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Re: SV: SV: st: From probit to dprobit to interpretation


From   Maarten buis <[email protected]>
To   [email protected]
Subject   Re: SV: SV: st: From probit to dprobit to interpretation
Date   Fri, 11 Jan 2008 16:22:40 +0000 (GMT)

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/
-----------------------------------------


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