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I am a bit confused when to use, and how to identify the need to use the following transformations: log, quadratic and inverse, in a linear model.

Usually the models I'm looking at have around 8-10 quantitative variables.

My main issue is that I'm not sure what to look out for. For e.g in log, is the shape of the distribution and skew/kurtosis values sufficient? So if the skew/kurtosis indicates a distribution closer to a normal distribution, then it makes sense to use log on the variable?

Sycorax
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JohnFire
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  • This is a big subject, about which entire books have been written. The duplicate provides a brief introduction to part of it. For more details and examples also see https://stats.stackexchange.com/a/35717/919, https://stats.stackexchange.com/questions/4831, https://stats.stackexchange.com/questions/10975, https://stats.stackexchange.com/questions/11985, *etc.* – whuber Apr 30 '21 at 12:30
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    Thank you very much! @whuber – JohnFire May 22 '21 at 18:34

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