assisomentl. Contents A. Assignment B. Data description The German Credit data has data on lDIJIII past credit applicants, described by 3i] variables. Each applicant is rated as \"Good\" or \"Bad\" credit [encoded as l and {1 respectively in the Response variable}. We want to obtain a model that may be used to determine if new applicants present a good or had credit risk. The original data has been transformed to ease analysis in this initial assignment. It is informative to compare the original and transformed data descriptions to see how different variables have been transformed. Assigpment Questions Explore the data: What is the proportion of \"Good\" to \"Bad\" cases? Obtain descriptions of the predictor [independent] variables mean, standard deviations, etc. for realvalues attributes, frequencies of different category values. Anything noteworthy in the data? If you assume that the Amount: Age and Duration variables are normally distributed, then which of the observations you will consider as outliers for the data using properties of normal distribution on the data? On the basis of pure probabilities: which of the follovo'ng variables impact default maximum? CHI-LACET REAL_ESTAIE NEW_CAR EDUCAIIDN RETRMhTiG 1What would you conclude from the above