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RE: st: chi2 on aggregate results


From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   RE: st: chi2 on aggregate results
Date   Mon, 10 May 2010 19:17:37 +0100

It's the same test; it's just a question of which way you look at it, in terms of null hypothesis or not, and of how pedantically (carefully) you word it. 

Nick 
[email protected] 

mai7777

Interesting. So if the tabi test is for the independence of the row &
column vars, how can I test that the proportions are statistically
different given the sample sizes?

On Mon, May 10, 2010 at 1:31 PM, Nick Cox <[email protected]> wrote:
> No; it is not. 47.22 and 76.67 are observed column percents, and not predictions of the hypothesis.
>
> The hypothesis being tested is that the row and column variables are independent. This may be clearer if you also use the -expected- option of -tabi-.
>
> Nick
> [email protected]
>
> mai7777
>
> Thanks for the quick response. I guess it was the "col" option that I
> was looking for, but just to confirm: Below, is the Pearson chi2(1) =
>  5.9421   Pr =  testing the hypothesis that the proportion of 76.67 in
> a sample size of 30 equal to the proportion of 47.22 in a sample size
> of 36?
>
>
> . tabi 23 17 \ 7 19, chi col
>           |          col
>      row |         1          2 |     Total
> -----------+----------------------+----------
>        1 |        23         17 |        40
>           |     76.67      47.22 |     60.61
> -----------+----------------------+----------
>        2 |         7         19 |        26
>           |     23.33      52.78 |     39.39
> -----------+----------------------+----------
>   Total |        30         36 |        66
>           |    100.00     100.00 |    100.00
>
>          Pearson chi2(1) =   5.9421   Pr = 0.015
>
>
> On Mon, May 10, 2010 at 1:15 PM, Alan Neustadtl
> <[email protected]> wrote:
>> Are you looking for something like this?
>>
>> - tabi 23 17  \ 7 19, chi col -
>>
>>
>>
>> On Mon, May 10, 2010 at 1:08 PM, mai7777 <[email protected]> wrote:
>>> hi,
>>> I'm trying to conduct a chi2 on aggregate results to test that the
>>> proportions of incidence across two treatments are the same:
>>>
>>> Treatment 1 has a total of 30 subjects, 23 of which had incidence
>>> Treatment 2 has a total of 36 subjects, 17 of which had incidence.

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