Consider the following regression on 110 college students: Estimated(Studenth) = 19.6 + 0.73(Midparh) , R = 0.45, SER= 2.0 %3D Standard errors are as hereunder: SE(intercept) = (7.2) SE(Midparh) =...

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Extracted text: Consider the following regression on 110 college students: Estimated(Studenth) = 19.6 + 0.73(Midparh) , R = 0.45, SER= 2.0 %3D Standard errors are as hereunder: SE(intercept) = (7.2) SE(Midparh) = (0.10) (Values in parentheses are heteroskedasticity-robust standard errors). where "Studenth" is the height of students in inches, and "Midparh" is the average of the parental heights. (a) Using a t-test approach and 5% level of significance, test if slope coefficient can be positive. Make sure you write both hypothesis claims properly. (b) If children, on average, were expected to be of the same height as their parents, then this would imply that the coefficient of intercept becomes zero and the coefficient of slope will be 1: (i) Test if the coefficient of intercept is zero at 1% level of significance. (ii) Test if the slope coefficient is 1 at 5% level of significance. (Note: the statistical table is attached hereto) (C) Repeat part (B)-(i) using the p-value approach. (d) Repeat part (B)-(ii) using the p-value approach.