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  3. Foundations of Probability in Python

Exercise

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.

Instructions 1/4

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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