Exercise

# Visualizing permutation sampling

To help see how permutation sampling works, in this exercise you will generate permutation samples and look at them graphically.

We will use the Sheffield Weather Station data again, this time considering the monthly rainfall in June (a dry month) and November (a wet month). We expect these might be differently distributed, so we will take permutation samples to see how their ECDFs *would look if* they were identically distributed.

The data are stored in the Numpy arrays `rain_june`

and `rain_november.`

As a reminder, `permutation_sample()`

has a function signature of `permutation_sample(data_1, data_2)`

with a return value of `permuted_data[:len(data_1)], permuted_data[len(data_1):]`

, where `permuted_data = np.random.permutation(np.concatenate((data_1, data_2)))`

.

Instructions

**100 XP**

- Write a
`for`

loop to generate 50 permutation samples, compute their ECDFs, and plot them.- Generate a permutation sample pair from
`rain_june`

and`rain_november`

using your`permutation_sample()`

function. - Generate the
`x`

and`y`

values for an ECDF for each of the two permutation samples for the ECDF using your`ecdf()`

function. - Plot the ECDF of the first permutation sample (
`x_1`

and`y_1`

) as dots. Do the same for the second permutation sample (`x_2`

and`y_2`

).

- Generate a permutation sample pair from
- Generate
`x`

and`y`

values for ECDFs for the`rain_june`

and`rain_november`

data and plot the ECDFs using respectively the keyword arguments`color='red'`

and`color='blue'`

. - Label your axes, set a 2% margin, and show your plot. This has been done for you, so just hit 'Submit Answer' to view the plot!