Skip to main content

Course

Foundations of Probability in R

BasicSkill Level

4.8+

Updated 03/2022

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

Start Course for Free

RProbability & Statistics

4 hr

13 videos

54 Exercises

4,350 XP

42,032

Statement of Accomplishment

Create Your Free Account

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

Training a Team?

Try for Business

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.

Prerequisites

Introduction to R

1

The binomial distribution

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.

Flipping coins in R

Simulating coin flips

Simulating draws from a binomial

Density and cumulative density

Calculating density of a binomial

Calculating cumulative density of a binomial

Varying the number of trials

Expected value and variance

Calculating the expected value

Calculating the variance

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.

Probability of event A and event B

Solving for probability of A and B

Simulating the probability of A and B

Simulating the probability of A, B, and C

Probability of A or B

Solving for probability of A or B

Simulating probability of A or B

Probability either variable is less than or equal to 4

Multiplying random variables

Expected value of multiplying a random variable

Simulating multiplying a random variable

Variance of a multiplied random variable

Adding two random variables

Solving for the sum of two binomial variables

Simulating adding two binomial variables

Simulating variance of sum of two binomial 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.

Updating with evidence

Updating with simulation

Updating after 16 heads

Updating with simulation after 16 heads

Prior probability

Updating with priors

Updating with three coins

Bayes' theorem

Updating with Bayes theorem

Updating for other outcomes

More updating with priors

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.

The normal distribution

Approximating a binomial to the normal

Simulating from the binomial and the normal

Comparing the cumulative density of the binomial

Comparing the distributions of the normal and binomial for low n

The Poisson distribution

Approximating a binomial with a Poisson

Simulating from a Poisson and a binomial

Density of the Poisson distribution

Sum of two Poisson variables

The geometric distribution

Waiting for first coin flip

Using replicate() for simulation

Simulating from the geometric distribution

Probability of a machine lasting X days

Graphing the probability that a machine still works

Foundations of Probability in R

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.8

from 420 reviews

82%

16%

1%

0%

0%

Sort by

Anna

5 days ago

Ahmed

5 days ago

YING

7 days ago

undertandable

Larissa

last week

Louis

last week

Miguel Angel

last week

Ahmed

"undertandable"

YING

Larissa

FAQs

Which probability distributions does this course cover?

You learn the binomial distribution in depth, then explore the normal, Poisson, and geometric distributions in the final chapter, understanding how they relate to each other.

How does simulation feature in the course?

Throughout all four chapters, you use random simulation in R to verify probability results, building strong intuition for how probability problems work in practice.

How long does this course typically take to complete?

The course has 4 chapters with 54 exercises. The median completion time is about 2.5 hours, though the estimated total learning time is 4 hours.

Join over 19 million learners and start Foundations of Probability in R today!

Create Your Free Account

or

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

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.