003eCase Study 12—Further Considerations in the Determination of Lost Profits 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Review of Methods of Loss Calculation 210\u003c\/p\u003e \u003cp\u003eA Case Study: Dunlap Drive-In Diner 211\u003c\/p\u003e \u003cp\u003eSkeptical Analysis Using the Fraud Theory Approach 212\u003c\/p\u003e \u003cp\u003eRevenue Adjustment 212\u003c\/p\u003e \u003cp\u003eOfficer’s Compensation Adjustment 214\u003c\/p\u003e \u003cp\u003eContinuing Salaries and Wages (Payroll) Adjustment 215\u003c\/p\u003e \u003cp\u003eRent Adjustment 215\u003c\/p\u003e \u003cp\u003eEmployee Bonus 216\u003c\/p\u003e \u003cp\u003eDiscussion 216\u003c\/p\u003e \u003cp\u003eConclusion 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 \u003c\/b\u003e\u003cb\u003eCase Study 13—A Simple Approach to Forecasting Sales 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMonth Length Adjustment 221\u003c\/p\u003e \u003cp\u003eGraph Your Data 221\u003c\/p\u003e \u003cp\u003eWorksheet Setup 222\u003c\/p\u003e \u003cp\u003eFirst Forecasting Method 227\u003c\/p\u003e \u003cp\u003eSecond Forecasting Method 227\u003c\/p\u003e \u003cp\u003eSelection of Length of Prior Period 228\u003c\/p\u003e \u003cp\u003eReasonableness Test 228\u003c\/p\u003e \u003cp\u003eConclusion 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 \u003c\/b\u003e\u003cb\u003eCase Study 14—Data Analysis Tools for Forecasting Sales 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNeed for Analytical Tests 231\u003c\/p\u003e \u003cp\u003eGraph Your Data 231\u003c\/p\u003e \u003cp\u003eStatistical Procedures 233\u003c\/p\u003e \u003cp\u003eTests for Randomness 235\u003c\/p\u003e \u003cp\u003eTests for Trend and Seasonality 240\u003c\/p\u003e \u003cp\u003eTesting for Seasonality and Trend with a Regression Model 246\u003c\/p\u003e \u003cp\u003eConclusion 249\u003c\/p\u003e \u003cp\u003eNotes 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 \u003c\/b\u003e\u003cb\u003eCase Study 15—Determining Lost Sales with Stationary Time Series Data 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePrediction Errors and Their Measurement 251\u003c\/p\u003e \u003cp\u003eMoving Averages 252\u003c\/p\u003e \u003cp\u003eArray Formulas 254\u003c\/p\u003e \u003cp\u003eWeighted Moving Averages 256\u003c\/p\u003e \u003cp\u003eSimple Exponential Smoothing 260\u003c\/p\u003e \u003cp\u003eSeasonality with Additive Effects 263\u003c\/p\u003e \u003cp\u003eSeasonality with Multiplicative Effects 268\u003c\/p\u003e \u003cp\u003eConclusion 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 \u003c\/b\u003e\u003cb\u003eCase Study 16—Determining Lost Sales Using Nonregression Trend Models 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhen Averaging Techniques are Not Appropriate 273\u003c\/p\u003e \u003cp\u003eDouble Moving Average 275\u003c\/p\u003e \u003cp\u003eDouble Exponential Smoothing (Holt’s Method) 277\u003c\/p\u003e \u003cp\u003eTriple Exponential Smoothing (Holt-Winter’s Method) for Additive Seasonal Effects 279\u003c\/p\u003e \u003cp\u003eTriple Exponential Smoothing (Holt-Winter’s Method) for Multiplicative Seasonal Effects 285\u003c\/p\u003e \u003cp\u003eConclusion 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix \u003c\/b\u003e\u003cb\u003eThe Next Frontier in the Application of Statistics 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Technology 291\u003c\/p\u003e \u003cp\u003eEViews 291\u003c\/p\u003e \u003cp\u003eMinitab 292\u003c\/p\u003e \u003cp\u003eNCSS 292\u003c\/p\u003e \u003cp\u003eThe R Project for Statistical Computing 293\u003c\/p\u003e \u003cp\u003eSAS 294\u003c\/p\u003e \u003cp\u003eSPSS 295\u003c\/p\u003e \u003cp\u003eStata 296\u003c\/p\u003e \u003cp\u003eWINKS SDA 7 Professional 298\u003c\/p\u003e \u003cp\u003eConclusion 299\u003c\/p\u003e \u003cp\u003eBibliography of Suggested Statistics Textbooks 301\u003c\/p\u003e \u003cp\u003eGlossary of Statistical Terms 303\u003c\/p\u003e \u003cp\u003eAbout the Authors 317\u003c\/p\u003e \u003cp\u003eIndex 319\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMARK G. FILLER\u003c\/b\u003e, \u003cb\u003eCPA\/ABV, CBA, AM, CVA,\u003c\/b\u003e is President of Filler \u0026amp; Associates, a valuation and litigation support practice. He recently was also chair of the editorial board of NACVA's \u003ci\u003eThe Valuation Examiner\u003c\/i\u003e and coauthor of NACVA's quarterly marketing newsletter \u003ci\u003eInsights on Valuation\u003c\/i\u003e. 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJAMES A. D\u003csmall\u003eI\u003c\/small\u003eGABRIELE, P\u003csmall\u003eH\u003c\/small\u003eD\/DPS, CPA\/ABV, CFF, CFE, CFSA, CR.FA, CVA,\u003c\/b\u003e is a professor of accounting at Montclair State University and has been published in various journals, including \u003ci\u003eJournal of\u003c\/i\u003e \u003ci\u003eForensic Accounting, Journal of Business Valuation and Economic Loss Analysis,\u003c\/i\u003e and \u003ci\u003eThe Value Examiner.\u003c\/i\u003e Dr. DiGabriele is also Managing Director of DiGabriele, McNulty, Campanella \u0026amp; Co., LLC, an accounting firm specializing in forensic\/investigative accounting and litigation support.