# Probability in R. Discrete Random Variables

## Probability in R. Discrete Random Variables

### Infermath links mathematical theory with programming application to give a high-level understanding of quantitative fields

#### What youâ€™ll learn

Probability in R. Discrete Random Variables

• draw random numbers in R
• use descriptive statistics in R
• use boolean variables in R
• define and use Bernoulli random variable
• define and derive the 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 the 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 applications. Discrete Random Variables series gives an 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.

The 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 a useful prerequisite it is not essential. The suggested method of using the course is by repeating the reasoning and replicating the R code. Therefore students need 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. The remaining four lectures focus solely on a theory of probability.

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

#### Who this course is for:

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