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| Preface | |
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| Is This a Course in Statistics? | |
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| How This Book Is Set Up | |
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| The Job of the Testifying Expert | |
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| About the Companion Web Site-Spreadsheet Availability | |
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| Note | |
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| Acknowledgments | |
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| Introduction The Application of Statistics to the Measurement of Damages for Lost Profits | |
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| The Three Big Statistical Ideas | |
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| Variation | |
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| Correlation | |
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| Rejection Region or Area | |
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| Introduction to the Idea of Lost Profits | |
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| Calculating the Difference Between Those Revenues That Should Have Been Earned and What Was Actually Earned During the Period of Interruption | |
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| Analyzing Costs and Expenses to Separate Continuing from Noncontinuing | |
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| Examining Continuing Expenses Patterns for Extra Expense | |
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| Computing the Actual Loss Sustained or Lost Profits | |
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| Choosing a Forecasting Model | |
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| Type of Interruption | |
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| Length of Period of Interruption | |
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| Availability of Historical Data | |
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| Regularity of Sales Trends and Patterns | |
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| Ease of Explanation | |
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| Conventional Forecasting Models | |
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| Simple Arithmetic Models | |
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| More Complex Arithmetic Models | |
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| Trendline and Curve-Fitting Models | |
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| Seasonal Factor Models | |
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| Smoothing Methods | |
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| Multiple Regression Models | |
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| Other Applications of Statistical Models | |
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| Conclusion | |
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| Notes | |
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| Case Study 1-Uses of the Standard Deviation | |
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| The Steps of Data Analysis | |
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| Shape | |
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| Spread | |
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| Conclusion | |
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| Notes | |
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| Case Study 2-Trend and Seasonality Analysis | |
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| Claim Submitted | |
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| Claim Review | |
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| Occupancy Percentages | |
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| Trend, Seasonality, and Noise | |
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| Trendline Test | |
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| Cycle Testing | |
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| Conclusion | |
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| Note | |
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| Case Study 3-An Introduction to Regression Analysis and Its Application to the Measurement of Economic Damages | |
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| What Is Regression Analysis and Where Have I Seen It Before? | |
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| A Brief Introduction to Simple Linear Regression | |
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| I Get Good Results with Average or Median Ratios-Why Should I Switch to Regression Analysis? | |
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| How Does One Perform a Regression Analysis Using Microsoft Excel? | |
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| Why Does Simple Linear Regression Rarely Give Us the Right Answer, and What Can We Do about It? | |
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| Should We Treat the Value Driver Annual Revenue in the Same Manner as We Have Seller's Discretionary Earnings? | |
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| What Are the Meaning and Function of the Regression Tool's Summary Output' | |
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| Regression Statistics | |
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| Tests and Analysis of Residuals | |
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| Testing the Linearity Assumption | |
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| Testing the Normality Assumption | |
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| Testing the Constant Variance Assumption | |
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| Testing the Independence Assumption | |
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| Testing the No Errors-in-Variables Assumption | |
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| Testing the No Multicollinearity Assumption | |
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| Conclusion | |
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| Note | |
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| Case Study 4-Choosing a Sales Forecasting Model: A Trial and Error Process | |
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| Correlation with Industry Sales | |
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| Conversion to Quarterly Data | |
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| Quadratic Regression Model | |
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| Problems with the Quarterly Quadratic Model | |
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| Substituting a Monthly Quadratic Model | |
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| Conclusion | |
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| Note | |
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| Case Study 5-Time Series Analysis with Seasonal Adjustment | |
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| Exploratory Data Analysis | |
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| Seasonal Indexes versus Dummy Variables | |
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| Creation of the Optimized Seasonal Indexes | |
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| Creation of the Monthly Time Series Model | |
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| Creation of the Composite Model | |
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| Conclusion | |
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| Notes | |
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| Case Study 6-Cross-Sectional Regression Combined with Seasonal Indexes to Determine Lost Profits | |
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| Outline of the Case | |
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| Testing for Noise in the Data | |
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| Converting to Quarterly Data | |
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| Optimizing Seasonal Indexes | |
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| Exogenous Predictor Variable | |
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| Interrupted Time Series Analysis | |
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| "But For" Sales Forecast | |
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| Transforming the Dependent Variable | |
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| Dealing with Mitigation | |
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| Computing Saved Costs and Expenses | |
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| Conclusion | |
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| Note | |
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| Case Study 7-Measuring Differences in Pre- and Postincident Sales Using Two Sample t-Tests versus Regression Models | |
