Session Ready
Exercise

Calculating confidence intervals

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. Consequently, it's a good idea to state a confidence interval. That is, you say "we are 95% 'confident' the proportion of late shipments is between A and B" (for some value of A and B).

Sampling in R 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; dplyr is loaded.

Instructions 1/2
undefined XP
  • 1
  • 2
  • Summarize the prop_late_shipments column of late_shipments_boot_distn to calculate the 95% confidence interval using the quantile method. Label the lower and upper CI values lower and upper.
Loading