This community-built FAQ covers the “Quantiles Review” exercise from the lesson “Quantiles”.
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Hi team,
I just finished learning the Quartiles, Quantiles and Percentiles part in Data Science path. I need a bit more perspective in interpreting this graph from the lesson:
The questions are:
- If you had an SAT score of
1350, which tenth of the data would you be in for each school?
- Then, which schools should you apply to?
- Would any of the schools be unrealistic options?
I personally can answer question 1, but not so much for question 2 and 3.
It would be great if some aspiring Data Scientist can explain their answers to Q2 and Q3.
Looking forward to it.
Regards,
Jimmy
1 Like
I plotted the line with:
plt.axvline(x=1350, c = ‘yellow’)
You will see that with that score yow would be top of the class in schools 2 and 3 but just below the half in school 1. It appears that School One is the better school.
Question 3. I have no idea.
As @bjrompal pointed out, you could easily draw the 1350 line to have a better visualization of this score vs the different percentiles.
Q1: which percentile for each school?
- School 1: 1350 places you in the fifth decile, aka top 50% (but just slightly above the median). It is unlikely that you would be successful in applying to this school.
- School 2: 1350 places you in the first decile, aka top 10%. Your chances of getting into that school are extremely high.
- School 3: 1350 places you in the second decile, (just below the first decile). Your are in the top 20% scores, so your chances of getting into that school are also very high.
Q2: which schools to apply to?
Based on the info above, schools 2 and 3.
Q3: unrealistic option?
School 1 would be an unrealistic option as almost 50% of other students have a better SAT score.
I hope that helps 
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When creating the deciles is it better practice to manually write out all of the steps or would it be better to create the list using np.arange as shown below?
deciles_one = np.quantile(school_one, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
deciles_one = np.quantile(school_one, np.arange(.1,1,.1))
