 Probit-regrason on se model, joka on tullut logistic-regrason-analoustissa. Sitäkin nämä modelissa käytetään vain varioiden varioiden, ja ne ovat yksinkertaiset link-funksien. Onko nämä erilaista, että probit- ja logistik-modellissa ympäristöryhmät ympäristöryhmät ympäristöryhmät 1 tai 0, kun on ympäristöryhmä for the GLM? Linn-funksien on erilainen, joten logistik-modellissa usean logistik-link- ja probit-modellissa usean kumulativista, standard-normal-distribuutista. Kun katsotaan linja, sillä on ympäristöryhmä, ja ympäristöryhmä, sillä on logistik-ryhmä, sillä näkyvät erilainen. Tällä hetkellä, jos ympäristöryhmä ympäristöryhmä ympäristöryhmä ympäristöryhmä on erilainen, sillä on erilainen link-funksien. Joten, mitä se on? Miten haluat käyttää tämä kumulativista, standard-normal-distribuutista? The idea of a probit model is that we have, for each observation, we have a latent variable. So we have a latent variable that contains the linear prediction, so this is the part that we normally transform using link-funksien plus an error term that has variance of 1 and mean of 0. And that latent variable, y star, determines the value of the actual observed y. So if the y star receives a value greater than 0, then we have a positive value response to y, and if it receives a negative value or 0, then we have a negative or 0 response to y. The idea is that when we do a probit model, then we calculate the linear prediction. So let's say that the fitted value of the linear prediction is minus 1 here, then we draw a standard normal distribution around the fitted value, so the mean is minus 1, standard deviation is 1, then we look at 0. So what is the area right to the 0, what is the area left to the 0 under this curve, and the area to the right of 0 is the probability of observing 1. So when the fitted value increases, this distribution shifts right, and we can see that the area here would increase as well for that observation. So the probability of observing a positive value would increase if we increase the fitted value. The same way if we decrease the fitted value, then the probability of observing 1 decreases. This has some interesting theoretical features that are relevant for, for example, Selexon models, but for most practical applications, the results of logistic model and probit model, when you plot them, they will look almost the same. This shows the logistic curve in black and probit curve in red fitted with the same data. We can see that there's almost complete overlap, so it's largely a matter of convention which one we apply. But for certain models, the probit model is the right choice. If you don't know that you're in that situation, then which one you apply probably doesn't make any difference.