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
>
>
[[alternative HTML version deleted]]
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