Thanks!  I mostly intend for this app to be a teaching aide, i.e. soemthing I pull up while lecturing and walk them through what's going on.  

- Regression estimate:  The estimated slope of the line, or (to interpret it causally), the estimated effect that X has on Y

- Standard deviation:  How spread apart the values are from the mean.  For example, rolling a six-sided dice has smaller variance than rolling a 20-sided dice.  The 20-sided dice gives values that are more spread out.  In other words, higher SD = more noise.

You can tell how much noise there is by how sparead out the blue dots are around the line, yes.

Really cool, and I can see how it could help your students visualise and assimilate the concepts. When I studied statistics in school, it was merely about formulas, and not much about **understanding** the concepts and their real world application... In hindsight I think it was just a waste of time! 

Could you please define your terms for the lay audience? 
- regression estimate
- standard deviation

Also, can you tell if the data is noisy just by looking at the blue dots?

Thanks.

398ja

Are you saying ~math should be a subterritory of ~science?  I object!

> Also, I put this in ~science because it's the closest thing to ~math, but now I'm thinking we need a dedicated ~math territory.

Eagerly awaiting sub territories for this. Soon™

south_korea_ln

One cool thing about AI is that it's turned something like this from a multi-day project into a single afternoon thing that you can spin up in the coffee shop while waiting for your kids to finish their activities.

SimpleStacker

Just thought I'd show off something I made (vibe-coded) to help my students understand standard errors better.

You can think of standard errors as how much variance there will be in your regression estimate if you were to repeatedly sample the same underlying population over and over again.  In other words, *how much uncertainty is there in the estimated slope?*

If you take small samples, you'll get high standard errors, but if you take large samples, you'll get smaller standard errors.  But it also depends on how noisy the underlying data is.  If the relationship between X and Y in the population is noisy, you'll also get large standard errors.

science

math

, but now I'm thinking we need a dedicated 

Education

econ

![](https://m.stacker.news/134851)

Just thought I'd show off something I made (vibe-coded) to help my students understand standard errors better.

You can think of standard errors as how much variance there will be in your regression estimate if you were to repeatedly sample the same underlying population over and over again.  In other words, \*how much uncertainty is there in the estimated slope?\*

If you take small samples, you'll get high standard errors, but if you take large samples, you'll get smaller standard errors.  But it also depends on how noisy the underlying data is.  If the relationship between X and Y in the population is noisy, you'll also get large standard errors.

Also, I put this in ~science because it's the closest thing to ~math, but now I'm thinking we need a dedicated ~math territory.