Non-significant variables in a logistic regression model

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SUMMARY

In logistic regression modeling, non-significant variables can be included in risk index calculations if there is sufficient data to statistically determine their insignificance. However, including irrelevant predictors can lead to complications, especially when the dataset is small relative to the number of predictors. It is essential to focus on relevant variables to enhance the model's interpretability and performance.

PREREQUISITES
  • Understanding of logistic regression modeling
  • Familiarity with statistical significance and p-values
  • Knowledge of model selection techniques
  • Experience with data analysis and interpretation
NEXT STEPS
  • Research the impact of non-significant variables on logistic regression outcomes
  • Learn about model selection criteria such as AIC and BIC
  • Explore techniques for variable selection in regression analysis
  • Study the implications of multicollinearity in regression models
USEFUL FOR

Data scientists, statisticians, and researchers involved in predictive modeling and risk assessment using logistic regression.

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.
 

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