Pearson correlation of offspring and parental data
The Pearson correlation coefficient seems like a useful measure of how strongly the beak depth of parents are inherited by their offspring. Compute the Pearson correlation coefficient between parental and offspring beak depths for G. scandens. Do the same for G. fortis. Then, use the function you wrote in the last exercise to compute a 95% confidence interval using pairs bootstrap.
Remember, the data are stored in bd_parent_scandens, bd_offspring_scandens, bd_parent_fortis, and bd_offspring_fortis.
Diese Übung ist Teil des Kurses
Statistical Thinking in Python (Part 2)
Anleitung zur Übung
- Use the
pearson_r()function you wrote in the prequel to this course to compute the Pearson correlation coefficient for G. scandens and G. fortis. - Acquire 1000 pairs bootstrap replicates of the Pearson correlation coefficient using the
draw_bs_pairs()function you wrote in the previous exercise for G. scandens and G. fortis. - Compute the 95% confidence interval for both using your bootstrap replicates.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Compute the Pearson correlation coefficients
r_scandens = ____
r_fortis = ____
# Acquire 1000 bootstrap replicates of Pearson r
bs_replicates_scandens = ____
bs_replicates_fortis = ____
# Compute 95% confidence intervals
conf_int_scandens = ____
conf_int_fortis = ____
# Print results
print('G. scandens:', r_scandens, conf_int_scandens)
print('G. fortis:', r_fortis, conf_int_fortis)