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The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation

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The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE.- = so + ,6] EDUC. + s.- where WAGE.- is the hourly wage of person i [i.e., am:r specic person) and EDUCi is the number of 1.rears of education for that same person. The residual si encompasses other factors that inuence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a crosssectional data set, you run an DLS regression to obtain the following parameter estimates: WAGEi = 11.1 + 6.4 EDUCi [f the standard error of the estimate of 1 is 1.22, then the true value of ll lies between V and V . As the number of observations in a data set grows, you would expect this range to decrease v in size

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