I understand that stepwise regression is computationally intensive in general but is it only "suitable" in cases where you can ignore several variables from the model due to statistical insignificance, and is there a threshold for this to hold true?

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    $\begingroup$ What do you want to use the model for? For prediction or inference? $\endgroup$ – Stephan Kolassa Mar 9 at 18:40
  • $\begingroup$ See: stats.stackexchange.com/questions/215154/… $\endgroup$ – kjetil b halvorsen Mar 9 at 19:05
  • $\begingroup$ Many contributors to this site would argue that stepwise regression is almost never "suitable". Please look over that page and the page linked in another comment about whether variable selection is needed for predictive models. Editing your question to provide more specifics, based on what you've seen in those linked pages and your answer to the comment about prediction versus inference, is more likely to get you a useful answer. $\endgroup$ – EdM Mar 9 at 21:00
  • $\begingroup$ @StephanKolassa why does it matter? What does it change in the case of inference for example? $\endgroup$ – Digio Mar 9 at 21:39
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    $\begingroup$ @Digio: Bonferroni answers a different question. The problem in stepwise model selection is that the different tests are dependent, therefore so are their p values. To be honest, I am not aware of any justified way of correcting for the bias induced by model selection. p values only have their intended meaning in completely prespecified models. $\endgroup$ – Stephan Kolassa Mar 10 at 17:15

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