I'm hoping there's someone here who's good at

>>7733142
Is that... Boxxy?

>>7733160
I guess so

>>7733160
She's far too attractive to be Boxxy and only has 300 layers of makeup as opposed to over 9000.

>>7733163
Did you reverse image search it or did you literally guess? I really don't want to go through that effort because my browser extension broke.

>>7733166
I searched, and apparently it is.

>>7733179
Fuuuuck.

She's changed a lot. Damn.

>>7733160
FFFFFFFFFFFFFff--

>>7733142
Is this what they teach statisticians? You're over complicating something that is ridiculously simple if you understand random variables.

All you are doing is subtracting the co-variance. Look up the formula for adding variances, that may help. Do you know linear algebra? Thinking about this in terms of eigenvectors and eigenvalues is a lot simpler.

>>7733166
I literally guessed. I got this image off of /b/ right before I started the thread.

>>7733194
>You're over complicating something that is ridiculously simple
Well, then I'm hoping you could help me understand. I'm not a statistician, quite far from it actually and this is my first experience with regression to be honest.

Anyway, if this procedure is subtracting covariance, does it imply that variables b and c show a negative quadratic relationship with each other?

>>7733241

>Nothing to do with science or math

>>7733317
It's an eye catcher though

File: ce0ff0f3a9a94693ff250b84389ac83c.png (2KB, 490x23px) Image search: [Google]

2KB, 490x23px

>>7733213
This formula is where I believe the quadratic relationship is coming from.

I don't completely understand your original explanation.
>Now, if I instead use both variable a and b in a single regression analysis (so they are in the same model) this should give us coefficients that indicate the relationships between variables a and b to variable c, but independent of each other. By doing this we discard shared variance between a and b.

They will not be independent, your model will just fail as it assumes independence.

>>7733350
I fear I'm missing the point.

Where exactly does that equation come from? Isn't my model imposing independence since we're removing shared variance? It might also be worth mentioning that a and b are uncorrelated.

>>7733375
What step in what you describes is removing the shared variance?

>>7733398
Including two predictors in the model is equivalent to hierarchical regression where I first regress a onto c, and then use the residuals (r) for another regression of b onto r. So we're looking at independent contributions of a and b to the variance of c.

But please correct me if that's wrong.

please respond

>>7733410
Oh okay. I see. That works. That would make a and b's contributions orthogonal to each other thus linearly independent. What quadratic relation are you seeing?

>>7733499
The same ones as mentioned in the OP.

>>7733514
Show it mathematically. I don't see why one would occur. Unless you are implying that distance is somehow the quadratic relation.

>>7733715
>I don't see why one would occur.
That is exactly the problem, neither do I. It's an empirical finding, and it doesn't make much sense to my why it happens.
>Show it mathematically.
If I could that'd be excellent, but I don't nearly know enough about the math to do that.

>>7733164
Pretty sure she's Boxxy.

>Realize you first saw Boxxy when you were 16.
>You're now 22.
What.

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