# Calculating Univariate and MultiVariate Logistic Regression with Python

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


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.