Exercise

# Linear regression of average split time

We will assume that the swimmers slow down in a linear fashion over the course of the 800 m event. The slowdown per split is then the slope of the mean split time versus split number plot. Perform a linear regression to estimate the slowdown per split and compute a pairs bootstrap 95% confidence interval on the slowdown. Also show a plot of the best fit line.

*Note*: We can compute error bars for the mean split times and use those in the regression analysis, but we will not take those into account here, as that is beyond the scope of this course.

Instructions

**100 XP**

- Use
`np.polyfit()`

to perform a linear regression to get the slowdown per split. The variables`split_number`

and`mean_splits`

are already in your namespace. Store the slope and interecept respectively in`slowdown`

and`split_3`

. - Use
`dcst.draw_bs_pairs_linreg()`

to compute 10,000 pairs bootstrap replicates of the slowdown per split. Store the result in`bs_reps`

. The bootstrap replicates of the intercept are not relevant for this analysis, so you can store them in the throwaway variable`_`

. - Compute the 95% confidence interval of the slowdown per split.
- Plot the split number (
`split_number`

) versus the mean split time (`mean_splits`

) as dots, along with the best-fit line.