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=B+B EDUC; + 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 ; encompasses other factors that influence 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 OLS regression to obt Following parameter estimates: WAGE,= -10.1+5.3 EDUC; If the standard error of the estimate of B is 1.34, then the true value of B lies between in a data set grows, you would expect this range to decrease in size. 4.63 3.29 2.62 3.96 and . As the number of observations and 7.98 7.98 6.64 5.97 . As the number of observati Grade Final (to complete this step and unlock the ne