# of Cram Schools in Area
vs
Acceptance Rate to Top-Tier Univ.
Chae Lee
Hypothesis
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# of cram schools in area affects to the acceptance rate to Top-Tier university.
** The raw data is from South Korea, and Top-Tier university in Korea is Seoul National University (SNU).
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Data Cleaning & Evaluation
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Original dataset
Refined dataset
Total graduates, college enrollment, employments, others by area(county)
Average number of cram schools from 2015 - 2017 (the years affected to students who graduated on 2018)
Enrollment for Seoul National University (SNU) by area(county)
Total 231 Areas, 476,123 Students.
Refining dataset to get useful data for my hypothesis
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Data Cleaning & Evaluation
Technology used for this step
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Utilized VBA for getting the data�1. Get number of students enrolled in SNU by high school
2. From 1, get the area of the high school and find the area and add the number of enrollment.
3. Merge 2 with the dataset containing information about graduates college enrollment / employments / others rate.
** Since the original dataset contains state and county separately, I merged them into one cell ahead of processing the dataset utilized VBA.
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Conclusion
I wasn’t able to find a strong correlation between # of cram schools in area and acceptance rate to Top-Tier university.
© 2017 Udacity. All rights reserved.
As we can see from the graphs, I cannot find a strong correlation between # of cram schools in area and acceptance rate to Top-Tier University (SNU) - R^2 only 0.1309.
Also, it looked like there is a stronger correlation between college enrollment and # of cram schools in area than vs top-tier university enrollment rate - R^2 is 0.3111.
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Group Photo
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Thank you
Works Cited
© 2017 Udacity. All rights reserved.
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