I am reading the "An Introduction to Statistical Learning" (Gareth James & alii, Springer) as a primer to machine learning.
I am reading the part in linear regressors, and learnt there are different tests for measuring correlations and significance of correlations between predictors (also named variables)- under the assumption that the model may be linear.
What about if the relationship between variables is (or assumed to be) non-linear ?
I also read that anyway many linear models concepts underpins a lot of statistical models.
Can you explain which could be a good practice when one has to model a context that he/she does not know yet?
Shall we test linearity because it is simpler , and if not found, we'll proceed with other tests ?
What about though, for testing correlations between variables in non-linear models ?
the model is non-linear? Do you mean if the relationship between your variables in non-linear? Depends on what kind of non-linearity we are talking about, can you be specific? $\endgroup$