Admissions directors of graduate business programs constantly review the criteria they use to make admission decisions. Suppose that the director of a particular top-20 MBA program is trying to understand how she and her staff discriminate between those who are admitted to their program and those who are rejected. To do this, she collects data on each of the following variables for 100 randomly selected individuals who applied in the past academic year: whether the applicant graduated in the top 10% of his or her undergraduate class, whether the admissions office interviewed the applicant in person or over the telephone, the applicant's overall GMAT score (where the highest possible score is 800), and the applicant's undergraduate grade-point average (standardized on a four-point scale). These data are provided in the file P17_03.xlsx.

Using the admissions data, discretize the GMAT Score and Undergraduate GPA variables according to their quartiles.

Enter the limits for each quartile of GMAT Score and Undergraduate GPA. If necessary, round the values to a whole number for GMAT and two decimal digits for Undergraduate GPA.

Using the admissions data, discretize the GMAT Score and Undergraduate GPA variables according to their quartiles.

Enter the limits for each quartile of GMAT Score and Undergraduate GPA. If necessary, round the values to a whole number for GMAT and two decimal digits for Undergraduate GPA.

	GMAT				Undergraduate GPA
Quartile	From		To		From		To
1st	509				3.07
2nd
3rd
4th			736				3.80

Then use the nave Bayes procedure to classify the 100 applicants as Yes or No for admittance. (Remember that classifications can be based on the probabilities themselves, not necessarily their logs.) Based on these results, consider a new customer: in top 10% of undergraduate class, did not interview with admissions officer, GMAT 580, GPA 3.29. How would you classify this applicant? SelectYesNoItem 13