PLEASE UPLOAD R CODE SCREENSHOTS WITH A CLEAR EXPLANATION OF THE VALUES.

Using the dataset Aw ards.xds we want to understand what determines the number of awards earned by students at private college. Predictors of the number of awards earned include the type of program in which the student was enrolled (e.g., Arts, Business or Science) and the score on their exam in math. a) First estimate a linear regression model using OLS and check if the assumptions of OLS regression hold. b) Estimate a Poisson model using the glm function with the poisson specification. c) Estimate a Poisson using a quasipoisson model using the glm function with the quasipoisson specification. d) Compare the results from parts a,b and c. Are there any differences? If yes, why? \begin{tabular}{|r|r|l|l|} \hline id & num_awa prog & math \\ \hline 1 & 45 & 0 Arts & 41 \\ \hline 2 & 108 & 0 Business & 41 \\ \hline 3 & 15 & 0 Arts & 44 \\ \hline 4 & 67 & 0 Arts & 42 \\ \hline 5 & 153 & 0 Arts & 40 \\ \hline 6 & 51 & 0 Business & 42 \\ \hline 7 & 164 & 0 Arts & 46 \\ \hline 8 & 133 & 0 Arts & 40 \\ \hline 9 & 2 & 0 Arts & 33 \\ \hline 10 & 53 & 0 Arts & 46 \\ \hline 11 & 1 & 0 Arts & 40 \\ \hline 12 & 128 & 0 Science & 38 \\ \hline 13 & 16 & 0 Arts & 44 \\ \hline 14 & 106 & 0 Arts & 37 \\ \hline 15 & 89 & 0 Arts & 40 \\ \hline 16 & 134 & 0 Business & 39 \\ \hline 17 & 19 & 0 Business & 43 \\ \hline 18 & 145 & 0 Arts & 38 \\ \hline 19 & 11 & 0 Science & 45 \\ \hline 20 & 117 & 0 Arts & 39 \\ \hline 21 & 109 & 0 Business & 42 \\ \hline 22 & 12 & 0 Arts & 45 \\ \hline 23 & 37 & 0 Arts & 40 \\ \hline 24 & 69 & 0 Arts & 40 \\ \hline 25 & 43 & 0 Science & 43 \\ \hline 27 & 196 & 0 Science & 49 \\ \hline 28 & 36 & 1 Business & 44 \\ \hline \end{tabular}