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A random variable is basically a mathematical postulate (rule) that allocates a numerical value to each outcome in a sample space. Random variables can either be a discrete random variable or continuous random variable. A random variable is said to be discrete when it assumes only particular values in an interval. Or else, it will be continuous. When X takes values 1, 2, 3, 4, 5, 6…, it is said to contain a discrete random variable.

As a function, a random variable is required to be computed, which enables probabilities to be allocated to a set of potential values. It is evident that the result is dependent upon some physical variables which are not foreseeable. Say, when we toss a fair coin, the ultimate outcome of occurring to be heads or tails will rely on the possible physical conditions. We are not able to foresee, which outcome will be noted. Although there are other probabilities like the coin could get lost or tampered, such contemplation is overlooked.

There are mainly 2 types of random variables, as given below:

Continuous Random Variable

Discrete Random Variable

In probability, a real-valued function, described over the sample space of a random experiment, is known as a random variable. That is, the values of the random variable are in correspondence to the results of the random experiment. Random variables could either be continuous or discrete.

A random variable’s possible values may exhibit the possible results of an experiment, which is about to be carried out or the possible results of a preceding experiment whose existing value is not known. They may also theoretically define either the outcomes of an “objectively” random procedure (like rolling a die) or the “subjective” randomness that occurs as a consequence of insufficient knowledge of a quantity.

The domain of a random variable is a sample space, which is bespoke of an assemblage of possible outcomes from a random event. For example, when a coin is tossed, only two possible outcomes are addressed such as heads or tails.

A random variable and probability distribution can appear to be like:

Conceptual recording of outcomes as well as probabilities of the outcomes.

An experimental listing of outcomes linked with their distinguished relative frequencies.

A subjective listing of outcomes linked with their subjective probabilities.

The probability of a random variable X that takes the values x is described as a probability function of X which is represented by f (x) = f (X = x)

A probability distribution always fulfills two conditions that are as given:

f(x)≥0

∑f(x)=1

Following are the important probability distributions:

Binomial distribution

Bernoulli’s distribution

Exponential distribution

Normal distribution

Poisson distribution

Discrete probability distribution of a random variable X is a list of every possible value of X together with the probability that X gets hold of that value in one trial of the experiment. Each probability P(x) should be between 0 and 1 i.e.: 0≤P(x) ≤1. The sum of all the likely probabilities is 1: ∑P(x)=1.

Random Variables discrete or continuous can also be transformed, meaning that the value can be reassigned to another variable.

The transformation of random variable is actually incorporated to remap the number line from x to y

There is also a random variable that we call a geometric random variable

Example:

Calculate the mean value for the continuous random variable, when assigned the function f(x) = x, 0 ≤ x ≤ 2.

Solution:

Given function: f(x) = x, 0 ≤ x ≤ 2

Using the formula to calculate the mean value: E(X) = \[\int_{-\infty}^{\infty}\] x f(x)dx

We get,

E(X) = \[\int_{0}^{2}\] xf(x) dx

E(X) = \[\int_{0}^{2}\] x. xdx

E(X) = \[\int_{-\infty}^{\infty}\] x\[^{2}\] dx

E(X)=(x³/3) \[_{0}^{2}\]

E(X)=(2³/3)−(0³/3)

E(X)=(8/3)−(0)

E(X)=8/3

Hence, we get the mean of the continuous random variable= E(X) = 8/3.

FAQ (Frequently Asked Questions)

Q1. What is a Binomial Random Variable?

Answer: A unique class of discrete random variables– binomial random variables counts how frequently a specific event takes place in a definite number of trials. On each trial, the event of interest either happens or does not. The probability of occurrence (or not) is just similar on each trial. Remember that these trials are not dependent on one another.

A distribution of binomial random variables can be simply thought of as the probability of either SUCCESS or FAILURE as an outcome in a survey that is repeated multiple times. The binomial is a kind of distribution that will have two possible outcomes. For example, a competitive exam taken could have two possible outcomes: Selected or Rejected, a coin toss has only two possible outcomes: heads or tails.

Q2. What is a Binomial Distribution in the Bernoulli Random Variable?

Answer: The binomial distribution is very closely associated to the Bernoulli distribution. When each Bernoulli trial is independent of one another, then the frequency of successes in Bernoulli trials contains a binomial Distribution. Conversely, the Bernoulli distribution is a set of Bernoulli trials and has Binomial distribution with n=1.”

There could only be one possible outcome of each Bernoulli trial, chosen from S, selected, or F, failure. In every trial, the probability of success, P(S) = p, is similar. On the other hand, the probability of failure is 1 minus (-) the probability of success: P(F) = 1 – p. (Note that “1” is the total probability of an event happening…probability is always between 0 and 1).