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A Quantitative Approach to Commercial Damages, + Website

Applying Statistics to the Measurement of Lost Profits

Medium: Buch

ISBN: 978-1-118-07259-2

Verlag: Wiley

Erscheinungstermin: 08.05.2012

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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.

Produkteigenschaften

Artikelnummer: 9781118072592
Medium: Buch
ISBN: 978-1-118-07259-2
Verlag: Wiley
Erscheinungstermin: 08.05.2012
Sprache(n): Englisch
Auflage: 1. Auflage 2012
Produktform: Gebunden, + Website
Gewicht: 848 g
Seiten: 323
Format (B x H x T): 183 x 260 x 23 mm
Ausgabetyp: Kein, Unbekannt

Themen

Autoren

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

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