Exercise

# Parameter estimation: active bout length

Compute the mean active bout length for wild type and mutant, with 95% bootstrap confidence interval. The data sets are again available in the `numpy`

arrays `bout_lengths_wt`

and `bout_lengths_mut`

. The `dc_stat_think`

module has been imported as `dcst`

.

Instructions

**100 XP**

- Compute the mean active bout length for wild type and mutant using
`np.mean()`

. Store the results as`mean_wt`

and`mean_mut`

. - Draw 10,000 bootstrap replicates for each using
`dcst.draw_bs_reps()`

, storing the results as`bs_reps_wt`

and`bs_reps_mut`

. - Compute a 95% confidence interval from the bootstrap replicates using
`np.percentile()`

, storing the results as`conf_int_wt`

and`conf_int_mut`

. - Print the mean and confidence intervals to the screen.