I have a question that I find really confusing regarding linear modelling and linear regression. I have expectation regarding the way some dependent variable (DV) are going to evolve with an independent variable (IV).
In order to check for a relationship between IV and DV, on several participants, I just computed the linear model by calculating Y as follow:
Y = XB + E
Therefore I used the weight of my linear model as B and my DV as X. Finally I just calculated a weighted sum. Then I tested for an effect by using a one sample t test on the various Y.
Well I'm confused because I don't see the difference between doing that and computing a linear regression by ordinary least square and calculating the slopes.
According to the two methods (weighted sum to the predictive X) or linear regression, I get different numerical values, but these values are correlated between them to 1.
If anyone can enlighten me about the difference, on the theoretical ground, between using one of these two methods, thank you!