STEP: 2 of 2 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: WAGEi = J31] -I- ll EBB-Ci -I- 8i where WAGE.- is the hourly wage of person i {i.e., any specic person) and EDUC; is the number of 1rears of education for that same person. ' residual s,- encompasses other factors that inuence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose 'd'iat after collecting a cross-sectional data set, you run an DLS regression to obtain the following parameter estimates: WAGEi = 11.1 i 5.4EDUCi If the standard error of the estimate of ,3] is 1.35, then the two value of ,3] lies between V and 'V . As the number of observe in a data set grows, you would expect this range to V in size