You are a machine learning specialist working for an oil refinery company.
Your team is working on a machine learning problem where you need to determine the relationship between oil well depth and oil well production.
In order to select the appropriate machine learning algorithm to use to attempt to solve the oil well production problem, you need to gain a better understanding of your data.
For example, what is the correlation between your oil well depth data and your oil well production data? When you examine your data visually using the python matplotlib library, you find that your data has what looks like a non-Gaussian distribution of oil well depth and oil well production: Which correlation coefficient would you use to best summarize the strength of the correlation between your oil well depth and oil well production?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: C.
Option A is incorrect.
Covariance is used when you have a Gaussian relationship between your variables.
Option B is incorrect.
Pearson's correlation coefficient is also used when you have a Gaussian relationship between your variables.
Option C is correct.
Spearman's correlation coefficient is used when you have a non-Gaussian relationship between your variables.
Option D is incorrect.
The polychoric correlation coefficient is used to understand the relationship of variables gathered via surveys such as personality tests and surveys that use rating scales.
Reference:
Please see the Machine Learning Mastery page titled How to Calculate Correlation Between Variables in Python (https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/), the Wikipedia page titled Correlation coefficient (https://en.wikipedia.org/wiki/Correlation_coefficient), the Wikipedia page titled Polychoric correlation (https://en.wikipedia.org/wiki/Polychoric_correlation), and the Medium article titled What are Covariance and Correlation coefficients and their significance? (https://towardsdatascience.com/covariance-and-correlation-321fdacab168)