I need help understanding this question. this is all of the information provided to me.

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 = l30 + l51 EDUCi + 52' where WAGEi is the hourly wage of person i (i.e., any specific person) and EDUCi is the number of years of education for that same person. The residual 55 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 crosssectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE,- : 11.5 + 6.4 EDUCi If the standard error of the estimate of l is 1.22, then the true value of l lies between V and V . As the number of observations in a data set grows, you would expect this range to V in size. STEP: 1 of2 Suppose that you are interested in determining the average height of a person in a large city. You begin by collecting the heights of a random sample of 256 people from the city. The average height of your sample is 65 inches, while the standard deviation of the heights in your sample is 4 inches. The standard error of your estimate of the average height in the city is 0.25 V/ . K Points: 1 / 1 Explanation: Open Explanation V