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Analytics Case Competition 2013

Analytics Case Competition 2013. Team Name : Regressors. Case Analysis : Bridgei2i. Nishu Navneet (12125032), MBA Sunny Goyal (12125047), MBA. Approach. Data Set Operation Missing Data Data Preparation Segmentation Data Analysis Tools Used – SPSS, MS-Excel

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Analytics Case Competition 2013

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  1. Analytics Case Competition 2013 Team Name : Regressors Case Analysis : Bridgei2i Nishu Navneet (12125032), MBA Sunny Goyal (12125047), MBA

  2. Approach Data Set Operation Missing Data Data Preparation Segmentation Data Analysis Tools Used – SPSS, MS-Excel Binary Logistic Regression Model Specification Significant Variable Identification Backward Elimination Result & Interpretation Recommendation

  3. Data Set Operation • Initial Total Sample Size – 3400 • Missing Data • Replacement • Randomization (Limits - ±3 Sigma) • Linear Regression Substitution(STDNT_TEST_ENTRANCE_COMB,DISTANCE_FROM_HOME) • Removal • Case-Wise • CORE_COURSE_NAME_1_F – (NotRep/Incompl) – 208 cases • HIGH_SCHL_NAME – (#NA) – 1 case • Discrepancy • 2nd Semester – (Grade attempted != 0 but Number_ofSubjects_Attempted = 0) – 21 Cases • Operational Sample Size – 3170 • Data Preparation • CGPA Calculation (CORE_COURSE_GRADE- A-5,B-4,C-3,D-2,F-1) • Semester Hours Ratio(TERM_EARNED_HRS / TERM_ATTEMPT_HRS) • K-Means Cluster Analysis (HIGH_SCHL_NAME– 14 Clusters) • Categorical - STDNT_MAJOR(4) , UNMET_NEED (2) , STDNT_AGE (3)

  4. Segmentation • Clustering (K-Means Cluster Analysis) • STDNT_MAJOR(4 Clusters ) • Ratio of Students Dropping Out = RETURNED_2ND_YR(0) / RETURNED_2ND_YR(0+1) • Ratio of Students Opting for Major = No. of Students Opting for a Particular major / Total Students • HIGH_SCHL_NAME(4 Clusters) • Ratio of Students Dropping Out= RETURNED_2ND_YR(0) / RETURNED_2ND_YR(0+1) • Ratio of Students from Schools = Number of Students from a Particular School / Total Students • Percentage Students Dropping Out • Percentage Students Dropping Out • % Students from Schools • % Students Opting for Majors

  5. Data Analysis Binomial Logistic Regression Significant Variables

  6. Model Equation -> log((1 – P)/P)= With Probability Cut Off as .5, the Overall Percentage becomes 85.6%.

  7. Interpretations • With other variables remaining constant- • Odds of attrition amongst female is 1.5 times that of male. • Odds of attrition amongst students with 0 and 1 number of subjects in second semester is 5.8 and 1.3 times that of higher number of subjects(>1). • Odds of attrition amongst On Campus students is 1.43 times that of Off Campus students. • Odds of attrition in Students opting for Major Cluster 2,3 is 4 and 2.7 times that of students opting Major Cluster 1. • Odds of attrition in Students with Backgrounds as BGD 1,5 and 7 are 1.9, 5.2 and 3 times that of students with Backgrounds as BGD3. • Odds of attrition decreases with increase in CGPA for 1st and 2nd Semester but this effect is higher with 2nd Semester CGPA. • Odds of attrition amongst Students with 1st and 3rd Cluster of High_School_Name is very high compared to Cluster 4th.

  8. Recommendations • Students opting for no subject in second semester should be given extra attention as their attrition rate is 87%. • Grading in Second semester can be done with leniency. • On Campus facilities should be improved to retain more students. • Facilities for female candidates should be given more attention. • Students coming from High School Cluster 1st and 3rd needs to be scrutinized more carefully. • Attrition amongst students with Background category 5 has very high probability; special attention is needed on them.

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