Shouldn't this be similar (if not equivalent ) to examining the
leverage or influence of z1 and z{n+1} in the full model of Y ~
beta*Z[1:(n+1)] ?
--
David
On Jan 27, 2012, at 7:46 PM, Michael wrote:
> Yes, these observations are measured at equal-spaces...
>
> And the "n"-axis is the time axis...
>
> Thank you!
> On Fri, Jan 27, 2012 at 3:54 PM, David Winsemius <dwinsem...@comcast.net
> > wrote:
>
> On Jan 27, 2012, at 4:10 PM, Michael wrote:
>
> I changed the notation for data from x to z...
>
> That's it. Should be very clear now... Thanks!
>
> Data: z1, z2, ..., z_{n+1}
>
> y1 = z_1,z_2,......... z_n
> y2 = z_2, z_3,......... z_{n+1}
>
> x1 = 1, ..., n
> x2 = 1, ..., n
>
> y1 = A1+ x1 * B1 + epsilon_1
> y2 = A2 + x2 * B2 + epsilon_2
>
> H0: B1 and B2 are statistically significally different...
>
> So in hopes of clarifying, ....So you want to test whether estimated
> slopes are different after you slide a data-window one unit to the
> right on the y-scale. Are you willing to say anything else about the
> mathematical properties of Y? is it for instance measured at equal
> time intervals?
>
> --
>
>
>
>
>
> On Fri, Jan 27, 2012 at 2:41 PM, Mark Leeds <marklee...@gmail.com>
> wrote:
>
> now i'm confused because you first use y_1, y_2 and then use y
> later. I
> would take
> a look at that earlier paper i mentioned. I think it's along the
> lines of
> what you want. Unfortunately. I don't have a computer copy of it. I
> got it
> from a library service where I once worked.
>
>
> mark
>
>
> On Fri, Jan 27, 2012 at 3:35 PM, Michael <comtech....@gmail.com>
> wrote:
>
> Thanks all.
>
> Here are a more clear statement of my question:
>
> Data: z1, z2, ..., z_{n+1}
>
> y1 = z_1,z_2,......... z_n
> y2 = z_2, z_3,......... z_{n+1}
>
> x1 = 1, ..., n
> x2 = 1, ..., n
>
> y = A1+ x1 * B1 + epsilon_1
> y = A2 + x2 * B2 + epsilon_2
>
> H0: B1 and B2 are statistically significally different...
>
> Any more thoughts?
>
> Thanks a lot!
>
> On Fri, Jan 27, 2012 at 1:39 PM, Mark Leeds <marklee...@gmail.com>
> wrote:
>
> 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(nï¼1).
> beta1 is obtained from regressing x1 to xn onto 1 to n.
>
> beta2 is obtained from regressing x2 to x(nï¼1) onto 1 to n.
>
> Is there a way in R to test whether beta1 and beta2 are statistically
> different?
>
> Thanks a lot!
>
> [[alternative HTML version deleted]]
> .
>
> David Winsemius, MD
> West Hartford, CT
>
>
David Winsemius, MD
West Hartford, CT
[[alternative HTML version deleted]]
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