{"product_id":"a-quantitative-approach-to-commercial-damages-website-applying-statistics-to-the-measurement-of-lost-profits-hardback-9781118072592","title":"A Quantitative Approach to Commercial Damages, + Website; Applying Statistics to the Measurement of Lost Profits (Hardback) 9781118072592","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eA Quantitative Approach to Commercial Damages, + Website\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eApplying Statistics to the Measurement of Lost Profits\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMark G. Filler (Author), James A. DiGabriele (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118072592, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 May 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e26.2 x 18.5 x 2.8 cm, 0.726 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\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\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Finance \u0026amp; accounting [\u003ca title=\"See our other books on Finance \u0026amp; accounting\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Finance%20\u0026amp;%20accounting%20%5BKF%5D%22\"\u003eKF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52417750663448,"sku":"9781118072592","price":60.77,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118072592.jpg?v=1784506420","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/a-quantitative-approach-to-commercial-damages-website-applying-statistics-to-the-measurement-of-lost-profits-hardback-9781118072592","provider":"Freshly Printed Books","version":"1.0","type":"link"}