Calculating a confidence interval
If you give a single estimate of a sample statistic, you are bound to be wrong by some amount. For example, the hypothesized proportion of late shipments was 6%. Even if evidence suggests the null hypothesis that the proportion of late shipments is equal to this, for any new sample of shipments, the proportion is likely to be a little different due to sampling variability. Consequently, it's a good idea to state a confidence interval. That is, you say, "we are 95% 'confident' that the proportion of late shipments is between A and B" (for some value of A and B).
Sampling in Python demonstrated two methods for calculating confidence intervals. Here, you'll use quantiles of the bootstrap distribution to calculate the confidence interval.
late_prop_samp and late_shipments_boot_distn are available; pandas and numpy are loaded with their usual aliases.
Deze oefening maakt deel uit van de cursus
Hypothesis Testing in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Calculate 95% confidence interval using quantile method
lower = ____
upper = ____
# Print the confidence interval
print((lower, upper))