Non-significant variables in a logistic regression model

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  • #1
nyla
1
0
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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  • #2
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.
 

Related to Non-significant variables in a logistic regression model

1. What are non-significant variables in a logistic regression model?

In a logistic regression model, non-significant variables are those that do not have a statistically significant impact on the outcome variable. This means that the p-value for these variables is greater than the chosen level of significance, typically 0.05.

2. Why are non-significant variables important to consider in a logistic regression model?

Non-significant variables are important to consider because they can affect the accuracy and reliability of the model. Including non-significant variables can lead to overfitting, which means the model is too closely fitted to the training data and may not perform well on new data.

3. How can non-significant variables be identified in a logistic regression model?

Non-significant variables can be identified by looking at the p-values for each variable in the model. Variables with p-values greater than the chosen level of significance are considered non-significant.

4. Should non-significant variables be removed from a logistic regression model?

It is generally recommended to remove non-significant variables from a logistic regression model. This can improve the model's performance and make it more interpretable. However, it is important to consider the context of the study and the potential impact of removing these variables.

5. Can non-significant variables become significant in a different logistic regression model?

Yes, non-significant variables can become significant in a different logistic regression model if the model is fitted to a different dataset or if different variables are included in the model. It is important to carefully consider the variables included in the model and their potential impact on the outcome variable.

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