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 exactly k heads out of n coin flips.
binom.cdf() calculates the probability of having k heads or less out of n coin flips.
binom.sf() calculates the probability of having more than k heads out of n coin flips.

Question

Let's answer a simple question before we start calculating probabilities:

What is the probability of getting more than 20 heads from a fair coin after 30 coin flips?

Possible Answers

binom.pmf(k=20, n=30, p=0.5)
1 - binom.pmf(k=20, n=30, p=0.5)
binom.sf(k=20, n=30, p=0.5)
binom.cdf(k=20, n=30, p=0.5)

Exercise

Predicting the probability of defects

Instructions 1/4

Question

Possible Answers