When to remove insignificant variables?

I’m working on logistic regression model. I checked the summary of the model which is built on 5 independent variables out which one is not significant with a P-value of 0.74.I wish to know that do we directly remove the variable or is there any other way to check for it’s significance?

A senior of mine suggested to do logarithmic transformation of the insignificant variable & look for correlation then. Will that count towards checking it’s significance.

model <- glm(Buy ~ a_score + b_score+ c_score+lb+p, data = history, family = binomial)

All variables come out to be significant with 2 or 3 stars apart from a_score which is shown insignificant.


Let me first ask this: What is the goal of the model? If you are only interested in predicting if a customer will buy, then statistcal hypothesis tests really aren’t your main concern. Instead, you should be externally validating your model via a validation/test prodecedure on unseen data.

If, instead, you are interested in examining which factors contribute to the probability of a customer buying, then there is no need to remove variables which fail to reject the null (especially in a stepwise sort of manner). Presumably, you included a variable in your model because you thought (from past experience or expert opinion) that it played an important part in a customer deciding if they will buy. That the variable failed to reject the null doesn’t make your model a bad one, it just means that your sample didin’t detect an effect of that variable. That’s perfectly ok.

Source : Link , Question Author : Community , Answer Author : Demetri Pananos

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