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The most common correlation is the Pearson correlation that measures the degree and the direction of the linear relationship between two variables; when there is a perfect linear relationship, every change in the *X* variable is accompanied by a corresponding change in the *Y* variable (Gravetter, 2021). So, a correlation is a statistical technique used to measure and describe the relationship between two variables. A positive correlation is a relationship in which two variables tend to change in the same direction and a negative correlation is a correlation in which two variables tend to go in opposite directions (Gravetter, 2021). The Pearson correlation consists of a ratio comparing the coverability of *X* and *Y* (the numerator) with the variability of *X* and *Y* separately (the denominator) (Gravetter, 2021). It’s important to address the fact that a correlation simply describes a relationship between two variables and It does not explain why the two variables are related so it cannot be interpreted as proof of a cause-and-effect relationship between the two variables (Gravetter, 2021).