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.

NO. XY
1. x1y1
2. x2y2
3.x3y3
4.x4y4
5.x5y5
6.x6y6

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.