Hi Richard: I read michael's question as meaning that he says two
univariate no intercept
regression model where the predictor data is different in each model so that

x1 = x_11,x_12,.........  x_1n
x2 = x_21, x_22,......... x_2n
y = y_1, .....y_n

y = x1 * B1 + epsilon_1
y = x2 * B2 + epsilon_2

and he wants to see which coefficient ( B1 or B2 ) "works" better. But I
could be wrong
which I only realized after reading your recommendation. michael: if i'm
wrong, then disregard the paper reference that I sent earlier.


Mark



On Fri, Jan 27, 2012 at 2:29 PM, Richard M. Heiberger <r...@temple.edu>wrote:

> It looks like you might be asking for the anova() on two models.
>
> M1 <- lm(y ~ x1 + x2 + x3, data=something)
> M2 <- lm(y ~         x2 + x3, data=something)
> anova(M1, M2)
>
> Please send a reproducible example to the list if more detail is needed.
>
> Rich
>
> On Thu, Jan 26, 2012 at 11:59 PM, Michael <comtech....@gmail.com> wrote:
>
> > Hi al,
> >
> > I am looking for a R command to test the difference of two linear
> > regressoon betas.
> >
> > Lets say I have data x1, x2...x(n+1).
> > beta1 is obtained from regressing x1 to xn onto 1 to n.
> >
> > beta2 is obtained from regressing x2 to x(n+1) onto 1 to n.
> >
> > Is there a way in R to test whether beta1 and beta2 are statistically
> > different?
> >
> > Thanks a lot!
> >
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> >
> >
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> >
> >
>
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>
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