Calculating Univariate and MultiVariate Logistic Regression with Python

Question

I have a simple data set of a number of variables and a single binary dependent variable. The data is stored in a data frame. When I use python's statsmodels.api and logit.fit() on the dataframe I am presented with a table detailing p values and confidence intervals etc for each of the variables. I need to calculate both univariate and multivariate p values and confidence intervals for each variable, however I am unsure what logit.fit is calculating - multivariate? If so how do I calculate univariate values - maybe just analyse a single variable at a time? Sample output below:

==============================================================================
Dep. Variable:           Vehicle        No. Observations:                11540
Model:                          Logit   Df Residuals:                    11515
Method:                           MLE   Df Model:                           24
Date:                Thu, 29 Aug 2019   Pseudo R-squ.:                 0.05443
Time:                        11:57:39   Log-Likelihood:                -7463.8
converged:                       True   LL-Null:                       -7893.4
                                        LLR p-value:                6.082e-166
=========================================================================================
                            coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------
Red                    0.0084      0.001      6.880      0.000       0.006       0.011
Green                  0.1345      0.041      3.293      0.001       0.054       0.215

Answer 1

A regression is multivariate when you try to explain your y using more than one explanatory variable. Each coefficient will have to be interpreted as the impact of a given x, while keeping all other values constant.

It is univariate instead when it takes only one variable.

Because of this, you must run univariate models independently from the multivariate, one by one.

Answer 2

I case somebody is searching for the same thing. The answer is fairly obvious. The results as presented are a multivariate analysis. To achieve a univariate analysis process a single variable at a time.