Difference Between Correlation And Covariance

Correlation and covariance are statistical notions used to understand the relationship between two values or variables. Correlation measures the linear relationship between two random variables. In the other hand, covariance measures the dependency between two variables. Correlation shows the relationship and covariance shows the variation between the variables.

Difference Between Correlation And Covariance

The main dissimilarities or difference between correlation and covariance can be studied as follows:

1. Introduction

Correlation: A mathematical concept which is used to measure the quantitative relationship between two random variables.

Covariance: A mathematical concept which is used to measure the variation between two random variables

2. What Is It?

Correlation: It is simply a relationship between the variables

Covariance: It is known as the variation between variables

3. Value

Correlation: Value of correlation always lies between -1 and +1

Covariance: Value of covariance lies between - Infinity and + Infinity

4. Change In The Scale

Correlation: It is not influenced by the change in the scale, it means the value of variable does not change if the value of another variable is changed.

Covariance: It is easily influenced by the change in the scale, it means if the value of one variable is changed then the value of another variable is also changed.

5. Shows

Correlation: It shows the connection between the variables.

Covariance: It shows the joint variability between the variables

Correlation Vs Covariance (Comparison Chart)

Basis	Correlation	Covariance
Introduction	Statistical measurement of quantity relationship between the variables	Statistical measurement of variation between two variables
It Is	Relationship between two random variables	Variation between two variables
Lies Between	-1 and +1	- infinity and + infinity
Influenced By The Change In Scale	No	Yes
Shows	Connection	Variability

Distinction Between Correlation And Covariance In Short

- Correlation indicates the relationship between two values. But Covariance indicates the variation between the values.

- Correlation is not affected by the change in the value of one variable. Covariance is affected by the change in the scale.

- Correlation measures the connection between the variables. On the other hand, covariance measures the joint variability between the values or variables.