Regression Analysis #2 (6 points) This part is based upon a birthweight dataset that we analyzed in class. Specifically, we have data on 1387 births. The following linear probability model (LPM) regression is used in order to explain whether or not a mother smokes during pregnancy (smoke = 1 if yes, 0 if no), where the explanatory variables are family income (faminc, measured in $1000's), mother's education (motheduc, measured in years of schooling), and race (white = 1 if mother is white, 0 otherwise). regr smoke faminc motheduc white, robust Linear regression Number of obs = 1387 F( 3, 1383) 35 .35 Prob > F 0 . 0000 R-squared = 0 . 0640 Root MSE . 34864 Robust smoke Coef. Std. Err. t P>It| [95% Conf. Interval] --+-- faminc - . 0022096 . 0005314 -4.16 0 . 000 - . 003252 - . 0011672 motheduc - . 0281384 . 0042072 -6. 69 0 . 000 - . 0363916 - . 0198852 white . 0443496 . 0244681 1 . 81 0 . 070 - . 0036491 . 0923483 cons . 5462237 . 0559525 9. 76 0 . 000 . 4364627 . 6559847(d) (2 points) Describe how you would conduct a "Chow test" to see if a different model for smoke should be specified for whites (white = 1) and non-whites (white = 0). Specifically, (a) what new LPM regression would you run and (b) what null hypothesis would you test for this new regression? (e) (1 point) A classmate suggests that you should use an alternative method to LPM, Probit model. What is the STATA command to implement it with the data