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I apologize in advance if this is poorly worded.

If I have a stdDev = 1, mean = 0, scipy.stats.cdf(-1, loc = 0, scale = 1) will give me the probability that a normally distributed random variable will be <= -1, and that is 0.15865525393145707.

Given 0.15865..., how do I find the value that gives me -1?

i.e. value(cdf = 0.15865, loc = 0, scale = 1)

Thanks for the help.

Alexander Kleinhans
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1 Answers1

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edit: you actually need import norm from scipy.stats.

I found the answer. You need to use ppf in scipy.stats which stands for "percent point function".

So let's say you have a normal distribution with stdDev = 1, and mean = 0 and you want to find the value at which the random variables will be below ~15% of the time. Just use:

value = norm.ppf(0.15, loc = 0, scale = 1)

This will return ~ -1, likewise if you do:

cdf = norm.cdf(-1, loc = 0, scale = 1)

This will return ~ 0.15 or 15%.

Cool beans.

Alexander Kleinhans
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  • For the record, `ppf` is what is more commonly called a "quantile" function. I don't know why they used a nonstandard name. – Robert Dodier May 10 '15 at 05:27