Hints for Lab 3: Classical Inference I: Maximum Likelihood Estimation

For the class on Wednesday, January 24th

See also

Go back to Lab 4

A. Standard Error of Statistics

Hints for Part A

In terms of coding, you have already done something very similar in Lab 2, Part A. Here’s a code snippet on how to collect things if you don’t recall. You will need this structure for both A1 and A2.

import numpy as np

number_of_runs = ...

mean_collected = []
median_collected = []
variance_collected = []

for _ in range(number_of_runs):
    mean = np.mean(...)
    median = np.median(...)
    variance = np.var(...)
    mean_collected.append(mean)
    median_collected.append(median)
    variance_collected.append(variance)

# TODO: then do things with mean_collected, median_collected, variance_collected

B. Comparing samples

Hints for Question 1

Recall from Part A that you can estimate \(\lambda\) and the standard error on your estimate!