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When to remove insignificant variables?

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

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

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

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

2 Answers
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6

$begingroup$

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.

share|cite|improve this answer

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Upvoted for excellence of the answer.
  $endgroup$
  – James Phillips
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @JamesPhillips Thanks
  $endgroup$
  – Demetri Pananos
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  +1 Removing predictors potentially related to outcome (even if "insignificant") is tricky in logistic regression, given its inherent omitted-variable bias. Removing a predictor related to outcome can lead to bias in the estimates of the coefficients of the retained predictors, even if the retained predictors aren't correlated with the removed predictor.
  $endgroup$
  – EdM
  8 hours ago
  

  
  
  
  

add a comment | 

1

$begingroup$

Have a look at the help pages for step(), drop1() and add1(). These will help you to add/remove variables based on AIC. However, all such methods are somewhat flawed in their path dependence. A better way would be to use the functions in the penalised or glmnet package to perform a lasso regression.

share|cite|improve this answer

answered 9 hours ago

Feakster

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6

$begingroup$

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.

share|cite|improve this answer

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Upvoted for excellence of the answer.
  $endgroup$
  – James Phillips
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @JamesPhillips Thanks
  $endgroup$
  – Demetri Pananos
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  +1 Removing predictors potentially related to outcome (even if "insignificant") is tricky in logistic regression, given its inherent omitted-variable bias. Removing a predictor related to outcome can lead to bias in the estimates of the coefficients of the retained predictors, even if the retained predictors aren't correlated with the removed predictor.
  $endgroup$
  – EdM
  8 hours ago
  

  
  
  
  

add a comment | 

6

$begingroup$

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.

share|cite|improve this answer

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Upvoted for excellence of the answer.
  $endgroup$
  – James Phillips
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @JamesPhillips Thanks
  $endgroup$
  – Demetri Pananos
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  +1 Removing predictors potentially related to outcome (even if "insignificant") is tricky in logistic regression, given its inherent omitted-variable bias. Removing a predictor related to outcome can lead to bias in the estimates of the coefficients of the retained predictors, even if the retained predictors aren't correlated with the removed predictor.
  $endgroup$
  – EdM
  8 hours ago
  

  
  
  
  

add a comment | 

$begingroup$

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.

share|cite|improve this answer

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

$endgroup$

share|cite|improve this answer

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619

answered 8 hours ago

Demetri PananosDemetri Pananos

2,121619