# Difference Between Correlation vs Regression

Humans are social in nature. We interact with others and try to figure out how different concepts work in regard to certain factors. It is like a network of comparisons, and this is how the human mind works too. For instance, a person who lives in a big and beautiful house is presumed to be rich, while a person who lives in a thatched house is assumed to be poor.

This may be true in some cases but not all. There are other factors that need to be considered. In statistics, the regression vs correlation methodologies are applied in order to reach the most accurate result and make certain predictions. Today, we will discuss the disparities between the two techniques.

## Definition of Correlation

Correlation is the relationship between two variables placed under the same condition. The term is made of two words with different meanings. These are “co,” which means together, and “relation,” which stands for connection.

When a set of data is put together and studied under the same terms, the affiliation of these variables with each other will be revealed. Let us take Q and Y as variables in a data set. When subjected to a test, it was observed that Q and Y experienced a unit change each.

This means they are correlated either directly or indirectly. If the change in Q is not met with an equivalent change in Y, then they are said to be uncorrelated.

A good understanding of the difference between regression and correlation requires a clear grasp of the two concepts. They are also used in real-world scenarios. A good example is the connection between profit and investment.

The greater the investment you make, the greater the profit you will likely make. The two grow together. However, when the elements move in the opposite direction, negative correlation exists. For instance, the cost of a product and demand for that product have a negative correlation.

## Definition of Regression

Regression is a statistical technique where the outcome of a variable depends on another. This method is used in mathematics and science to estimate the value of one element based on its association with the other.

Indeed, every element involved is mutually dependent on each other. It is noteworthy that regression incorporates every sort of connection that may exist between the elements in a data set including linear and non-linear relationships. More importantly, it involves different types based on their functionality.

First, there is the simple linear type that is mostly applicable when studying two continuous variables — an independent and a dependent one. The second one is the multiple linear type, which is applied in examining the common grounds that exist between a dependent variable and more than one independent variable.

## Main Differences Between Correlation vs Regression

Now that you have a good understanding of these two terms, here is a brief representation of the difference between correlation and regression.

## Difference Between Correlation and Regression: Conclusion

Having come this far, there is no doubt that we have fully discussed the subject. In the correlation vs regression comparison, it is not possible to see the contrasts or similarities between these two if they are studied independently.

While the former is used to find out if two or more elements are relatable and the strength of their association, the latter shows how a variable relates to an independent element. Putting them side by side and observing how they function reveals the huge disparity between the two concepts.

In truth, we have dissected the fundamentals of everyday math, thus helping beginners to kick off their journey of learning with a great start.