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I have a low-level knowledge of statistics. Hoping there can be somebody who can answer my queries. So, I have these dataset and I want to run a multiple linear regression. Sadly my data and the residuals cannot be normalized ever after I transformed my data.

Then, I tried to look for another method to use, I found the Generalized Linear Model. My problem with the GLM is that I cannot identify what "Family" and "Link" to use. I tried to read references and reviews but I cannot find anything in which family data will fall.

My dataset is like this. The dependent variable is continuous but bounded. The values range from 0 to 1 (any number between 0 and 1). My independent variables are a mix of ordinal and ratio variables. I am now looking at quasi family but I am still not sure.

Also, if you know other methods that might help, please send your suggestions.

Thank you in advance!

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    $\begingroup$ By far the best approach is based on understanding the response variable, because whenever possible you want to select a model that is appropriate for its conditional distribution. What can you tell us about that? $\endgroup$ – whuber Apr 24 at 14:13
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For choosing a "type" of regression, the first step is to look at the DV. When the DV is bounded (as yours is) beta regression is often a good alternative to OLS regression.

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