a. Using the information in this dataset, if an accident has just been reported and no further information is available, what should the prediction be? (INJURY = Yes or No?) Why? Select the first 12 records in the dataset and look only at the response (INJURY) and the two predictors WEATHER_ R and TRAP ___CON R. I. ii. iii. iv. Create a pivot table that examines INJURY as a function of the two predictors for these 12 records. Use all three variables' in the pivot table as rows/columns. Compute the exact Bayes conditional probabilities of an injury (INJURY = Yes) given the six possible combinations of the predictors. Classify the 12 accidents using these probabilities and a cutoff of 0.5. Compute manually the naive Bayes conditional probability of an injury given WEATHERWR = 1 and TRAF_CON_R = 1. Run a naive Bayes classier on the 12 records and two predictors using R. Check the model output to obtain probabilities and classifications for all 12 records. Compare this to the exact Bayes classication. Are the resulting classications equivalent? Is the ranking (= ordering) of observations equivalent? C. Let us now return to the entire dataset. Partition the data into training (60%) and validation (40%). I. Assuming that no information or initial reports about the accident itself are available at the time of prediction (only location characteristics, weather conditions, etc), which predictors can we include in the analysis? (Use the Data_Codes sheet.)