Statistical relationships between two variables

a.k.a.

Correlations

Any statistical relationship

between two variables.

They can indicate a predictive relationship that can be exploited in practice.

They are measured through coefficients, such as the Pearson coefficient and the Spearman coefficient.

Pearson coefficient:

What we were using with Pd.corr().

Good for measuring possible linear correlations. Ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation)

Spearman coefficient:

Can be used to search for non-linear monotonic* correlations. Also ranges from -1 to +1.

*Monotonic:

1) as the value of one variable increases, so does the value of the other variable

2) as the value of one variable increases, the other variable value decreases

Correlations do not show all relationships.

Cum hoc ergo propter hoc:

With this, therefore because of this.

Correlation does not mean causation.

r = 0.992558

r = 0.899899

r = 0.935701

By Pol Lorente