Linear regression and bivariate normal, is there a relationship?

[CantorSet]

Hi everyone,

This is not a homework question. I just want to understand an aspect of linear regression better. The book "Applied Linear Models" by Kutchner et al, states that a linear regression model is of the form

Y_i = B_0 + B_1 X_i + \epsilon_i

where
Y_i is the value of the response variable in the ith trial
B_0, B_1 are parameters
X_i is a known constant
\epsilon_i is a random variable, normally distributed.
Therefore, Y_i is also a random variable, normally distributed but X_i is a constant.

This confused me a bit because I always associated linear regression with the bivariate normal distribution. That is, the underlying assumption of linear regression is the data \{(x_1,y_1), (x_2,y_2),...,(x_n,y_x) \} is sampled from a bivariate normal distribution. In which case, both X and Y are random variables. But in the formulation above, X is a known constant, while \epsilon and therefore Y are the random variables.

So in summary, what is the connection (if any) is between linear regression as formulated by Kutner and the bivariate normal.

 

[Stephen Tashi]

the underlying assumption of linear regression is the data \{(x_1,y_1), (x_2,y_2),...,(x_n,y_x) \} is sampled from a bivariate normal distribution. In which case, both X and Y are random variables.

I've never seen a treatment of regression that made that assumption. Are you confusing linear regession with some sort of "total least squares" regression?
http://en.wikipedia.org/wiki/Total_least_squares

 

[CantorSet]

I've never seen a treatment of regression that made that assumption. Are you confusing linear regession with some sort of "total least squares" regression?
http://en.wikipedia.org/wiki/Total_least_squares

Thanks for responding, Stephen.

Yea, that was my own confusion for making that assumption. Thanks for clearing that up.

By the way, total least squares is just a generalization of linear regression in that the curve you're fitting the data points to can be polynomials with degrees higher than 1, right? Or is there more to total least squares?

 

[Stephen Tashi]

Total least squares treats both X and Y as random variables.