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@@ -0,0 +1,32 @@
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+#!/usr/bin/env python3
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+""" LPC stats HW2, Problem 1
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+ Author: Caleb Fangmeier
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+ Created: Sep. 25, 2017
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+"""
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+from numpy import sum, sqrt
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+from numpy.random import exponential, poisson
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+
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+
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+def find_relative_frequency(n_experiments, b):
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+ # First get the mean counts for each experiment
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+ mean_counts = exponential(scale=b, size=n_experiments)
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+
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+ # Since this is a counting experiment, sample from a Poisson distribution with the
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+ # previously generated means.
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+ single_counts = poisson(lam=mean_counts)
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+
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+ # Generate the per-experiment bounds based on the observed single_counts
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+ bound_low = single_counts - sqrt(single_counts)
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+ bound_high = single_counts + sqrt(single_counts)
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+
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+ # Finally, count for how many experiments the mean_count lies in the range
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+ # calculated above.
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+ n_pass = sum((bound_low < mean_counts) & (mean_counts < bound_high))
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+ relative_frequency = n_pass / n_experiments
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+
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+ # and print the results
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+ print(f"For b={b}, {n_pass}/{n_experiments} passed, R={relative_frequency*100:4.2f}%")
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+
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+
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+find_relative_frequency(10000, 5)
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+find_relative_frequency(10000, 10)
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