A quantitative approach to commercial damages: applying statistics to the measurement of lost profits + website

How-to guidance for measuring lost profits due to business interruption damagesA 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 stepfrom 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 spreadsheetsOffers a step-by-step approach to computing damages using case studies and over 250 screen shotsOften 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. INDICE: PrefaceIs This A Course In Statistics?How This Book is SetupThe Job of the Testifying ExpertSpreadsheet AvailabilityAcknowledgementsIntroductionThe Application of Statistics to the Measurement of Damages for Lost ProfitsThe Three Big Statistical IdeasVariationCorrelationThe Concept of the Null HypothesisRejection Region, or AreaIntroduction to the Idea of Lost ProfitsStage 1.Calculating the Difference Between Those Revenues That Should Have Been Earned and What was Actually Earned During the Period of InterruptionStage 2. Analyzing Costs and Expenses to Separate Continuing from Non-ContinuingStage 3. Examining Continuing Expenses Patterns for Extra ExpenseStage 4. Computing the Actual Loss Sustained, or Lost ProfitsChoosing a Forecasting ModelType of InterruptionLength of Period of InterruptionAvailability of Historical DataRegularity of Sales Trends and PatternsEase of ExplanationConventional Forecasting ModelsSimple Arithmetic ModelsMore Complex Arithmetic ModelsTrend‑Line and Curve‑Fitting ModelsSeasonal Factor ModelsSmoothing MethodsMultiple Regression ModelsOther Applications of Statistical ModelsConclusionNotesChapter 1 Case Study 1 Uses of the Standard DeviationThe Steps of Data AnalysisShapeSpreadConclusionNotesChapter 2 Case Study 2 Trend and Seasonality AnalysisClaim SubmittedClaim ReviewOccupancy PercentagesTrend, Seasonality and NoiseTrendline TestCycleTestingConclusionNotesChapter 3 Case Study 3 An Introduction to Regression Analysis and Its Application to the Measurement of Economic DamagesWhat Is Regression Analysis and Where Have I Seen It Before?A Brief Introduction to Simple Linear RegressionI Get Good Results with Average or Median Ratios - Why ShouldI 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 AnnualRevenue in the Same Manner as We Have Seller's Discretionary Earnings?What isthe Meaning and Function of the Regression Tool's Summary Output?Regression StatisticsTests and Analysis of ResidualsTesting the Linearity AssumptionTesting the Normality AssumptionTesting the Constant Variance AssumptionTesting the Independence AssumptionTesting the No Errors-in-Variables AssumptionTesting the No Multicollinearity AssumptionConclusionChapter 4 Case Study 4 Choosing a Sales Forecasting Model: A Trial and Error ProcessCorrelation with Industry SalesConversion to Quarterly DataQuadratic Regression ModelProblems with the Quarterly Quadratic ModelSubstituting a Monthly Quadratic ModelConclusionNotesChapter 5 Case Study 5 Time Series Analysis with Seasonal AdjustmentExploratory Data AnalysisSeasonal Indices vs. Dummy VariablesCreation of the Optimized Seasonal IndicesCreation of the Monthly Time Series ModelCreation of the Composite ModelConclusionNotesChapter 6 Case Study 6 Cross Sectional Regression Combinedwith Seasonal Indices to Determine Lost ProfitsOutline of the CaseTesting forNoise in the DataConverting to Quarterly DataOptimizing Seasonal IndicesExogenous Predictor VariableInterrupted Time Series Analysis“But For” Sales ForecastTransforming the Dependent VariableDealing with MitigationComputing SavedCosts and ExpensesConclusionNotesChapter 7 Case Study 7 Measuring Differencesin Pre- and Post- Incident Sales Using Two Sample t-Tests versus Regression ModelsPreliminary Tests of the DataSelecting the Appropriate Regression ModelFinding the Facts Behind the FiguresConclusionNotesChapter 8 Case Study 8 Interrupted Time Series Analysis, Holdback Forecasting and Variable TransformationGraph Your DataIndustry ComparisonsAccounting for SeasonalityAccounting for TrendAccounting for InterventionsForecasting “Should Be” SalesTesting the ModelFinal Sales ForecastConclusionChapter 9 Case Study 9 An Exercise in Cost Estimation to Determine Saved ExpensesClassifying Cost BehaviorAn Arbitrary ClassificationGraph Your DataTesting the Assumption of SignificanceExpense DriversConclusionChapter 10 Case Study 10 Saved Expenses, Bivariate Model Inadequacy, and Multiple Regression ModelsGraph Your DataRegression Summary Output of the First ModelSearch for Other Independent VariablesRegression Summary Output of the Second ModelConclusionChapter 11 Case Study 11 Analysis of and Modificationto Opposing Experts' ReportsBackground InformationStipulated Facts and DataThe Flaw Common to Both ExpertsDefendant's Expert's ReportPlaintiff's Expert's ReportThe Modified-Exponential Growth CurveFour Damages ModelsConclusionChapter12 Case Study 12 Further Considerations in the Determination of Lost ProfitsAReview of Methods of Loss CalculationA Case Study Dunlap Drive-in-DinerSkeptical Analysis using the Fraud Theory ApproachRevenue AdjustmentFraud Theory ApproachDeterminationOfficer's Compensation AdjustmentFraud Theory ApproachDeterminationContinuing Salaries and Wages (Payroll) AdjustmentFraud Theory ApproachDeterminationRent AdjustmentFraud Theory ApproachDeterminationEmployee BonusFraud Theory ApproachDeterminationDiscussionConclusionChapter 13 Case Study 13 ASimple Approach to Forecasting SalesMonth Length AdjustmentGraph Your DataWorksheet SetupFirst Forecasting MethodSecond Forecasting MethodSelection of Length of Prior PeriodReasonableness TestConclusionChapter 14 Case Study 14 Data Analysis Tools for Forecasting SalesNeed for Analytical TestsGraph Your DataStatistical ProceduresTests for RandomnessTests for Trend and SeasonalityTesting for Seasonality and Trend with a Regression ModelConclusionNotesChapter 15 Case Study 15 Determining Lost Sales with Stationary Time Series DataPrediction Errors and Their MeasurementMoving AveragesArray FormulasWeighted Moving AveragesSimple Exponential SmoothingSeasonality with Additive EffectsSeasonality with Multiplicative EffectsConclusionChapter 16 Case Study 16 Determining Lost Sales Using Non-Regression Trend ModelsWhen Averaging Techniques Are Not AppropriateDouble Moving AverageDouble Exponential Smoothing (Holt's Method)Triple Exponential Smoothing (Holt-Winter's Method) For Additive Seasonal EffectsTripleExponential Smoothing (Holt-Winter's Method) For Multiplicative Seasonal EffectsConclusionAppendix The Next Frontier in the Application of StatisticsThe TechnologyEViewsMinitabNCSSSales Ratio ReportsComparables ReportsHybrid Appraisal ModelThe R Project for Statistical ComputingSASSPSSSTATAWINKS SDA 7 PROFESSIONALConclusion /DiscussionBibliography of Suggested Statistics TextbooksGlossary of Statistical TermsAbout the AuthorsIndex

• ISBN: 978-1-118-07259-2
• Editorial: John Wiley & Sons
• Encuadernacion: Rústica
• Páginas: 352
• Fecha Publicación: 23/05/2012
• Nº Volúmenes: 1
• Idioma: Inglés