Which measure of correlation must you use on your task? Learn all it’s worthwhile to learn about Pearson and Spearman correlations
Consider a symphony orchestra tuning their instruments before a performance. Each musician adjusts their notes to harmonize with others, ensuring a seamless musical experience. In Data Science, the variables in a dataset could be in comparison with the orchestra’s musicians: understanding the harmony or dissonances between them is crucial.
Correlation is a statistical measure that acts just like the conductor of the orchestra, guiding the understanding of the complex relationships inside our data. Here we are going to deal with two varieties of correlations: Pearson and Spearman.
If our data is a composition, Pearson and Spearman are our orchestra’s conductors: they’ve a singular type of interpreting the symphony, each with peculiar strengths and subtleties. Understanding these two different methodologies will will let you extract insights and understand the connections between variables.
The Pearson correlation coefficient, denoted as r, quantifies the strength and direction of a linear relationship between two continuous variables [1]. It’s calculated by dividing the covariance of the 2 variables by the product of their standard deviations.
Here X and Y are two different variables, and X_i and Y_i represent individual data points. bar{X} and bar{Y} denote the mean values of the respective variables.
The interpretation of r relies on its value, starting from -1 to 1. A price of -1 implies an ideal negative correlation, indicating that as one variable increases, the opposite decreases linearly [2]. Conversely, a price of 1 signifies an ideal positive correlation, illustrating a linear increase in each variables. A price of 0 implies no linear correlation.
Pearson correlation is especially good at capturing linear relationships between variables. Its sensitivity to linear patterns makes it a strong tool when investigating relationships governed by a consistent linear trend. Furthermore, the standardized nature of the…