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Lesson 5 - minima add up to zero #671

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HeatherAn opened this issue Jul 18, 2019 · 1 comment
Open

Lesson 5 - minima add up to zero #671

HeatherAn opened this issue Jul 18, 2019 · 1 comment

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@HeatherAn
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@HeatherAn HeatherAn commented Jul 18, 2019

In Lesson 5 referring to the plots of the third dataset, where the minima per day were zero, it says:

looks like a healthy person snuck into our study

But that does not necessarily apply (not at least with the information we have so far). What we plotted in the previous lesson was the minimum number of inflammation episodes registered across all 60 patients, per day (during the 40-days clinical study). Meaning the person who did not register any inflammation episodes in day 1, could be different from the person who did not register any inflammation episodes in day 20, etc.

Now from the data we can check whether a single person experienced no inflammation episodes during the 40-day study (and there are such patients). But that is not what we did when plotting the data of inflammation-03.csv. Thus I would remove that phrase, or add there a code that checks if a row contains only 0s in the data.

@maxim-belkin
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@maxim-belkin maxim-belkin commented Jul 24, 2019

First of all, thank you for taking a close look at the lesson and for spending the time to report the issue! I truly appreciate it.

Second of all, you're correct: with the code that we currently have in the lesson we don't check whether there is a healthy person or not. Instead, we're detecting whether there is a healthy person in each day of the study. To identify healthy people in the study, we should use

numpy.sum(numpy.min(data, axis=1)) == 0

Let's remove that text about a healthy person in the study for now. Later, we should consider how filtering out healthy people from the study can affect the load on the learners.

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