This data set provides selected county demographic information (CDI) for 440 of the most populous counties in the United States. Each line of the data set has an identification number with a county name and state abbreviation and provides information on 14 variables for a single county. Counties with missing data Identification Number were deleted from the data set. The information generally pertains to the years 1990 and 1992. The 17 variables are: County State Land Area Total Population Percent of Population aged 18-34 Percent of population 65 or older Number of active physicians (Y) Number of hospital beds Total serious crimes Percent high school graduates Percent bachelor's degrees Percent below poverty level Percent unemployment Per capita income Total personal income Geographic region (1 = Northeast, 2 = Midwest, 3 = South, 4 = West The goal is to model the number of physicians per 1000 inhabitants, using the other demographic variables. (1) Plot Number of active physicians against each of Total Population, Total personal income, per capita income , Total serious crimes and pop65plus. (2) Plot In (Number of active physicians) against the others (Total Population, Total personal income, per capita income , Total serious crimes and pop65plus). Does is seem reasonable to take the log? (3) a. Regress the In(number of active physicians) in turn on (SLR)each of the three predictor variables (total population, number of hospital beds, and total personal income). State the estimated regression functions. b. Plot the three estimated regression functions and data on separate graphs. Does a linear regression relation appear to provide a good fit for each of the three predictor variables? c. Calculate s(sgit(MSE)) for each of the three predictor variables. Which predictor variable leads to the smallest variability around the fitted regression line? d. Obtain Bonferroni joint confidence intervals for 60 and Bl using a 95 percent family confidence coefficient and interpret the interval for all the models. e. An investigator has suggested that for model with total population 60 should be-100 and B1 should be.0028. Do the joint confidence intervals in part (d) support this view? Discuss. f. Estimate the expected number of active physicians for counties with total population of X = 500, 1000, 5000 thousand with Bonferroni family confidence coefficient 0.90. Please provide R codes, thank you