Skip to main content
HomeRFoundations of Probability in R

Foundations of Probability in R

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Start Course for Free
4 Hours13 Videos54 Exercises
35,666 LearnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies


Course Description

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for how to solve probability problems through random simulation. These principles will help you understand statistical inference and can be applied to draw conclusions from data.
  1. 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.

    Play Chapter Now
    Flipping coins in R
    50 xp
    Simulating coin flips
    100 xp
    Simulating draws from a binomial
    100 xp
    Density and cumulative density
    50 xp
    Calculating density of a binomial
    100 xp
    Calculating cumulative density of a binomial
    100 xp
    Varying the number of trials
    100 xp
    Expected value and variance
    50 xp
    Calculating the expected value
    100 xp
    Calculating the variance
    100 xp
  2. 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.

    Play Chapter Now

In the following tracks

Statistician with R

Collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon
Collaborator's avatar
Nick Solomon

Prerequisites

Introduction to R
David Robinson HeadshotDavid Robinson

Principal Data Scientist at Heap

See More

What do other learners have to say?

Join over 13 million learners and start Foundations of Probability in R today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.