FAQ: Associations: Two Quantitative Variables - Correlation Part 2

Frequently Asked Questions Python FAQ
1

Community%20FAQs%20on%20Codecademy%20Exercises
Community%20FAQs%20on%20Codecademy%20Exercises1000×208 141 KB

This community-built FAQ covers the “Correlation Part 2” exercise from the lesson “Associations: Two Quantitative Variables”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Master Statistics with Python

FAQs on the exercise Correlation Part 2

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!
You can also find further discussion and get answers to your questions over in Language Help.

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head to Language Help and Tips and Resources. If you are wanting feedback or inspiration for a project, check out Projects.

Looking for motivation to keep learning? Join our wider discussions in Community

Learn more about how to use this guide.

Found a bug? Report it online, or post in Bug Reporting

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

2

In this code what is the pvalue?

corr_sleep_performance, p = pearsonr(sleep.hours_sleep, sleep.performance)
print(p)
print(corr_sleep_performance)

>>> 0.0476593612399499
>>> 0.28149781890494135
3

“p” is the p-value: 0.047. The pearson corr is .28

corr_sleep_performance, p = pearsonr(sleep.hours_sleep, sleep.performance)

print(corr_sleep_performance, p)

>>>0.28149781890494135 0.0476593612399499
4

p is the p-value, which is the probability the correlation would happened under the null hypotheses. The null hypotheses is the assumption that there is no correlation, in this example there is a 4.765…% chance of this happening by random chance. In other words, if there was zero correlation we’d still find this result ~5 times out of 100.

This means for every 21 tests we do that have no correlation, we mistakenly think 1 of them does have a correlation. Keep in mind we get 21 correlations from only 7 variables.