'Individual P-values in Logistic Regression

I ran a logistic regression with like 10 variables (with R) and some of them have high P-values (>0.05). Should we follow the elimination techniques that we follow in multiple linear regression to remove insignificant variables? Or is the method different in logistic regression?

I'm new to this field so please pardon me if this question sounds silly.

Thank you.

logistic-regressionmulticollinearityparsimonious


Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source

Related Questions

GridSearchCV on LogisticRegression in scikit-learn

How to force PyStan to recompile a stan model?

Only two distinct values of probabilities in Logistic regression output

Error : PerfectSeparationError: Perfect separation detected, results not available

AssertionError: Wrong values for d['w'] | deeplearning specialization

How to extract coefficients from fitted pipeline for penalized logistic regression?

Can we use Normal Equation for Logistic Regression ?

How to reduce a categorical variable in a logistic regression model in R

Comparing MSE loss and cross-entropy loss in terms of convergence

Can I include covariates outside of the minimally sufficient set in a causal framework that aren't in the causal pathway?

Invalid labels for classification logistic regression model in pyspark databricks

SAS BigQuery Logistic Regression

Trying to extract data from repeated univariate logistic regressions on an imputed dataset

Error: $ operator not defined for this S4 class while running hoslem.test

Specifying Reference Category for Outcome Variable in Multinomial Logit Using SKlearn's LogisticRegression