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} = [90 + [91EDUC1' + 6'," where WAGE.' is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual ei 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 cross-sectional data set, you run an 0L5 regression to obtain the following parameter estimates: WAGE,- = 11.1 + 5.1 EDUC; If the standard error of the estimate of [31 is 1.04, then the true value ofl lies between v and v . As the number of observations in a data set grows, you would expect this range to v in size