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Difference between Positive Correlation and Negative Correlation

To begin with, it is worth noting that mathematics is the deductive science that is dedicated to the study of the properties of abstract entities and their relationships. This means that mathematics works with numbers, symbols, geometric figures, among others. Mathematics is subdivided into various branches such as algebra, statistics, logic, arithmetic, geometry and probability, among others.

Among them, statistics is the branch that is responsible for working with numerical data or transforming them into numbers, having applications in real life situations, because it subtracts the figures of different social events such as birth, mortality, unemployment, among others. Thus, the main function of statistics is precisely the collection of data of different types to make statistical reports that allow insights into any subject that may be quantifiable.

On the other hand, statistics is not only used in social aspects but also in scientific research. So, within statistics there is a technique called correlation, which consists of determining if one variable is related to another. Therefore, it is said that there are two types of statistical correlation, positive and negative.

In this order of ideas, in this article we will define both types of correlations and analyze their particularities based on their definitions to summarize their differences.

Positive Correlation

We speak of a positive correlation when a relationship between one variable and another is linear and direct, so that a change in one variable predicts the change in the other variable. In that case, the correlation is said to be perfect positive, that is, both variables vary at the same time. This type of correlation is directly proportional. There is a positive correlation when the two variables are correlated in a direct sense. Therefore, high values ​​of one correspond to high values ​​of the other and equally with low values.

Negative Correlation

We speak of a negative correlation when the relationship between one variable and another is opposite or inverse, that is, when one variable changes, the other changes in the opposite direction. So, when one variable has high values, the other has low values ​​and the closer this value is to -1, the more evident this covariation will be.

There is said to be perfect negative correlation when r = -1. This type of correlation is inversely proportional. So, there is a negative correlation when the two variables are inversely correlated.

As can be seen in the definitions presented, there are important differences between positive correlation and negative correlation. Here are some differences between the existing mapping types:

Positive Correlation Negative Correlation
In positive correlation, the relationship between variables is linear and straight. In negative correlation, the relationship between variables is opposite.
In positive correlation, the change in one variable predicts the change in the other. In negative correlation when one variable changes, the other does the opposite.
The positive correlation is directly proportional. The negative correlation is inversely proportional.

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