1. Predictive Analytics & Enrollment Management Chris J. Foley Director of Undergraduate Admissions Mary Beth Myers Registrar

2. Question #1 Can we more accurately predict the size of the incoming freshman class?

3. Traditional yield ratios cannot take into consideration shifts in the composition of the applicant pool Given the rate that IUPUI is attracting different types of students, incorrect predictions are likely based on yield ratios Models based on regression analysis may provide a solution

4. Predictive Modeling Admitted FTFT Applied SIGS Summer, International, and Gen Studies Students 2012 = 167 2013 = 162 2014 = 160 est. Ratio between the non- decisioned apps to enrolled students not in Admitted or SIGS Regression equation based on multiple data points First-time full- time cohort as reported by UIRR

5. The 3 Model Results Admitted FTFT Applied SIGS 2148 3476 1168 160 2719 3551 672 160 3132 3472 180 160 May 1 st Model March 1 st Model January 1 st Model

6. Significant Variables January Academic Honors App btwn Oct-Nov Class Rank Class Size Distance from campus Ethnicity GPA High School Home County Max SAT or ACT score Network ID Created Plan/Major Program/School Rank Percentage Referral Source Code Region of Home Address Top 10 Rank March Academic Honors App btwn Dec-Jan App btwn Oct-Nov Class Size Core 40 Distance from home to campus Ethnicity is known First Generation Gender GPA Graduation Period Home State Max SAT or ACT score Nbr days applied before term start Network ID Created Program Code Rank Number Rank Percentage Referral Source Code Residency School ID School State Top 10 Rank May Academic HonorsMax SAT or ACT score Age When Applied Nbr days applied before term start App btwn April-May Nbr days from app to admit App btwn August-SeptProgram Code App btwn Dec-JanRank Number App btwn Feb-MarRank Percentage App btwn Oct-NovRegion of the U.S. Application DateResidency BirthdateSchool ID Class SizeSchool Name Core 40School State Distance from home to campusSchool ZIP First Generation Student is Spring HS Graduate GenderTop 10 Rank GPA Graduation Period High School Out of State Home Country Home County Home State HS Grad Period is within 6mos of term

7. Yields 2012 Actual 2013 Actual 2014 Predicted Jan 1 st Model44% 41% Mar 1 st Model44% 42% May 1 st Model44% 41%

8. Therefore, the models predict a drop of yield of 2-3 percentage points. However, our admit-to-deposit yield has shown no decline over prior years and has actually increased by.5%.

9. How Did The Models Perform? Prior Ratio Estimates3,650 Model 1 (Jan 1 st )3,476 Model 2 (Mar 1 st )3,557 Model 3 (May 1 st )3,472 Actual Enrollment: 3,584

10. Question #2 Can we predict the number of freshmen who will require COMM R110 in their first 2 semesters based on information available in May?

11. Predictive Modeling for R 110 Deposited by May 1 R 110 Yet to Deposit Estimate of R 110 enrollees who had not deposited by May 1st Regression equation based on multiple data points of May 1 st Deposits Number of new freshmen who enrolled in R 110 in either fall or spring semester

12. R 110 Analysis 2013 2012 May 1 st Predicted R 110 Residual 867 1,248 3811,492 1,960 468

13. 2014 Projected (estimated) May 1 st Predicted R 110 Residual 1,311 1,896 585

14. Significant Variables for R 110 Model Positive (Increased Likelihood of Enrolling in R 110) Negative (Decreased Likelihood of Enrolling in R 1110) BusinessFirst Generation TechnologyScience Avon HS AddressPre-Medicine Program Mooresville HSPre-Music Technology May/June GraduatePre-Nursing Pre-Computer ScienceBen Davis University HS Pre-Mechanical EngineeringFranklin Community HS Greenfield Central HS Biology BS major Pre-Herron Fine Arts

15. Course Enrollment

17. Analyze success of fall 2014 freshman model Analyze spring 2015 R 110 course data once available Complete overall course analysis for fall 2014 & spring 2015 based on fall 2014 freshman model (R 110 & W 131) Next Steps

18. Build enrollment models for fall 2015 freshmen Build and test W 131 May 1 st model Build and test May 15 th model Explore the use of individual probability scores for recruitment Analyze R 110 and W 131 course data based on best fall 2015 model including significant variables Next Steps