in the independent variable (the return on the S&P 500 index). Recall from Section 10.1 that the part of the total variance of the rate of return on an asset, 2, that is explained by market returns is the systematic variance, 2 2 . Hence the R-square is sys- tematic variance over total variance, which tells us what fraction of a firms volatility is at- tributable to market movements:   2 2 R2 M 2   The firm-specific variance, 2(e), is the part of the asset variance that is unexplained by the market index. Therefore, because   2 2 2 2(e)   the coefficient of determination also may be expressed as III. Equilibrium In Capital Markets 10. Single−Index and Multifactor Models The McGraw−Hill Companies, 2001           306 PART III Equilibrium in Capital Markets     R2 1 (e) 2   (10.13)   Accordingly, the column following R-SQR reports the standard deviation of the non- systematic component, (e), calling it RESID STD DEV-N, in reference to the fact that the e is estimated from the regression residuals. This variable is an estimate of firm-specific risk. The following two columns appear under the heading of Standard Error. These are sta- tistics that allow us to test the precision and significance of the regression coefficients. The standard error of an estimate is the standard deviation of the possible estimation error of the coefficient. A rule of thumb is that if an estimated coefficient is less than twice its standard error, we cannot reject the hypothesis that the true coefficient is zero. The ratio of the coefficient to its standard error is the t-statistic. A t-statistic greater than 2 is the tra- ditional cutoff for statistical significance. The two columns of the standard error of the es- timated beta and alpha allow us a quick check on