Computers and Technology, 25.04.2020 01:17 sebby33
It's time to bootstrap so that we can quantify the variability in our estimate! Simulate 1000 resamples from close_novas. For each resample, compute the slope of the least-squares regression line, and multiply it by 1 million to compute an estimate of the age of the universe. Store these ages in an array called bootstrap_ages, and then use them to compute a 95% confidence interval for the age of the universe.
bootstrap_ages = make_array()
for i in np. arange(1000):
bootstrap_ages = np. append(bootstrap_ages, 1e6*fit_line(close_novas. sample
lower_end = percentile(2.5, bootstrap_ages)
upper_end = percentile(97.5, bootstrap_ages)
Table().with_column("Age estimate", bootstrap_ages*1e-9).hist(bins=np. arange(12, 16, .1), unit= "billion years")
print("95% confidence interval for the age of the universe: [{:g}, {:g}] billion years".format(lower_end*1e-9, upper_end*1e-9)
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It's time to bootstrap so that we can quantify the variability in our estimate! Simulate 1000 resamp...
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