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Thread: Probit Regression?

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    Re: Probit Regression?




    Quote Originally Posted by bukharin View Post
    Only by convention. It's just another data transformation. It happens to be very useful for modelling binary proportions, which is why it's used for that; but as far as I know there is absolutely no statistical or mathematical reason why it shouldn't be used for arbitrary proportions between 0 and 1.
    Sure the logistic transformation is just a transformation. But logistic regression does need to be binomial data because that is the assumption that is being made. We could fit a logistic curve using nonlinear regression though.
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    Re: Probit Regression?

    Sure, but as Greta said, a proportion is just a combination of binary values. I forgot to mention in my initial post to use a robust variance estimator for the GLM model. I would be (very) interested to see a real-life example where using a GLM with a logit link, or using nonlinear regression to fit a logistic curve, gave meaningfully different results.

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    Re: Probit Regression?

    Quote Originally Posted by bukharin View Post
    Sure, but as Greta said, a proportion is just a combination of binary values.
    Yes a proportion is - but we would need to know the number of successes and number of trials. A "probability" could just be an estimate - I have no idea how the OP is getting these "probabilities" so we can't assume that it's actually a proportion.
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    Re: Probit Regression?

    That's true - in fact in general I think the OP (sorry Greta "original poster" ) needs to give more info, since it's unclear to me whether any of the approaches we've discussed would be appropriate.

    I am signing off but thanks for the interesting discussion.

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    Re: Probit Regression?


    After talking with some other people I misinterpreted what the data was saying. I will now be doing a Logistic regression of pass/fail (where any number of complaints is a pass/fail) and another regression that will be a poisson regression since there are days with multiple complaints. If I remember correctly, there is practically no overdispersion so I am not going to use quasiposson general linear model. Should I still use quasi? Anyways, thanks for all the help and I will keep my posts foreign friendly

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