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA Quantitative Approach to Commercial Damages\u003c\/b\u003e \u003ci\u003eApplying Statistics to the Measurement of Lost Profits\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eThere was a fire. The damages are extensive, and the restaurant will be closed for at least two months. It's your job to calculate the recoverable economic losses, whether stream of lost profits or lost value of the business. The problem is you're not entirely up to speed on the most sophisticated and flexible statistical techniques and tools currently available. Written for practitioners who have some experience in the field of calculating economic damages but who need new tools, \u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e provides an introduction and a \"how to\" of some basic statistical techniques to help you establish a precise lost profits analysis.?? \u003c\/p\u003e\u003cp\u003eDemonstrating the application of the various statistical forecasting and analytical models, authors Mark Filler and James DiGabriele—leading forensic and valuation experts—present selected statistical techniques you can apply in lost profits cases. You'll discover new ways to integrate computing power and spreadsheets—especially in Excel and its add-in statistical tools—to quickly simplify complex financial calculations in preparing cases. \u003c\/p\u003e\u003cp\u003eSixteen real-world case studies show you how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eUse the standard deviation to determine if a number falls within an expected range based on past performance\u003c\/li\u003e \u003cli\u003eTest the sales history of the XYZ Motel to determine if there is an upward trend in the data\u003c\/li\u003e \u003cli\u003eForecast expected sales during the period of restoration using a time series regression model\u003c\/li\u003e \u003cli\u003eCompare pre- and post-incident sales and demonstrate techniques\u003c\/li\u003e \u003cli\u003eDetermine saved expenses and the issue of statistical significance vs. practical significance\u003c\/li\u003e \u003cli\u003eApply forensic accounting principles to a lost profits case\u003c\/li\u003e \u003cli\u003eAnalyze historical sales data searching for trend and seasonality\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eA companion website contains all the spreadsheets for the case studies. You can either create the spreadsheets from scratch, following the instructions contained in each chapter and using the website spreadsheets as guidelines, or simply download them from the website and start your own analysis immediately. \u003c\/p\u003e\u003cp\u003eDon't underestimate the value of your business loss. Get the tools to compute precise lost profits with \u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e. \t   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA Quantitative Approach to Commercial Damages\u003c\/b\u003e \u003ci\u003eApplying Statistics to theMeasurement of Lost Profits\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eThere was a fire. The damages are extensive, and the restaurant will be closed for at least two months. It's your job to calculate the recoverable economic losses, whether stream of lost profits or lost value of the business. The problem is you're not entirely up to speed on the most sophisticated and flexible statistical techniques and tools currently available. Written for practitioners who have some experience in the field of calculating economic damages but who need new tools, \u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e provides an introduction and a \"how to\" of some basic statistical techniques to help you establish a precise lost profits analysis. \u003c\/p\u003e\u003cp\u003eDemonstrating the application of the various statistical forecasting and analytical models, authors Mark Filler and James DiGabriele—leading forensic and valuation experts—present selected statistical techniques you can apply in lost profits cases. You'll discover new ways to integrate computing power and spreadsheets—especially in Excel and its add-in statistical tools—to quickly simplify complex financial calculations in preparing cases. \u003c\/p\u003e\u003cp\u003eSixteen real-world case studies show you how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eUse the standard deviation to determine if a number falls within an expected range based on past performance\u003c\/li\u003e \u003cli\u003eTest the sales history of the XYZ Motel to determine if there is an upward trend in the data\u003c\/li\u003e \u003cli\u003eForecast expected sales during the period of restoration using a time series regression model\u003c\/li\u003e \u003cli\u003eCompare pre- and post-incident sales and demonstrate techniques\u003c\/li\u003e \u003cli\u003eDetermine saved expenses and the issue of statistical significance vs. practical significance\u003c\/li\u003e \u003cli\u003eApply forensic accounting principles to a lost profits case\u003c\/li\u003e \u003cli\u003eAnalyze historical sales data searching for trend and seasonality\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eA companion website contains all the spreadsheets for the case studies. You can either create the spreadsheets from scratch, following the instructions contained in each chapter and using the website spreadsheets as guidelines, or simply download them from the website and start your own analysis immediately. \u003c\/p\u003e\u003cp\u003eDon't underestimate the value of your business loss. Get the tools to compute precise lost profits with \u003ci\u003eA Quantitative Approach to Commercial Damages\u003c\/i\u003e.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988643561701,"sku":"NP9781118072592","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118072592.jpg?v=1761781091","url":"https:\/\/k12savings.com\/products\/a-quantitative-approach-to-commercial-damages-website-isbn-9781118072592","provider":"K12savings","version":"1.0","type":"link"}