Heteroscedastic Residual Correlation

Problem

Measure heteroscedasticity as the absolute Pearson correlation between |residual| and the fitted values.

Return 0.0 when a variance is 0 or input is empty.

Hint

A growing spread of residuals with fitted values signals heteroscedasticity.

case 1

in: [[1,-1,2,-2],[1,2,3,4]]

expected: 0.8944271909999159

case 2

in: [[1,1,1],[1,2,3]]

expected: 0

case 3

in: [[1,-1],[1,2]]

expected: 0

case 4

in: [[],[]]

expected: 0

Starter codePython

def heteroscedastic_correlation(residuals, fitted):
    # Your code here
    pass

4 test cases · run your code against them in the editor