✔️ Information reviewed and updated in August 2024 by Eduardo López

**➡What is the correlation coefficient? ✨**

**The correlation coefficient or also called Pearson's correlation coefficient is focused on quantitative variables**(minimum interval scale), and refers to an index that

**allows to analyze the degree of covariation that exists between those variables that are linearly related.**Correlation refers in itself to the numerical form that statistics can verify through the relationship of one or more variables, which are achieved by measuring the level of dependence of a variable with respect to another totally independent variable.

**According to statistics, the correlation coefficient has a linear measure character between two random variables that are quantitative.**

**➡What is the correlation coefficient for? ✨**

**The main objective of the correlation coefficient is to measure the correlation between two variables**. And among the advantages for which the correlation coefficient stands out with respect to other forms of correlation measurement, it is the so-called covariance, do not forget that the results of the correlation coefficient are between -1 and +1, its simplicity being useful to compare different correlations in a more direct and simple way.

**If you analyze two random variables X and Y, related to a certain population, the relationship coefficient will be expressed with Pxy.**

**➡How is it interpreted? ✨**

**This usually varies in the interval [-1,1],**The sign thus establishing the meaning of the relationship, and the interpretation of each result is interpreted as follows:

- If r is equal to 1, it means that it is a positive correlation, where the index reflects the total dependence between both two variables, this is called a direct relationship, where one of the variables increases while the other increases in constant proportion .
- If 0 <r <1 it means that a positive correlation is occurring.
- If r = 0 there is no linear relationship, although this does not mean that the variables are independent, since there may be non-linear relationships between both variables.
- If -1 <r <0 indicates that there is a negative correlation.
- If r = - 1 indicates a perfect negative correlation and a total dependence between both variables, this is known as an inverse relationship. It occurs when one variable increases, while the other instead decreases in proportion.

**the correlation will allow you to know exactly the strength and direction of the linear relationship that occurs between two or more random variables.**