Understanding Correlation Coefficient Results | AWS Certified Machine Learning Exam
Question
You are a machine learning specialist working for a clothing manufacturer.
You have been tasked with building a machine learning model to determine the return on investment (ROI) for advertising a specific clothing line on social media based on the labeled data of past social media campaigns for similar clothing lines. You decide to run a Pearson's correlation coefficient to understand your data correlation in a better way.
When you calculate your Pearson's correlation coefficient of social media advertising ROI, you get a value of 0.35
What conclusions can you draw from this result?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is incorrect.
Your coefficient value is not high enough to indicate a positive relationship.
For a Pearson's correlation coefficient to indicate a notable correlation, the coefficient value should be above 0.5 for a positive correlation, or below -0.5 for a negative correlation.
Your score is 0.35, which falls into the indeterminate range.
Option B is incorrect.
Your coefficient value is not low enough to indicate a negative relationship.
For a Pearson's correlation coefficient to indicate a notable correlation, the coefficient value should be above 0.5 for a positive correlation, or below -0.5 for a negative correlation.
Your score is 0.35, which falls into the indeterminate range.
Option C is incorrect.
A coefficient value of 0 or close to 0 indicates no correlation.
Your value of 0.35 is not close enough to 0 to indicate no correlation.
Option D is correct.
Your coefficient falls into the indeterminate range.
For a Pearson's correlation coefficient to indicate a notable correlation, the coefficient value should be above 0.5 for a positive correlation, or below -0.5 for a negative correlation.
Your score is 0.35, which falls into the indeterminate range.
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), and the Medium article titled What are Covariance and Correlation coefficients and their significance? (https://towardsdatascience.com/covariance-and-correlation-321fdacab168)
The Pearson correlation coefficient is a statistical measure that shows the degree of linear correlation between two variables. It ranges between -1 and 1, where a value of -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation between the variables.
In this case, the Pearson correlation coefficient of social media advertising ROI is 0.35. Since the value is positive, we know that there is a positive correlation between social media advertising and ROI. However, the value of 0.35 is not close to 1, indicating a weak correlation between the two variables.
Therefore, the answer is D. We cannot declare a notable correlation with confidence based on the resulting coefficient. A value of 0.35 indicates some degree of correlation between social media advertising and ROI, but it is not strong enough to draw a definitive conclusion about the relationship.
It is worth noting that correlation does not necessarily imply causation. Even if there is a strong correlation between social media advertising and ROI, it does not mean that advertising on social media caused an increase in ROI. There may be other factors at play that need to be considered.
