sleep.

A university in Italy was doing research on the effect of sleep on programming performance. The question is, where do we draw the line? When is the difference in performance big enough that you can't say that it is due to chance? Also, can't we explain the effect with gpa?

The Problem Summary The Plan Calculating Differences Histograms Drawing the Line Counting the Effect That GPA Though Wrapping Up

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Notes

Here's the code we wrote to prepare for the gpa stats.

agg = (df
 .assign(gpa=lambda d: d['gpa'] < d['gpa'].mean())
 .groupby('gpa')
 .agg(mean_unit_tests=('passed_unit_tests', np.mean),
      mean_asserts=('passed_asserts', np.mean),
      mean_user_stories=('tackled_user_stories', np.mean))).T

effect_dict = dict(agg[False] - agg[True])
effect_dict

This is where we calculate the numbers exactly.

df_diff.assign(limit=lambda d: d['diff_unit_tests'] < 0.1948)['limit'].mean()
df_diff.assign(limit=lambda d: d['diff_asserts'] < 0.2922)['limit'].mean()
df_diff.assign(limit=lambda d: d['diff_user_stories'] < 0.0584)['limit'].mean()

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