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Probability in R. Discrete Random Variables

via Udemy

Overview

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

What you'll learn:
-> 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

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.

Taught by

Pawel Dudko

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