I that case you would get the expected number of successes if everybody
had value 1 for X and the expected number of successes if everybody had
0 for X.
-- Maarten
--- [email protected] wrote:
> Does not that mean that this,
>
> > Turning of the effect of X:
> > 0.99%*1000000=9900
> >
> > Turning on the effect of X:
> > 0.29%*1000000=2900
> >
> > Then, the way I have understood this:
> > Discrete change, reduction induced by x=9900-2900=7000?
>
> Will be wrong? Here I have applied the on and off effects on the
> total sample.
> In the model n=1000000, and there is 500000 members of the program
> (x=1), thus 600000 are not member.
> I the example above I argue that x causes a reduction in the 1000000
> sample of 7000, due to the dicrete change of .7%.
>
> Alex
>
> -----Opprinnelig melding-----
> Fra: [email protected]
> [mailto:[email protected]] P� vegne av Maarten
> buis
> Sendt: 11. januar 2008 16:11
> Til: [email protected]
> Emne: Re: SV: st: From probit to dprobit to interpretation
>
> The separate probabilities need to be applied to their group, but the
> discrete change needs to be applied to the total sample.
>
> --- [email protected] wrote:
>
> > Thanks Maarten! That is very helpful. I guess what have been
> confusing
> > for me then is how to apply the predicted -0.7% discrete change
> (the
> > difference between turning on and off the effect), on the full
> sample
> > as I have done below, or only on those 500000 that are signed up to
>
> > the membership program. The difference offcourse making a huge
> impact
> > on the result.
> >
> > Best wishes,
> > Alexander
> >
> > -----Opprinnelig melding-----
> > Fra: [email protected]
> > [mailto:[email protected]] P� vegne av Maarten
> buis
> > Sendt: 11. januar 2008 14:16
> > Til: stata list
> > Emne: RE: st: From probit to dprobit to interpretation
> >
> > What you say is correct and there is no contradiction between all
> > these statements. From a probit model you can derive predicted
> > proportions, and with predicted proportions you can derive
> predicted
> > counts in your sample (and if you know the size of your population
> the
> > predicted counts in your population).
> >
> > Hope this helps,
> > Maarten
> >
> > --- [email protected] wrote:
> > I have estimated a probit model where n=1000 000 customers with
> only
> > 1 independent dummy variable (x) (for the sake of clarity), and get
>
> > the following estimated coefficients:
> >
> > y_pred=-2.33-0.431*x (x being significant)
> >
> > No the way I understand this is that these coefficients, except for
>
> > the signs and significance level, is hard to interpret. Thus, I can
>
> > derive it as a probability model, and then again calculate
> > probabilities from any table with standard cumulative normal
> > distribution values. Turning on and off x will give me the discrete
>
> > change, thus
> >
> > Turning off the effect of X thus gives me:
> > y_pred=-2.33-(0.431*0) and
> > Pr(z<2.33)=0.99%
> >
> > Tuning on the effect
> > y_pred=-2.33-(0.431*1)=-2.761 and
> > Pr(z<2.761)=0.29%
> >
> > The difference between these probabilities is the discrete change,
> and
> > this change can be directly estimated using a dprobit model in
> Stata?
> > Discrete change=0.99-0.29=-0.7%
> >
> > Most textbooks stops here, and I think that so far I am on the
> right
> > track - but I want to interpret this probability in terms of what
> this
> > x induced effect means in terms of my sample...
> >
> > In this particular model my sample is 1000000, and x=1 is a
> membership
> > program of which there are 500000 members. Would it be correct to
> > assume that the discrete change estimated above in terms of
> customers
> > could be interpreted as following:
> >
> > Turning of the effect of X:
> > 0.99%*1000000=9900
> >
> > Turning on the effect of X:
> > 0.29%*1000000=2900
> >
> > Then, the way I have understood this:
> > Discrete change, reduction induced by x=9900-2900=7000?
> >
> >
> >
> > -----------------------------------------
> > Maarten L. Buis
> > Department of Social Research Methodology Vrije Universiteit
> Amsterdam
> > Boelelaan 1081
> > 1081 HV Amsterdam
> > The Netherlands
> >
> > visiting address:
> > Buitenveldertselaan 3 (Metropolitan), room Z434
> >
> > +31 20 5986715
> >
> > http://home.fsw.vu.nl/m.buis/
> > -----------------------------------------
> >
> >
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>
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology Vrije Universiteit
> Amsterdam Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
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>
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
__________________________________________________________
Sent from Yahoo! Mail - a smarter inbox http://uk.mail.yahoo.com
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