Which correlation measure should you use for your task? Learn everything you need to know about Pearson and Spearman correlations
Consider a symphonic Orchestra tuning their instruments before a performance. Each musician adjusts his notes to harmonize with those of the others, ensuring a perfect music experience. In data scienceThe variables in a data set can be compared to the orchestra musicians: Understanding the harmony or dissonances between them is crucial.
Correlation is a statistical measure that acts as the conductor of the orchestra, guiding the understanding of the complex relationships within our data. Here we will focus on two types of correlations: pearson and Lancer.
If our data is a composition, Pearson and Spearman are our orchestra conductors: They have a unique style of performing the symphony, each with peculiar strengths and subtleties. Understanding these two different methodologies will allow you extract ideas and understand the connections between variables.
He Pearson correlation coefficientdenoted as rquantifies the force and direction of a linear relationship between two Continuous variables (1). It is calculated by dividing the covariance of the two variables by the product of their standard deviations.
Here x and AND They are two different variables and x_i and y_i represent individual data points. \bar{X} and \bar{Y} They denote the mean values of the respective variables.
The interpretation of r It depends on its value, ranging from -1 to 1. A value of -1 implies a perfect negative correlation, indicating that as one variable increases, the other decreases linearly (2). In contrast, a value of 1 means a perfect positive correlation, illustrating a linear increase in both variables. A value of 0 implies that there is no linear correlation.
Pearson correlation is particularly good at capturing linear relationships between variables. Its sensitivity to linear patterns makes it a powerful tool when investigating relationships governed by a consistent linear trend. Additionally, the standardized nature of…
