Suppose we are interested in exploring the relationship between city air particulate and rates of childhood asthma (data not from actual tests). we sample 15 cities for particulate measured in parts-per-million (ppm) of large particulate matter and for the rate of childhood asthma measured in whole percents. the data are as follows: particulate 11.6 15.9 15.7 7.9 6.3 13.7 13.1 10.8 6.0 7.6 14.8 7.4 16.2 13.1 11.2 asthma 14.5 16.6 16.5 12.6 12.0 15.8 15.1 14.2 12.2 13.1 16.0 13.5 16.4 15.4 14.4(a) plot the data as you see fit and interpret the results in the context of the problem.(b) build a linear regression model for the data and report the estimated values for the slope and intercept.(c) graphically assess whether the assumptions for linear regression are met.(d) conduct a formal test to determine whether or not the slope is significantly different form 0. if the slope is significant, can we, using only this statistical information, conclude that increased particulate concentration causes an increase in childhood asthma? (e) calculate and interpret the r2 value in the context of the problem.(f) suppose we sample a new city whose particulate is 13 ppm. if possible, create a 95% confidence intervalfor the predicted rate of childhood asthma in this city. if not possible, explain why.(g) if possible create a different 95% confidence interval for the average rate of childhood asthma among cities with 10 ppm of large particulate. if not possible, explain why.(h) if possible, create a different 95% confidence interval for the predicted rate of childhood asthma in a city with 3 ppm of large particulate. if not possible, explain why.