From formula to intuition
Linear regression is often introduced through equations first. This dashboard turns the method into an interactive learning space where students can see how data, model estimation, and interpretation connect.
Understanding linear regression is a fundamental step in learning econometrics, statistics, and data analysis. However, many students find it challenging to connect theoretical concepts with practical applications. To help bridge this gap, I developed an interactive dashboard using R Shiny that allows users to explore and perform linear regression analysis in a simple and intuitive environment.
Upload data
Start with a dataset and move directly into exploration without complex setup.
Explore variables
Visualize relationships and detect patterns before estimating the model.
Estimate the model
Run linear regression and review coefficients, fit, and significance indicators.
Interpret results
Connect statistical outputs with econometric meaning through immediate feedback.
The application has been designed as both a learning and analytical tool. Users can upload data, visualize relationships between variables, estimate regression models, and interpret key statistical outputs through an interactive interface. By providing immediate visual feedback, the dashboard helps users better understand concepts such as model estimation, goodness of fit, coefficient interpretation, and statistical significance.
Key Features
- Interactive linear regression estimation
- Data visualization and exploratory analysis
- Dynamic model outputs and statistical indicators
- User-friendly interface suitable for students and researchers
- Educational approach for learning econometric concepts
Why it matters
This project reflects my interest in combining economics, data science, and technology to create practical tools that support teaching, learning, and research. It can be used by students discovering econometrics for the first time, as well as by researchers looking for a lightweight application to perform quick regression analyses.
I welcome feedback, suggestions, and contributions that can help improve the application and make it even more useful for the academic and data science communities.