Bootstrap Bias Estimate

Problem

Estimate the bootstrap bias of the sample mean: draw n_boot resamples with replacement using a self-contained Lehmer RNG seeded with seed (state = seed % 2147483647, next = state * 48271 % 2147483647) and return mean(resample means) - mean(data). Return 0.0 for empty data or n_boot <= 0.

Hint

Bias is the difference between the average bootstrap mean and the observed mean.

Test cases

case 1

in: [[1,2,3,4,5],200,7]

expected: -0.023000000000000576

case 2

in: [[5],50,1]

expected: 0

case 3

in: [[],10,1]

expected: 0

case 4

in: [[1,2],100,3]

expected: 0.03499999999999992

case 5

in: [[10,20,30,40],300,99]

expected: -0.31666666666666643

Starter codePython

def bootstrap_bias(data, n_boot, seed):
    # Your code here
    pass

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