You are looking at comparing models, not comparing covariates really. If you have the models in mind, that is, you want to compare model $a$ to model $b$, you can use AIC, which estimates the fit of a model while taking its complexity into account: aic(model_a,model_b) and choose the lower outcome AIC model.

Another method, is using the adjusted $R^2$ of all the models you want and compare them. Generally, the model with the smallest amount of covariates and the highest adjusted $R^2$ is your winner. If you want to check all covariate combinations instead of manually choosing specific models to compare, you can use an all subsets regression from library(leaps) in R. This will allow you to choose the number of best combinations to show, which you can also plot() for a visual aid. If this is what you need, see this great tutorial from R-studio pubs.

You are looking at comparing models, not comparing covariates really. If you have the models in mind, that is, you want to compare model $a$ to model $b$, you can use AIC, which estimates the fit of a model while taking its complexity into account: aic(model_a,model_b) and choose the lower outcome AIC model.

Another method, is using the adjusted $R^2$ of all the models you want and compare them. Generally, the model with the smallest amount of covariates and the highest adjusted $R^2$ is your winner. If you want to check all covariate combinations instead of manually choosing specific models to compare, you can use an all subsets regression from library(leaps) in R. This will allow you to choose the number of best combinations to show, which you can also plot() for a visual aid. If this is what you need, see this great tutorial from R-studio pubs.

It might be worth noting that choosing the fittest model should be done with care. I come from social sciences, and the way I see it, sometimes it is worth adding non-significant covariates to the model - it they have a meaning. As a simple example, if I check for predictors of salary, and gender is non-significant, than perhaps some other covariate made it non-significant. If so - that is of interest. If not, than perhaps there is no gender pay-gap - also interesting. My point is Choose a model with care, not just by looking at the numbers - they do not always convey the entirety of the picture.

You are looking at comparing models, not comparing covariates really. If you have the models in mind, that is, you want to compare model $a$ to model $b$, you can use AIC aic(model_a,model_b) and choose the lower outcome AIC model.

Another method, is using the adjusted $R^2$ of all the models you want and compare them. Generally, the model with the smallest amount of covariates and the highest adjusted $R^2$ is your winner. If you want to check all covariate combinations instead of manually choosing specific models to compare, you can use an all subsets regression from library(leaps) in R. This will allow you to choose the number of best combinations to show, which you can also plot() for a visual aid. If this is what you need, see this great tutorial from R-studio pubs.

You are looking at comparing models, not comparing covariates really. If you have the models in mind, that is, you want to compare model $a$ to model $b$, you can use AIC, which estimates the fit of a model while taking its complexity into account: aic(model_a,model_b) and choose the lower outcome AIC model.

Another method, is using the adjusted $R^2$ of all the models you want and compare them. Generally, the model with the smallest amount of covariates and the highest adjusted $R^2$ is your winner. If you want to check all covariate combinations instead of manually choosing specific models to compare, you can use an all subsets regression from library(leaps) in R. This will allow you to choose the number of best combinations to show, which you can also plot() for a visual aid. If this is what you need, see this great tutorial from R-studio pubs.