The summary table

The summary table below gives us a descriptive summary about the regression results.  

Output : 

                           Logit Regression Results                           
==============================================================================
Dep. Variable:               admitted   No. Observations:                   30
Model:                          Logit   Df Residuals:                       27
Method:                           MLE   Df Model:                            2
Date:                Wed, 15 Jul 2020   Pseudo R-squ.:                  0.4912
Time:                        16:09:17   Log-Likelihood:                -10.581
converged:                       True   LL-Null:                       -20.794
Covariance Type:            nonrobust   LLR p-value:                 3.668e-05
===================================================================================
                      coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------
gmat               -0.0262      0.011     -2.383      0.017      -0.048      -0.005
gpa                 3.9422      1.964      2.007      0.045       0.092       7.792
work_experience     1.1983      0.482      2.487      0.013       0.254       2.143
===================================================================================

Explanation of some of the terms in the summary table:

• coef : the coefficients of the independent variables in the regression equation.
• Log-Likelihood : the natural logarithm of the Maximum Likelihood Estimation(MLE) function. MLE is the optimization process of finding the set of parameters that result in the best fit.
• LL-Null : the value of log-likelihood of the model when no independent variable is included(only an intercept is included).
• Pseudo R-squ. : a substitute for the R-squared value in Least Squares linear regression. It is the ratio of the log-likelihood of the null model to that of the full model.

Prerequisite: Understanding Logistic Regression
Logistic regression is the type of regression analysis used to find the probability of a certain event occurring. It is the best suited type of regression for cases where we have a categorical dependent variable which can take only discrete values. 

The dataset : 
In this article, we will predict whether a student will be admitted to a particular college, based on their gmat, gpa scores and work experience. The dependent variable here is a Binary Logistic variable, which is expected to take strictly one of two forms i.e., admitted or not admitted.