Estimator MSE

Problem

The mean squared error of an estimator decomposes into squared bias plus variance:

MSE = bias^2 + variance.

Return the MSE.

Hint

Bias is squared, so its sign does not matter.

case 1

in: [1,2]

expected: 3

case 2

in: [0,4]

expected: 4

case 3

in: [-0.5,0.25]

expected: 0.5

case 4

in: [0,0]

expected: 0

Starter codePython

def estimator_mse(bias, variance):
    # Your code here
    pass

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