While investigating the relationship between two variables or more, it is essential to know the regression vs. Correlation. That is why today, we’ll discuss differences and similarities between regression and Correlation along with examples.

But before moving ahead to know these terms, let’s see some related topics that you might like:

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Regression and Correlation both the terms are essential in Statistics. For measuring and analyzing the connections between the two variables, these terms are important.

## Regression Vs. Correlation

The following are some differences between regression vs. Correlation:

### What is Regression in Statistics?

- Regression describes how a single variable numerically related to the dependent variable. A simple linear regression uses the equation Y= a + bX.
- It is used to find out the value of one variable based on another variable.
- A single variable can be responsible for changing the value of other variables.
- Both variables are different, representing different factors. But it still depends on each other.

### What is the Correlation in Statistics?

- Correlation in statistics is the relations between two variables and always ties between -1.0 to 1.0.
- The correlation coefficient is used to represent the linear relationship between two variables.
- If one variable changes, the other variable also changes. It could be directly or indirectly.
- The main objective is to find the numerical value expressing the relationship between the two variables.

## 5 Similarities Between Regression and Correlation

- Both of them measures the direction and strength of the relationship between two variables.
- If the correlation ® is negative, the whole slope of regression will also be negative.
- If the Correlation is positive, then the whole slope of regression will be positive.
- In a simpler linear regression equation, Correlation (r2 or R2) has a specific meaning.
- It represents the proportion of variable Y explained by X.

## Some Additional Information on Regression Vs. Correlation

Regressions used to build models or equations which will predict a key response. In contrast, Correlation used to summarize the direction and strength of the relationships between a set of two or more than two numeric variables. Correlation can do it in a fast and concise manner.

Furthermore, to quantify the direction of relationships, experts use both Correlation and regression. But the variables, X and Y, are interchangeable in Correlation, which is not the case in regression. Besides this, prediction and optimization can be made in regression but not in Correlation.

Therefore above-mentioned are some critical information about regression vs. Correlation. If you need to know more about it or have to complete an assignment on the topic, get instant **online assignment help** from our professionals.