Need help answering question C. Questions A and B are already answered.

1. Linear Regression Use NHANSZ data to develop a regression model that studies how various socio-economic and health factors affect an individual's systolic blood pressure (bpsystol). These socio-economic and health factors include gender (female), race (race), age (age), whether they live in a rural area or not (rural), body mass index (bmz'), and whether or not they have diabetes (diabetes). . regress bpsystel female race age rural bni diabetes Source 55 at MS Number of obs = 10.349 Fl, 10342} = 795.17 Model 1770364.01 6 296394.001 Prob > F = 0.0000 Residual 3054916.04 10.342 372.743767 R-squared : 0.315? Adj Rsquared = 0.3153 Total 5033200.05 10,340 544.30340 Root HSE = 19.307 bpsystol Coefficient Std. err. t P>|t| [95% conf. Interval] female -4.104604 .300356 -10.70 0.000 -4.050235 -3.359093 race 1.705140 .4006034 3.71 0.000 .0430720 2.727224 age .5014593 .0113566 51.20 0.000 .5591903 .6037204 rural -.2942360 .4006902 -0.73 0.463 -1.079603 .4912092 bmi 1.20043? .0393??? 32.52 0.000 1.203249 1.357625 diabetes 5.30211 .9034205 5.0? 0.000 3.531231 ?.0?2909 _tons 70.40635 1.230770 57.27 0.000 00.07370 72.00001 a. Write down the regression model in the form ofy = B" + [31x] + [32x2 +. . .+ [3ka + a by using actual variables names instead of y or x. Please see lecture slides for examples. i. BPSYSTOL = [30+ [31 FEMALE + [32 RACE + [33 AGE + B4 RURAL + [35 BMI + [36 DIABETES + e b. Given the model in (a), which estimation approach is most appropriate for this model? Explain. i. The least squares principle would be the best estimation method for this model. This would be the best approach because it aims to minimize the errors between the predicted and observed variables in order to obtain the best regression line. Furthermore, because the goal of regression is to predict as closely as possible to the realworld or observed value, the least squares method is used to minimize that difference or error. 0. Estimate the model in (a) using the regression approach chosen in step (b) above. Display regression table and discuss the statistical signicance and meaning of each (statistically signicant) coefcient from this regression