Hi everyone,

I was wondering what model to pick when all the predictors are binomial distributions.

I have a website that's being visited through several (exclusive) channels. I know how many people enter a page from a certain channel. Then, they can go on to purchase and payment. However, not everyone makes it out to the end; most (95%) exit. I know how many people make a purchase in total but noy by channel, and I want to know the contribution of each channel.

The numbers of purchases are too low to use linear regression, so I want to use some count model. The only thing I can find is negative binomial regression, eg package pscl in R. However, then I'm modelling the exits instead of the visits and that seems to be a rather round-about way. This is how I model the exits right now:

When I search for "binomial regression" I get stuff on logistic regression, where the outcome is binary. That's not what I want.

Is there such a thing as "binomial regression" instead of "negative binomial regression"? It seems like such a logical thing but the only thing I see is the negative variant.

By the way, I tried approximating by linear regression. It works okayish, but it has obvious problems with the many zeroes. And some of the coefficients become negative, which is obviously impossible.

I was wondering what model to pick when all the predictors are binomial distributions.

I have a website that's being visited through several (exclusive) channels. I know how many people enter a page from a certain channel. Then, they can go on to purchase and payment. However, not everyone makes it out to the end; most (95%) exit. I know how many people make a purchase in total but noy by channel, and I want to know the contribution of each channel.

The numbers of purchases are too low to use linear regression, so I want to use some count model. The only thing I can find is negative binomial regression, eg package pscl in R. However, then I'm modelling the exits instead of the visits and that seems to be a rather round-about way. This is how I model the exits right now:

Code:

```
df$abandon <- df$buy_pag_visits- df$buyers
model.nb <- glm.nb(abandon ~ channel1 + channel2 + channel3 , link=sqrt) #sqrt seems to work better than log
```

Is there such a thing as "binomial regression" instead of "negative binomial regression"? It seems like such a logical thing but the only thing I see is the negative variant.

By the way, I tried approximating by linear regression. It works okayish, but it has obvious problems with the many zeroes. And some of the coefficients become negative, which is obviously impossible.

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