A Non-significant variables in a logistic regression model

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In logistic regression models, non-significant variables can still be included in risk index calculations, as they may yield a coefficient close to zero. However, including irrelevant predictors can complicate the model, especially when data is limited. The focus should be on maintaining relevant variables to ensure model accuracy and interpretability. Ultimately, the decision to include non-significant variables depends on the context and the amount of available data. Careful consideration is essential to avoid skewing results with unnecessary predictors.
nyla
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If some variables of a logistic regression models are non significant, should they be considered for a risk index calculation?
Should the logistic model include only relevant variables?

Thanks for the attention.
 
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If you have enough data to statistically determine the variables are non significant, then you can include them and you should get a 0 coefficient (probably not literally). Regression only gets interesting when your data is small for the number of predictors you want to include, in which case throwing in extra predictors you know are bad is going to cause issues.
 

Thread 'Deductive proof in logic formal systems'

I was reading documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...
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