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I'm modeling claim count using glm. My data contains both continuous and categorical data. Data is aggregated to 6000 risk profiles(referenced here by ID). Exposure for a policyholder for 1 year

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Here a summary of all columns: enter image description here

In fact, As far as I know, I must probably convert my continuous variables to categorical, is that all I have to do? I'll be modeling my glm using first Poisson and N.B type 2, I also think I might use exposure as an offset? Also, should ID and Year, stay as an explanatory variable?

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  • $\begingroup$ you need to look at the data before deciding whether to convert it to categorical. It's not a must. also some of your columns, e.g Rent are character.. hope that is intended. ID is a unique entry, you cannot use it $\endgroup$
    – StupidWolf
    Commented May 29, 2020 at 9:21
  • $\begingroup$ thanks for your comment, but if they are caracters, must it be converted to numbers or ligical when its 2 choices? $\endgroup$
    – roger
    Commented May 29, 2020 at 10:43

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From my understanding, transfomring categorical to dummy variable may not help you in the end.

https://online.stat.psu.edu/stat504/node/169/

This article shows specific steps about poisson regression modelling process in considering overdispersion and engineering variables.

Using offset may help to address over-dispersion, the article is worth a good read, after using offset, the model appearted to be good fit.

Regards, Dan

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