Simple Linear Regression with Statsmodels

This repository contains a straightforward implementation of a Simple Linear Regression model using Python. The primary goal of this project is not to solve a complex real-world problem, but to serve as a clear, educational demonstration of the fundamental principles of statistical modeling and how to interpret its results using the statsmodels library.

📖 About The Project

Linear Regression is one of the most fundamental algorithms in statistics and machine learning. This project walks through the essential steps of building a regression model:

1. Defining the independent (X) and dependent (Y) variables.
2. Adding a constant to the independent variable to account for the model's intercept.
3. Fitting an Ordinary Least Squares (OLS) model to the data.
4. Generating and interpreting a comprehensive summary of the model's performance.

This serves as a foundational exercise for anyone learning data science or statistical analysis.

🛠️ Tech Stack

• Language: Python
• Libraries:
  • numpy: For numerical operations and array management.
  • statsmodels: A powerful Python module for statistical modeling and econometrics.

🚀 How to Run

To run this project on your local machine, please follow these steps:

1. Clone the repository:

git clone https://github.com/ifanhakm/nama-repository-anda.git
cd nama-repository-anda

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required libraries:

pip install numpy statsmodels

4. Execute the Python script:

python nama_file_anda.py

📊 Results & Interpretation

Running the script will print a detailed OLS Regression Results summary to your console. This summary provides crucial statistical information about the model, including:

• R-squared: A measure of how well the model explains the variance in the dependent variable.
• coef: The estimated coefficients for the constant (intercept) and the independent variable (slope).
• P>|t|: The p-value, which helps determine the statistical significance of each variable. A low p-value (typically < 0.05) indicates that the variable is a significant predictor.
• Confidence Interval: The range in which the true coefficient is likely to fall.

This output is key to evaluating the model's validity and understanding the relationship between the variables.