Can a correlation be used in logistic regression?

Can a correlation be used in logistic regression?

Yes, since the outcome variable is binary, you can use logistic regression. You can also use Pearson or Spearman or other types of correlations between each independent (predictor) variable and your outcome. Have a read on the different types of correlations and in what scenairos they can be used, here is an example.

Which is the default prior distribution in logistic regression?

Gelman et al. have a nice paper describing a default prior distribution on coefficients in logistic regression, which pairs well with the bayesglm function they developed in the arm package in R, which allows you to easily build and summarize logistic and other generalized linear models. You can read their paper on the subject here.

Can a weakly correlated variable be included in a model?

You can include weakly correlated variables in your model, but any covariate pairs with r > 0.7 should be reduced to the most relevant predictor. Covariates with near-zero variance you can exclude from your analysis, since they don’t contribute to your model accuracy but increase the degrees-of-freedom.

What’s the difference between predicting variables and finding out correlation?

There’s a difference between predicting variables and finding out correlation. Logistic regression is predictor, more specifically, binary classifier. “Classifier” means that it tries to assign some class to every observation. “Binary” means that there are exactly 2 classes.

When do you need to make assumptions in logistic regression?

Logistic regression is a method that we can use to fit a regression model when the response variable is binary. Before fitting a model to a dataset, logistic regression makes the following assumptions: Logistic regression assumes that the response variable only takes on two possible outcomes. Some examples include:

When to use ordinal regression instead of logistic regression?

If there are more than two possible outcomes, you will need to perform ordinal regression instead. Logistic regression assumes that the observations in the dataset are independent of each other. That is, the observations should not come from repeated measurements of the same individual or be related to each other in any way.

Why is multicollinearity a problem in logistic regression?

This means that multicollinearity is likely to be a problem if we use both of these variables in the regression.