Exercise

# Jackknife confidence interval for the median

In this exercise, we will calculate the jackknife 95% CI for a non-standard estimator. Here, we will look at the median. Keep in mind that the variance of a jackknife estimator is `n-1`

times the variance of the individual jackknife sample estimates where `n`

is the number of observations in the original sample.

Returning to the wrench factory, you are now interested in estimating the median length of the wrenches along with a 95% CI to ensure that the wrenches are within tolerance.

Let's revisit the code from the previous exercise, but this time in the context of median lengths. By the end of this exercise, you will have a much better idea of how to use jackknife resampling to calculate confidence intervals for non-standard estimators.

Instructions

**100 XP**

- Append the median length of each jackknife sample to
`median_lengths`

. - Calculate the mean of the jackknife estimate of
`median_length`

and assign to`jk_median_length`

. - Calculate the upper 95% confidence interval
`jk_upper_ci`

and lower 95% confidence intervals of the median`jk_lower_ci`

using`1.96*np.sqrt(jk_var)`

.