Pearson Correlation Matrix

Prob. and Stats

Difficulty: 2 | Problem written by Mr. Umair
A 2 x 2 Correlation Matrix is a matrix representing correlation coefficients between two variables. Pearson's Correlation Coefficient is the covariance of the two variables divided by the product of their standard deviations.

It can be shown as: 


\(r = \frac{{}\sum_{i=1}^{n} (x_i - \overline{x})(y_i - \overline{y})} {\sqrt{\sum_{i=1}^{n} (x_i - \overline{x})^2(y_i - \overline{y})^2}}\)


Two NumPy arrays having same length.


Return the correlation matrix, which is a two-dimensional array with the correlation coefficients.


Sample Input:
<class 'numpy.ndarray'>
arr1: [1 0]
<class 'numpy.ndarray'>
arr2: [0 1]

Expected Output:
<class 'numpy.ndarray'>
[[ 1. -1.] [-1. 1.]]

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Input Test Case

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numpy has been already imported as np (import numpy as np)