# FAQ: Statistical Thinking - Variable Relationships

This community-built FAQ covers the “Variable Relationships” exercise from the lesson “Statistical Thinking”.

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

## FAQs on the exercise Variable Relationships

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 () 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.

You can also find further discussion and get answers to your questions over in Language Help.

Agree with a comment or answer? 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

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!

One thing I don’t understand is that in the learning environment, a correlation coefficient of 0 is considered a weak correlation, while 0.5 and -0.5 are unlabelled.

I was wondering why 0 would be considered weak rather than no correlation entirely, and why 0.5 and -0.5 are not labelled as weak.

Most likely a mistake - if you search more on this topic online, it is consistent that 0 means NO Correlation, and between 0.25 to 0.5 is considered a weak correlation. I would say anything below 0.25 is much too weak to be able to say there is a correlation at all.

3 Likes

The text says “The farther the coefficient is from 0, the stronger the relationship.” For some reason, my brain found it easier to think of it as "the closer it is to +1 (positive coefficient) or -1 (negative coefficient) the stronger the correlation. However, is that thinking flawed?

1 Like

I am confused about the difference between slope and correlation coefficient. Could someone please check my understanding?

The correlation coefficient mimics how close to a linear relationship that 2 variables have.

The actual slope of said linear relationship could be any amount of steepness (not like it has to be y=x, in other words 1 for a positive coefficient)

1 Like

Good question.
I think you are correct.
A slope is about a line. Whereas a correlation is about a scatter, a set of points in two dimensional space.
The correlation coefficient is computationally “normalized”, such that the size of the slope, if there is one, does not affect the correlation coefficient.

Thanks ramtob

Does normalize mean the steepness difference between a gradient of 2 and 5 is ignored and it simply highlights how close to a linear relationship the variables have (be it 2 or 5)?

So a perfect linear relationship of 2 or 5 would appear the same on a scatter?

You’re welcome.
No, in general 2 and 5 will not look the same on a scatter. The size of the (accurate or approximate) slope does affect the scatter. But the correlation coefficient will be the same.

2 Likes