Use Rstudio to work this, need write for analysis result and R code.

9.2 The data in Table 9.13 are numbers of insurance policies, n, and num bers of claims, 3;, for cars in various insurance categories, CAR, tabulated by age of policy holder, AGE, and district where the policy holder lived (DIST = 1, for London and other major cities, and DIST = 0, other wise). The table is derived from the CLAIMS data set in Aitkin et al. (2005) obtained from a paper by Baxter et al. (1980). (a) Calculate the rate of claims y for each category and plot the rates by AGE, CAR and DIST to get an idea of the main effects of these factors. (b) Use Poisson regression to estimate the main eects (each treated as categorical and modelled using indicator variables) and interaction terms. (c) Based on the modelling in (b), Aitkin et al. (2005) determined that all the interactions were unimportant and decided that AGE and CAR could be treated as though they were continuous variables. Fit a model incorporating these features and compare it with the best model obtained in (b). What conclusions do you reach? qattachments_eb8fe1c857798ec2c97852d4e37f1ee5bf4c9517 car age district y n 1 1 0 65 317 1 2 0 65 476 1 3 0 52 486 1 4 0 310 3259 2 1 0 98 486 2 2 0 159 1004 2 3 0 175 1355 2 4 0 877 7660 3 1 0 41 223 3 2 0 117 539 3 3 0 137 697 3 4 0 477 3442 4 1 0 11 40 4 2 0 35 148 4 3 0 39 214 4 4 0 167 1019 1 1 1 2 20 1 2 1 5 33 1 3 1 4 40 1 4 1 36 316 2 1 1 7 31 2 2 1 10 81 2 3 1 22 122 2 4 1 102 724 3 1 1 5 18 3 2 1 7 39 3 3 1 16 68 3 4 1 63 344 4 1 1 0 3 4 2 1 6 16 4 3 1 8 25 4 4 1 33 114 Page 1 9.2 (a) Claim rates appear to increase with CAR, decrease with AGE and are higher for DIST = 1. (c) This model is simpler than (b), ts well (deviance = 53.11, d.f. = 60, pvalue = 0.72) and gives coefcients (standard errors): AGE, 0.177 (0.018); CAR, 0.198 (0.021); DIST, 0.210 (0.059), consistent with (a)