The two most important and useful Concepts in Statistics explained.

Covariance and PCC

Adith - The Data Guy
3 min readApr 26, 2022

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.

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Adith - The Data Guy

Passionate about sharing knowledge through blogs. Turning data into narratives. Data enthusiast. Content Curator with AI.