Calculating the Y' variable

#1
Hi everyone.

I wanted to see if my data could be described better with a non-linear relationship. So I calculated a new Y' variable, based on the coefficients I got from the regression model. Just to make sure I didn't make any mistakes, I rerun the regression, with only the new Y' as the predictor. The results were the same. Then, I used the curve fit option in SPSS to look for a better relationship

However, when I try to do the same thing with an interaction variable, this doesn't seem to work. The regression loses a lot of R^2.
Why is this and what can I do to make it work?

Thanks
 

obh

Active Member
#2
Do you mean you use the same Y' but there is interactions in Y calculation? (first: Y=b+a2x1+a2x2+... via Y=b+a1x1+a12x1x2+a2x2))
 
#3
No. What I mean is that when I enter the interaction variables I recalculate Y' (Lets call it Y''), according to the new coefficients, and also include the interaction variable. However, while Y' explains all the variance in it's model, Y'' does not.
 

obh

Active Member
#4
Hi Shachar

I think this is what I wrote above...
First: Y=b+a2x1+a2x2. =>new regression based only over estimate Y: ŷ => Y'=b'+a' ŷ high R
second: Y=b+a2x1+a2x2+a12x1x2 =>new regression based only over estimate Y: ŷ=> Y''=b''+a'' ŷ

If not please write an example as I did.
 

obh

Active Member
#6
Hi Shachar,

I did a simple test and didn't get the same results as you described.
Can you please attach an excel file with the data you use?
Toda
 
#7
Hi obh. Sorry for the late reply.

Unfortunately, I can't upload the data. It's not something I can share.
Thank you for your effort!
 

obh

Active Member
#8
No worry Shachar,

I feel that you may do a mistake, or I may misunderstand you.
So when you have some extra time you can try to create a simple example without revealing your real data :)