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RE: st: Confidence Interval for Proportion


From   "Martin Weiss" <[email protected]>
To   <[email protected]>
Subject   RE: st: Confidence Interval for Proportion
Date   Tue, 11 Mar 2008 20:34:15 +0100

Jeff,

thanks for the reply, but am I still missing something here? I did
experiment with the " r(N)-1", but discarded the possibility as it did not
provide the correct lower and upper bound... Indeed,

************************
sysuse auto, clear
proportion rep78
matrix define A=e(b)
count if rep78!=.
*Std error
local stderr= sqrt(A[1,1]*(1-A[1,1])/`=`r(N)'-1')
*Upper/Lower Bound for proportion of "1"
di A[1,1]+invnormal(1-0.05/2)*`stderr'
di A[1,1]-invnormal(1-0.05/2)*`stderr'
************************

still gives the wrong numbers. Have you told us the whole story?

Martin Weiss
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-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Jeff Pitblado,
StataCorp LP
Sent: Tuesday, March 11, 2008 7:08 PM
To: [email protected]
Subject: Re: st: Confidence Interval for Proportion

Martin Weiss <[email protected]> is using the -proportion-
command
and has a question about how standard errors are computed:

> Dear Statalisters,
> 
> try this in Stata:
> 
> ************************
> sysuse auto, clear
> proportion rep78
> matrix define A=e(b)
> matrix define B=e(V)
> count if rep78!=.
> *Upper/Lower Bound for proportion of "1"
> di A[1,1]+invnormal(1-0.05/2)*sqrt(A[1,1]*(1-A[1,1])/`r(N)')
> di A[1,1]-invnormal(1-0.05/2)*sqrt(A[1,1]*(1-A[1,1])/`r(N)')
> *Standard Error for "1"
> *Mistake obviously there...
> di sqrt(A[1,1]*(1-A[1,1])/`r(N)')
> ************************
> 
> Then let me know: why do I not hit the correct CI for the proportion of
"1"
> in the repair record? Something`s wrong with the standard error, I do not
> know what, though...

Using Martin's example Stata code, -proportion- effectively computes the
standard error via

	sqrt(A[1,1]*(1-A[1,1])/(r(N)-1))

This is explained (rather tersely, I'll admit) in the 'Methods and Formulas'
section of -[R] proportion-.

	"Proportions are means of indicator variables; see -[R] mean-."




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