1. Louis Licamele Using HCE to Analyze Patterns Among Universities

2. The Dataset Was created manually by entering data found on the common data set forms hosted by different universities. Was created manually by entering data found on the common data set forms hosted by different universities. Attributes Collected and Calculated: Name, Funding, Coed Or Not, Calendar, Full Time Students, % Men, % Women, % Aliens, % Black, % Indian, % Asian, % Hispanic, % White, % Unknown Race, Men Applied, Women Applied, % Men Admitted, % Women Admitted, % Men Enrolled, % Women Enrolled, SAT Verbal 25 th, SAT Verbal 75 th, SAT Math 25 th, SAT Math 75 th, ACT 25 th, ACT 75 th, Tuition Attributes Collected and Calculated: Name, Funding, Coed Or Not, Calendar, Full Time Students, % Men, % Women, % Aliens, % Black, % Indian, % Asian, % Hispanic, % White, % Unknown Race, Men Applied, Women Applied, % Men Admitted, % Women Admitted, % Men Enrolled, % Women Enrolled, SAT Verbal 25 th, SAT Verbal 75 th, SAT Math 25 th, SAT Math 75 th, ACT 25 th, ACT 75 th, Tuition

3. Public Schools

4. Womens Schools

5. The Tuition Gap

6. Men vs. Women (Applied) Tech Schools (MIT & GATech) Womens Schools

7. More Women = Less Diverse?

8. Higher Average SAT Scores = Harder to get in

9. Relationship between Cost and Difficulty (Assumes harder to get in means a more difficult school)

10. So What’s The Best Bang for the Buck???

11. Minimize Cost

12. So What’s The Best Bang for the Buck??? Minimize Cost Minimize Acceptance

13. So What’s The Best Bang for the Buck??? Minimize Cost Minimize Acceptance

14. So What’s The Best Bang for the Buck??? Minimize Cost Minimize Acceptance UMD

15. Congratulations! You Picked The Right School