Louis Licamele Using HCE to Analyze Patterns Among Universities

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

Public Schools

Womens Schools

The Tuition Gap

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

More Women = Less Diverse?

Higher Average SAT Scores = Harder to get in

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

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

Minimize Cost

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

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

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

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