Hi: I don't think my paper applies because it needs the same y. Also, I
don't think I follow
what you're doing now ( now you have different y and different x's ? ) so
I'd rather not comment but hopefully someone else does understand Good luck.
On Fri, Jan 27, 2012 at 4:10 PM, Michael <comtech....@gmail.com> 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...
>
>
>
> 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]]
>>>>> >
>>>>> >
>>>>> > ______________________________________________
>>>>> > R-help@r-project.org mailing list
>>>>> > https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> > PLEASE do read the posting guide
>>>>> > http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
>>>>> <http://www.r-project.org/posting-guide.html>
>>>>>
>>>>> > and provide commented, minimal, self-contained, reproducible code.
>>>>> >
>>>>> >
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>>
>>>>> ______________________________________________
>>>>> R-help@r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>
>>>>>
>>>>
>>>
>>
>
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