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Exercise

Constructing a CI

You've seen one example of how p-hat can vary upon resampling, but we need to do this many many times to get a good estimate of its variability. Here you will compute a full bootstrap distribution to estimate the standard error (SE) that will be used to form a confidence interval. You'll use an additional verb from infer, calculate(), to streamline this process of calculating many statistics from many datasets.

Take a moment to inspect the output of calculate. This function reduces your data frame to just two columns: one for the "stat"s and another for the "replicate" they correspond to.

When you plot your bootstrap distribution, you'll find that it's bell-shaped. It's this shape that allows you to add and subtract two SEs to get a 95% interval.

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  • Create a bootstrap distribution called boot_dist using the following steps:
  • specify that you're interested in the consci variable where success is indicated by having "High" confidence.
  • generate 500 bootstrap replicates.
  • calculate a proportion statistic by setting stat to "prop".
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