# Probability in R. Discrete Random Variables

Infermath links mathematical theory with programming application to give high level understanding of quantitative fields
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10,866 students
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draw random numbers in R
use descriptive statistics in R
use boolean variables in R
define and use Bernoulli random variable
define and derive probability of binomial distribution
define and assign values to vectors
use histogram in R
use combinations in set theory
define and assign values to matrices in R
draw plots in R
use for and while loops in R
use logical conditions in R
sum geometric series
define and derive probability of geometric distribution
predict numerical limitations of computers and R
define functions in R
define infinite series of events
specify conditions for series convergence
use independence of events
use properties of complementary events
use squeeze theorem
hold the loop execution and print results in R
define and prove Borel-Cantelli lemma

## Requirements

• high school calculus
• high school probability theory

## Description

Probability in R is a course that links mathematical theory with programming application. Discrete Random Variables series gives overview of the most important discrete probability distributions together with methods of generating them in R. Fundamental functionality of R language is introduced including logical conditions, loops and descriptive statistics. Viewers are acquainted with basic knowledge of numerical analysis.

Course is designed for students of probability and statistics who would like to enrich their learning experience with statistical programming. While basic knowledge of probability and calculus is useful prerequisite it is not essential. The suggested method of using the course is by repeating the reasoning and replicating the R code. Therefore it is essential for students to download and use R in the course.

The course consists of twelve short lectures totaling two hours of video materials. Four major topics are covered: Bernoulli distribution (2 lectures), binomial distribution (3 lectures), geometric distribution (3 lectures) and Borel-Cantelli lemma (4 lectures). Eight lectures are presented in a form of writing R code. Remaining four lectures focus solely on theory of probability.

How is Infermath different from other education channels? It equips students with tools and skills to use acquired knowledge in practice. It aims to show that learning mathematics is not only useful but also fun and inspiring. It places emphasis on equal chances in education and promotes open source approach.

## Who this course is for:

• students of probability theory
• R and statistical programming students
• bachelor students of quantitative fields
• high school students
• open source enthusiasts
• programming beginners
• self learners
• classical music melomaniacs
• inquisitive souls
• philosophy and logic apprentices

## Course content

4 sections12 lectures1h 54m total length
• Introduction
12:03
• Bernoulli distribution
06:39

## Instructor

Financial Engineer

In 2012 received BSc in Mathematics from University of Warsaw.

In 2013 graduated with Distinction from Imperial College London  receiving MSc degree in Risk Management and Financial Engineering.