How can we find out the relationship between 2 or more dependent and independent categorical variables by not using chi square test? I'm trying to build a logistic regression model(in python) in some datasets which are having mostly categorical data, to check the relation between each and every independent variable with that of dependent variable i'm plotting the graphs for each of them, which is time consuming.
Is there any other easier way to check the relation between them other than chi-square test.
Thanks,
 A: It sounds like you are checking each predictor separately against the binary outcome. That's not a good idea with logistic regression, as is has an inherent omitted-variable bias. Omitting from a logistic regression any predictor associated with outcome will bias the coefficients for the included predictors. Unlike in linear regression, the omitted predictor doesn't even need to be correlated with the included predictors.
The best way (and potentially even the easiest) is to evaluate a logistic multiple regression including all predictors together. Depending on the number of predictors and cases you might need to deal with overfitting by intelligently restricting or combining predictors or by using penalized approaches like ridge regression or LASSO. The coefficients returned by the logistic regression model will show how each of your predictors is associated with the log-odds of outcome when all the other predictors are also taken into account. That's usually of much more interest than single-predictor relationships with outcome.
