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  3. Foundations of Probability in R
  • 1

    The binomial distribution

    Free

    One of the simplest and most common examples of a random phenomenon is a coin flip: an event that is either "yes" or "no" with some probability. Here you'll learn about the binomial distribution, which describes the behavior of a combination of yes/no trials and how to predict and simulate its behavior.

  • 2

    Laws of probability

    In this chapter you'll learn to combine multiple probabilities, such as the probability two events both happen or that at least one happens, and confirm each with random simulations. You'll also learn some of the properties of adding and multiplying random variables.

  • 3

    Bayesian statistics

    Bayesian statistics is a mathematically rigorous method for updating your beliefs based on evidence. In this chapter, you'll learn to apply Bayes' theorem to draw conclusions about whether a coin is fair or biased, and back it up with simulations.

  • 4

    Related distributions

    So far we've been talking about the binomial distribution, but this is one of many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial: the normal, the Poisson, and the geometric.

Initializing

Exercise

Updating with Bayes theorem

In this chapter, you used simulation to estimate the posterior probability that a coin that resulted in 11 heads out of 20 is fair. Now you'll calculate it again, this time using the exact probabilities from dbinom(). There is a 50% chance the coin is fair and a 50% chance the coin is biased.

Instructions

100 XP
  • Use the dbinom() function to calculate the exact probability of getting 11 heads out of 20 flips with a fair coin (50% chance of heads) and with a biased coin (75% chance of heads). Save them as probability_fair and probability_biased, respectively.
  • Use these to calculate the posterior probability that the coin is fair. This is the probability that you would get 11 from a fair coin, divided by the sum of the two probabilities.