By Jay B. Abrams

ISBN-10: 0070002150

ISBN-13: 9780070002159

Abrams provides an enormous portion of rigor that's sorely missing between such a lot valuation execs.

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Expenses (Column C) is our Y (dependent) variable, and sales (Column T A B L E 2-2 OLS Regression: Example of Deviation from Mean A B C D E 5 Variable 6 7 8 9 10 11 12 13 F Observation 1 2 3 Year Y X x x2 Expenses Sales Deviation From Mean Squared Dev. From Mean 1994 $ 80,000 1996 $115,000 1997 $195,000 Total Average $100,000 $150,000 $250,000 $500,000 $166,667 $(66,667) $(16,667) $ 83,333 $ - 4,444,444,444 277,777,778 6,9444,444,444 11,666,666,667 9. Technically this is true only when the y-axis is placed through the mean of x.

Although the spreadsheet labels this statistic Multiple R, because our example is an OLS regression, it is simply R. 7. This obviously does not apply to start-ups. CHAPTER 2 Using Regression Analysis 29 timate. We can multiply the standard error of $16,014 (B23) by two to calculate an approximate 95% confidence interval for the regression estimate. 8 Dividing $64,000 by the mean of adjusted costs (approximately $1 million) leads to a 95% confidence interval that varies by about ‫ע‬3%, or 6% total.

80, is the variable cost per dollar of sales. 80. We show this relationship graphically at the bottom of the table. The diamonds are actual data points, and the line passing through them is the regression estimate. 5 3. The regression parameters a and b are often shown in statistical literature as ␣ and ␤ with a circumflex (ˆ) over each letter. 4. The interested reader should consult a statistics text for the multivariate calculus involved in calculating a and b. Mathematically, OLS calculates the line that minimizes the sum of the squared deviations between the actual data points and the regression estimate.

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Quantitative Business Valuation: A Mathematical Approach for Today's Professionals by Jay B. Abrams


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