body { -ms-overflow-style: scrollbar; overflow-y: scroll; overscroll-behavior-y: none; } .errorContainer { background-color: #FFF; color: #0F1419; max-width: 600px; margin: 0 auto; padding: 10%; font-family: Helvetica, sans-serif; font-size: 16px; } .errorButton { margin: 3em 0; } .errorButton a { background: #1DA1F2; border-radius: 2.5em; color: white; padding: 1em 2em; text-decoration: none; } .errorButton a:hover, .errorButton a:focus { background: rgb(26, 145, 218); } .errorFooter { color: #657786; font-size: 80%; line-height: 1.5; padding: 1em 0; } .errorFooter a, .errorFooter a:visited { color: #657786; text-decoration: none; padding-right: 1em; } .errorFooter a:hover, .errorFooter a:active { text-decoration: underline; } #placeholder, #react-root { display: none !important; } body { background-color: #FFF !important; }

JavaScript is not available.

We’ve detected that JavaScript is disabled in this browser. Please enable JavaScript or switch to a supported browser to continue using twitter.com. You can see a list of supported browsers in our Help Center.

Terms of Service Privacy Policy Cookie Policy Imprint Ads info © 2023 Twitter, Inc.

Conversation

How do I interpret r values? Ppl usually say <3 is weak, 4-6 is medium, and 7+ is strong, but that this is dependent on field - but i can't find anybody saying more about how this changes per field! I'd love to see a chart of what's a medium strength across different fields

18

1

35

Replying to

I think of an R value as describing how much of the variation can be predicted if you know the relevant variable (interestingly, this is literally what it means). Therefore the more complex the prediction, the lower your standard for a strong correlation.

3

6

Replying to

this is the most useful comment on my question, thank you

1:59 AM · Nov 23, 2020

Replying to

You’re very welcome. 👍

Replying to

and

To be really precise, r^2 (as in squaring the r value) tells you what percentage of the variance in the dependent variable is predicted by your chosen independent variables.

1

3