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  3. Foundations of Probability in Python
  • 1

    Let's start flipping coins

    Free

    A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

  • 2

    Calculate some probabilities

    In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.

  • 3

    Important probability distributions

    Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.

  • 4

    Probability meets statistics

    No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

Initializing

Exercise

Formula 1 engines

Suppose that two manufacturers, A and B, supply the engines for Formula 1 racing cars, with the following characteristics:

  • 99% of the engines from factory A last more than 5,000 km.
  • Factory B manufactures engines that last more than 5,000 km with 95% probability.
  • 70% of the engines are from manufacturer A, and the rest are produced by manufacturer B.

What is the chance that an engine will last more than 5,000 km?

Instructions

100 XP
  • Calculate the following probabilities:
    • The manufacturer is A (P_A).
    • The engine lasts more than 5,000 km given that manufacturer is A (P_last5000_g_A).
    • The manufacturer is B (P_B).
    • The engine lasts more than 5,000 km given that manufacturer is B (P_last5000_g_B).
  • Use the total probability law to calculate the probability that the engine will last more than 5,000 km and store the result into P_last_5000.