Humans are social in nature, involving with others and trying to figure out how different concepts work with regard to certain factors. It is like a network of comparisons, and that 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 is 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 – ‘co’, which means together. On other hand, ‘relation’ stands for connection. When a set of data is put together and studied under the same terms, their affiliation 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 retaliated by an equivalent change in Y, then they are said to be uncorrelated.

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A good understanding of the difference between regression and correlation requires a clear grasp of the two concepts. It is also used in real-world scenarios. A good instance is the connection between profit and investment. The more investment you make, the chances are that you will make more profit, so the two grow together. However, when the elements displace in the opposite path, it cannot be used in this instance. For instance, the cost of a product and demand on that product.

## Definition of Regression

Regression is a statistical technique where the outcome of a variable depends on another. This method is used in mathematics and other fields of science to fit the best line and estimate the value of one element based on its association with the other. Indeed, every element involved in it are mutually dependent on each other. It is noteworthy that it incorporates every sort of connection that may exist between the elements in a data set, including linear and non-linear relations. More importantly, it is of different types, based on their functionality. 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.

## Conclusion of Main Differences Between Correlation and Regression

Without mincing words, 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 disclose the huge disparity between the two concepts.