The two most important and useful Concepts in Statistics explained.

Hola!! readers in this article we will look at the two most important and interesting concepts in statistics. They are Covariance and Pearson Correlation Coefficient. Okay without any further due let’s get started with Covariance as it is the base for Pearson Correlation Coefficient.


Covariance is the measure of the relationship between two random variables. And with the help of covariance, we will be able to find the direction in which the variables change with each other.

The formula for Covariance is given as

Covariance is of 2 types: Positive and Negative covariance

In positive covariance, if our random variable X increases, our random variable Y also increases simultaneously. For example, let’s say we have two variables Sq.ft and Price of a house. In this case, as the Sq.ft (size of the house) increases Price increases and vice versa.

In negative covariance, if our random variable X increases, our random variable Y also decreases simultaneously. For example, when the price is high, demand is less and when the price is less, demand is more.

So, in simple terms, we can say that both the variables in positive covariance move in the same direction and in the case of negative covariance both the variables move in different directions.

And now we have got a clear picture of What is Covariance and the types of Covariance. Now, let’s look at Pearson’s Correlation Coefficient.

Pearson’s Correlation Coefficient

Pearson’s Correlation Coefficient is also the measure of the relationship between two random variables. But in Pearson’s Correlation Coefficient we will be able to find the magnitude by which both the random variables are correlated.

Here, the magnitude means the strength, which gives us a picture of how strong or weak both the variables are correlated.

To find the correlation coefficient we will simply multiply the standard deviation of both random variables and divide it by the covariance of those two variables. The formula of Pearson’s Correlation Coefficient is given as:

The value of this Correlation Coefficient always lies under the range of -1 to +1.

Source: Wikipedia

The above image is the representation of Pearson’s Correlation Coefficient when they lie under a specific range of values.


And by now I hope you got to know about Covariance and Pearson’s Correlation Coefficient.

Don’t forget to leave your responses.✌

Everyone stay tuned!! To get my stories in your mailbox kindly subscribe to my newsletter.

Thank you for reading! Do not forget to give your claps and to share your responses and share them with a friend.




Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Entropy in Statistics

700 Level GMAT Mixture Problem

Factors of 162

Factors of 162

Game Theory application in Rock-Paper-Scissor

An Intuitive Explanation of Expected Utility Theory- Behavioural Economics 1

Next greater permutation

The Moon in Free Fall

GMAT, GRE, SAT Inequalities Quant Question with Equation Manipulation

Jeremiah LaBrash

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Adith - The Data Guy

Adith - The Data Guy

More from Medium

What is a Conditional Expectation? (Visual Examples)

Random variables, Probability Mass Function and Probability Density Function

Chebyshev’s Inequality in Statistics — Explained

Statistics for Data Science: Confidence Intervals