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| Preliminary Tests of the Data | |
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| Using the t-Test Two Sample Assuming Unequal Variances Tool | |
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| Regression Approach to the Problem | |
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| A New Data Set-Different Results | |
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| Selecting the Appropriate Regression Model | |
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| Finding the Facts Behind the Figures | |
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| Conclusion | |
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| Notes | |
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| Case Study 8-Interrupted Time Series Analysis, Holdback Forecasting, and Variable Transformation | |
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| Graph Your Data | |
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| Industry Comparisons | |
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| Accounting for Seasonality | |
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| Accounting for Trend | |
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| Accounting for Interventions | |
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| Forecasting "Should Be" Sales | |
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| Testing the Model | |
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| Final Sales Forecast | |
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| Conclusion | |
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| Case Study 9-An Exercise in Cost Estimation to Determine Saved Expenses | |
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| Classifying Cost Behavior | |
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| An Arbitrary Classification | |
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| Graph Your Data | |
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| Testing the Assumption of Significance | |
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| Expense Drivers | |
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| Conclusion | |
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| Case Study 10-Saved Expenses, Bivariate Model Inadequacy, and Multiple Regression Models | |
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| Graph Your Data | |
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| Regression Summary Output of the First Model | |
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| Search for Other Independent Variables | |
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| Regression Summary Output of the Second Model | |
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| Conclusion | |
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| Case Study 11-Analysis of and Modification to Opposing Experts' Reports | |
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| Background Information | |
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| Stipulated Facts and Data | |
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| The Flaw Common to Both Experts | |
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| Defendant's Expert's Report | |
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| Plaintiffs Expert's Report | |
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| The Modified-Exponential Growth Curve | |
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| Four Damages Models | |
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| Conclusion | |
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| Case Study 12-Further Considerations in the Determination of Lost Profits | |
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| A Review of Methods of Loss Calculation | |
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| A Case Study: Dunlap Drive-in Diner | |
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| Skeptical Analysis Using the Fraud Theory Approach | |
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| Revenue Adjustment | |
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| Officer's Compensation Adjustment | |
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| Continuing Salaries and Wages (Payroll) Adjustment | |
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| Rent Adjustment | |
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| Employee Bonus | |
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| Discussion | |
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| Conclusion | |
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| Case Study 13-A Simple Approach to Forecasting Sales | |
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| Month Length Adjustment | |
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| Graph Your Data | |
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| Worksheet Setup | |
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| First Forecasting Method | |
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| Second Forecasting Method | |
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| Selection of Length of Prior Period | |
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| Reasonableness Test | |
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| Conclusion | |
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| Case Study 14-Data Analysis Tools for Forecasting Sales | |
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| Need for Analytical Tests | |
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| Graph Your Data | |
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| Statistical Procedures | |
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| Tests for Randomness | |
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| Tests for Trend and Seasonality | |
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| Testing for Seasonality and Trend with a Regression Model | |
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| Conclusion | |
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| Notes | |
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| Case Study 15-Determining Lost Sales with Stationary Time Series Data | |
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| Prediction Errors and Their Measurement | |
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| Moving Averages | |
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| Array Formulas | |
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| Weighted Moving Averages | |
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| Simple Exponential Smoothing | |
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| Seasonality with Additive Effects | |
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| Seasonality with Multiplicative Effects | |
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| Conclusion | |
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| Case Study 16-Determining Lost Sales Using Nonregression Trend Models | |
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| When Averaging Techniques Are Not Appropriate | |
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| Double Moving Average | |
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| Double Exponential Smoothing (Holt's Method) | |
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| Triple Exponential Smoothing (Holt-Winter's Method) for Additive Seasonal Effects | |
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| Triple Exponential Smoothing (Holt-Winter's Method) for Multiplicative Seasonal Effects | |
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| Conclusion | |
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| Appendix The Next Frontier in the Application of Statistics | |
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| The Technology | |
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| EViews | |
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| Minitab | |
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| NCSS | |
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| The R Project for Statistical Computing | |
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| SAS | |
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| SPSS | |
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| Stata | |
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| WINKS SDA 7 Professional | |
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| Conclusion | |
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| Bibliography of Suggested Statistics Textbooks | |
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| Glossary of Statistical Terms | |
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| About the Authors | |
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| Index | |