The aim of this article is to fit and interpret a Multiple Linear Regression and Binary Logistic Regression using Statsmodels python package similar to statistical programming language R.

- Data Background
- Aim of the modelling
- Data Loading
- Basic Exploratory Analysis
- Multiple Linear Regression Model Fitting/Estimation
- Interpretation of MLR Model Summary
- Binary Logistic Regression Model Fitting/Estimation
- Interpretation of the Logistic Regression Model Summary
- References

**Data Description**

The dataset contains several parameters which are considered important during the application for Masters Programs. The parameters included are as follows:

- I1: GRE Scores ( out of 340 )
- I2: TOEFL Scores ( out of 120 )
- I3: University Rating ( out of 5 )
- I4: Statement of Purpose Strength ( out of 5 )
- I5: Letter of Recommendation Strength ( out of 5 )
- I6: Undergraduate GPA ( out of 10 )
- I7: Research Experience ( either 0 or 1 )
- O: Chance of Admit ( ranging from 0 to 1 )

I: independent variable; O: outcome variable

**Inspiration**

This dataset was built with the purpose of helping students in shortlisting universities with their profiles [2]. The predicted output gives them a fair idea about their chances for a particular university.

Dataset Link: https://www.kaggle.com/mohansacharya/graduate-admissions

The aim of this article is to fit and interpret a Multiple Linear Regression and Binary Logistic Regression using Statsmodels python package similar to statistical programming language R. Here we will predict student admission in masters’ degree. Additionally, we will learn how we could interpret the coefficients obtained from both modelling approaches.

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