Probability in R. Discrete Random Variables

Probability in R. Discrete Random Variables, available at Free, has an average rating of 4.4, with 12 lectures, 4 quizzes, based on 255 reviews, and has 12199 subscribers.

You will learn about 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 This course is ideal for individuals who are students of probability theory or R and statistical programming students or bachelor students of quantitative fields or high school students or open source enthusiasts or programming beginners or self learners or classical music melomaniacs or inquisitive souls or philosophy and logic apprentices It is particularly useful for students of probability theory or R and statistical programming students or bachelor students of quantitative fields or high school students or open source enthusiasts or programming beginners or self learners or classical music melomaniacs or inquisitive souls or philosophy and logic apprentices.

Enroll now: Probability in R. Discrete Random Variables

Summary

Title: Probability in R. Discrete Random Variables

Price: Free

Average Rating: 4.4

Number of Lectures: 12

Number of Quizzes: 4

Number of Published Lectures: 12

Number of Published Quizzes: 3

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 15

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Bernoulli random variable

Lecture 1: Introduction

Lecture 2: Bernoulli distribution

Chapter 2: Binomial distribution

Lecture 1: Binomial distribution 1

Lecture 2: Binomial distribution 2

Lecture 3: Binomial distribution 3

Chapter 3: Geometric distribution

Lecture 1: Geometric distribution 1

Lecture 2: Geometric distribution 2

Lecture 3: Geometric distribution 3

Chapter 4: Borel-Cantelli lemma

Lecture 1: Borel-Cantelli lemma 1

Lecture 2: Borel-Cantelli lemma 2

Lecture 3: Borel-Cantelli lemma 3

Lecture 4: Borel-Cantelli lemma 4

Instructors

Pawel Dudko
Financial Engineer

Rating Distribution

1 stars: 8 votes
2 stars: 9 votes
3 stars: 41 votes
4 stars: 96 votes
5 stars: 99 votes

Frequently Asked Questions

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