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  3. Statistical Thinking in Python (Part 2)
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Exercise

Correlation of offspring and parental data

In an effort to quantify the correlation between offspring and parent beak depths, we would like to compute statistics, such as the Pearson correlation coefficient, between parents and offspring. To get confidence intervals on this, we need to do a pairs bootstrap.

You have already written a function to do pairs bootstrap to get estimates for parameters derived from linear regression. Your task in this exercise is to modify that function to make a new function with call signature draw_bs_pairs(x, y, func, size=1) that performs pairs bootstrap and computes a single statistic on the pairs samples defined by func(bs_x, bs_y). In the next exercise, you will use pearson_r for func.

Instructions

100 XP
  • We have provided your original draw_bs_pairs_linreg() function (named as draw_bs_pairs()). Modify this function to make the draw_bs_pairs() function described above. Be sure to adjust the doc string appropriately, and remember that in this modified function, you only need to return a single statistic.
  • Things to keep in mind: The modified function requires an additional func parameter and returns only bs_replicates, as opposed to bs_slope_reps and bs_intercept_reps as the function in the sample code does.