Regression Analysis with R

Welcome to the Regression Analysis with R repository, a comprehensive guide and codebase for conducting regression analysis using the R programming language. This repository aims to help users gain a better understanding of regression techniques and how to apply them effectively using R.

Table of Contents

1. Introduction
2. Prerequisites
3. Getting Started
4. Included Techniques
5. Examples & Datasets
6. Contribution Guidelines
7. License

Introduction

Regression analysis is a powerful statistical method for modeling the relationship between a dependent variable and one or more independent variables. This repository provides a collection of R scripts, examples, and datasets to help you learn and apply various regression techniques in your projects.

Prerequisites

To get started with this repository, you'll need:

• A basic understanding of regression analysis concepts
• R and RStudio installed on your local machine (Download R, Download RStudio)

Getting Started

1. Clone the repository using git clone https://github.com/Briankim254/Regression-analysis-with-R.git
2. Navigate to the project directory using cd Regression-analysis-with-R
3. Open the R scripts and datasets in RStudio
4. Explore the examples and techniques provided, and apply them to your projects

Included Techniques

This repository covers various regression techniques, including:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression

Examples & Datasets

The repository includes practical examples and datasets to help you understand and apply regression techniques in real-world scenarios. These examples demonstrate the process of fitting regression models, evaluating their performance, and interpreting the results.

Contribution Guidelines

We welcome contributions to the repository, including new techniques, examples, or improvements to the existing content. To contribute, please follow these steps:

1. Fork the repository and create a new branch for your changes
2. Make your changes or additions to the project
3. Create a pull request and wait for a review from a team member

Please ensure that your code and documentation follow best practices for quality and clarity.

License

The Regression Analysis with R repository is licensed under the MIT License. This allows for open collaboration and sharing of the content while ensuring that contributors retain ownership of their work.