Statistics with R Programming Part 2 | Random Variables in R Studio | Data Science Tutorial

Statistics with R Programming Part 2 | Random Variables in R Studio | Data Science Tutorial
Hello and welcome back to another tutorial that is ‘Statistics Tutorial with R Studio’ powered by Acadgild. In this video, we will discuss the random variables, a very important topic in the domain of engineering statistics.
Almost every type of data exists in today’s world, can be said to be a result of random experiments moreover, this data will tend to show a pattern of its existence. It is essential that the uncertainties in them need to be evaluated and modeled with maximum accuracy.

The very first step lies in considering the data as random variables and understanding the unique conditions associated with them. So, let’s have a look at the sections that this topic dealing with.
Random Variables can be classified into ‘Discrete’ and ‘Continuous’ random variables.

Discrete Random Variables:
• Bernoulli’s distribution
• Binomial distribution
• Poisson distribution
• Geometric distribution

Continuous Random Variables:
• Normal/Gaussian distribution
• Lognormal distribution
• Gamma distribution
• Exponential distribution
• Weibull distribution
• Gumbel distribution

What is a Random Variable?
In statistics, the Random variable is a function that can take on either a finite number of values, each with an associated probability or an infinite number of values, whose probabilities are summarized by a density function. In other words, it is a variable which takes up possible values whose outcomes are numerical and are the result of a random phenomenon. It is usually represented by X.
• Discrete Random Variables: When all the possible outcomes of the random variable are finite and distinct, it is called discrete and the probabilities of the outcomes sum to 1. It can be represented by the discontinuous histogram.
• Continuous Random Variables: If the possible outcomes are infinite within an interval, the Random variable is called continuous and the probabilities correspond to a density function whose integral over the entire range of outcomes equals 1. It is represented by the area under a curve.
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[Atlas]

No way he just called it the Poison Distribution 😂