Co-Variance and It’s Interpretation in Statistics

Whenever we need to find out the type of relationship between two variables/columns in a dataset. Co-variance concept comes into the picture. It is used to determine relationships between different random variables.

1. x1y1
2. x2y2

We can calculate the co-variance between X and Y using the below formula:

cov (X, Y) = (1/N)* Sum ( (Xi – Mux) * (Yi – Muy) )

N – > Total Number of Data Points in the table

Xi -> ith Data Point in column X in the table

Yi -> ith Data Point in column Y in the table

Mux -> Mean of the column X in the table

Muy -> Mean of the column Y in the table

Sign of co-variance is indicative of the relationship between column X and Y. It’s value is not the indicative of strength between X and Y.

If Cov (X, Y) > 0 i.e. sing of Cov(X, Y) is positive, it means as X increases Y also increases.

If Cov(X, Y) < 0 i.e. sign of Cov(X, Y) is negative, it means as X increase Y decreases.