Predicting the probability of defects
Any situation with exactly two possible outcomes can be modeled with binomial random variables. For example, you could model if someone likes or dislikes a product, or if they voted or not.
Let's model whether or not a component from a supplier comes with a defect. From the thousands of components that we got from a supplier, we are going to take a sample of 50, selected randomly. The agreed and accepted defect rate is 2%.
We import the binom
object from scipy.stats
.
Recall that:
binom.pmf()
calculates the probability of having exactlyk
heads out ofn
coin flips.binom.cdf()
calculates the probability of havingk
heads or less out ofn
coin flips.binom.sf()
calculates the probability of having more thank
heads out ofn
coin flips.
This exercise is part of the course
Foundations of Probability in Python
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