Difference Between Correlation and Regression | Correlation vs Regression

By Mohit Uniyal|Updated : June 13th, 2022

Understanding the difference between Correlation and Regression can be confusing for various candidates. Both Correlation and Regression are an essential part of statistical research. They are multiple variable distribution-based analyses. The major difference between correlation and regression is that regression will tell about the effect of one variable on another whereas correlation will tell about the degree of relation between those two variables.

Here we will learn when and where to use correlation and regression based on our requirements. Along with learning about the detailed aspects of the difference between correlation and regression, we will see about correlation and regression in brief. 

Table of Content

Difference Between Correlation and Regression 

There exist various differences between correlation and regression based on the factors such as meaning, usage, dependent variables and independent variable, and even objectives. All these differences are provided in the table shown below:

Key Differences Between Correlation and Regression 

Tells about the correlation between two variables.Tells about the numerical relation between two dependent variables.
Used to represent the linear correlation.Used to tell the dependency of one variable onto another.
Shows no difference for dependent and independent variables.Shows different behaviour for dependent and independent variables.
It indicates the relationship between the changes in the two variables.It shows the impact of one variable on the other.
Used to find the numerical relationship.Used to estimate the value of one dependent on another fixed variable.
Can be helpful in creating a relationship between two variables.Can help in estimating the changes of one onto another. 

What is the Correlation Coefficient?

The correlation coefficient is also known as Pearson's correlation coefficient. It measures the degree of association between two variables. It is used in linear association problems. The correlation of one variable with another variable is when one variable changes and a significant effect can be seen on the other variable directly or indirectly.

The correlation between two variables can have positive as well as negative values. Significant change(increase or decrease) in one variable will change the correlated variable. The common example of two correlated variables is investment and profit.

What is Regression?

Regression is also a type of analysis which will give an average mathematical relation between two or more variables. The regression will provide an estimated change in one variable which is dependent on various independent variables. 

Let us understand this with an example. If we have two variables "x" and "y" where x is an independent variable and y is a dependent variable. The relation between the two variables will be called the line of regression y on x and can be represented as

Y = a + bx, where b is a regression coefficient. 

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FAQs on Difference Between Correlation and Regression

  • The major difference between correlation and regression is that regression is used to elaborate the effect of one variable onto another whereas correlation tells about the relation between eh two variables and effect of change in one causes effect on other.

  • based on the usage, the difference between correlation and regression is that correlation is used to establish and define the relationship between two variables whereas regression is used to explain the behaviour of one variable onto another.

  • It is important to know the difference between correlation and regression because it will help to know when to apply correlation coefficient between two variables and when to use regression. It will help in analysing the variable situation of two or more variables and will help in establishing the relation.

  • The correlation gives a slope whose value always remain between -1.0 and 1.0. whereas regression can have negative slope along with positive slope based on the slope of the correlation.

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