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.
This exercise is part of the course
Hypothesis Testing in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate 95% confidence interval using quantile method
conf_int_quantile <- late_shipments_boot_distn %>%
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# See the result
conf_int_quantile