Basic Poisson Regression Analysis

Welcome to the "Basic Poisson Regression" repository, designed to provide a comprehensive exploration of Poisson regression, a powerful statistical technique widely used for modeling count data. This repository guides users through various stages of analysis, from initial data importation to model fitting and interpretation. Here's a breakdown of the procedures covered:

Data Importation and Exploration: The analysis begins with importing necessary libraries and loading the dataset. Through concise Python code, users can quickly examine the first few rows of the dataset, its descriptive statistics, column names, and data types. This initial step ensures a solid understanding of the dataset's structure and content.

Assessing Correlation: Understanding the relationships between variables is crucial in statistical modeling. The repository provides a detailed demonstration of calculating the correlation matrix and visualizing it using a heatmap. This allows users to identify potential multicollinearity issues and gain insights into variable relationships.

Visualization of Key Variables: Effective visualization is essential for understanding data distributions. The repository offers histograms for key variables such as 'mathpr' and 'langpr', providing a visual representation of their frequency distributions. Additionally, bar plots and box plots explore relationships between categorical variables like gender, ethnicity, school type, and days absent.

Feature Engineering: To enhance model performance, feature engineering techniques are introduced. Users learn how to create new variables or transformations of existing ones that better capture the relationship with the target variable, 'daysabs'. An example demonstrates the creation of an interaction term between 'mathpr' and 'langpr', showcasing the flexibility of feature engineering in capturing complex relationships.

Poisson Regression Model Fitting: The repository culminates in the fitting of a Poisson regression model to predict 'daysabs' based on various independent variables. Through step-by-step code explanations, users are guided in defining dependent and independent variables, converting categorical variables to dummy variables, adding an intercept term, and fitting the Poisson regression model using the statsmodels library. The model summary provides valuable insights into the significance of each predictor variable and overall model performance.

With clear explanations and professionally presented code examples, this repository serves as an invaluable resource for anyone looking to delve into the fundamentals of Poisson regression analysis. Whether you're a novice or an experienced practitioner, this repository equips you with the knowledge and tools needed to conduct robust count data analysis using Poisson regression.