STEP: 2 01 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: WAGE' = '81] + [H] EDD-C" + E?" where WAGE.- is the hourly wage of person i (i.e., any specic person) and EDUC; is the number of years of education for that same person. The residual s,- 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 setI you run an DLS regression to obtain the following parameter estimates: WAGE; = 1[I.1 + 4.1 EDUCi If the standard error of the estimate of ll is 1.03, then the true value of ,31 lies between V and V . ts the number of observations in a data set grows, you would expect this range to V in size. TOTAL SCORE: 112 thonamplete'dlissnepandu'lockmenensnep}