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YouTube T2-16 Regression: standard error of regression

Nicole Seaman

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The standard error of the regression (SER) is a key measure of the OLS regression line's "goodness of fit." The SER equals the square root of [sum of squared residuals (SSR) divided by the degrees of freedom (d.f.)], where d.f. is the number of observations minus the number of regression coefficients including the intercept; i.e., SER = sqrt[SSR/df] = sqrt[SSR/(n-1)]. Just as the sample standard deviation of the Y-variable is a univariate measure of its dispersion about its mean, the SER is a measure of the dispersion of observed (aka, actual) values about the regression line. Therefore, the units of the SER are the same as the units of the dependent variable (in this course, hourly wage in dollars).

Here is David's XLS: https://trtl.bz/2EhY121

 
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