Exercise

Bootstrapping vs. normality

You've seen the results of a bootstrap confidence interval for Pearson's R. But what about common situations like making a confidence interval for a mean? Why would you use a bootstrap confidence interval over a "normal" confidence interval coming from stats.norm?

A DataFrame showing investments from venture capital firms (investments_df) has been loaded for you, as have the packages pandas as pd, NumPy as np, and stats from SciPy.

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Select only the companies in the Analytics market.
Construct a 95% confidence interval for the mean private_equity using the confidence interval function from stats.norm.
Do the same calculation on private_equity, but using a bootstrap confidence interval instead.