The Multicollinearity Diagnostic Tool is an interactive and data-driven solution designed to help analysts, researchers, and decision-makers detect and address multicollinearity in regression models. Multicollinearity occurs when predictor variables in a regression model are highly correlated, which can lead to unstable parameter estimates and unreliable statistical inference. This tool allows users to input predictor variables and compute diagnostic metrics such as Variance Inflation Factor (VIF), correlation matrix, and eigenvalues to identify multicollinearity issues. It provides actionable insights into the severity of multicollinearity and suggests potential remedies.

Key Features:

1. Dynamic Input Fields: Users can input datasets with up to five predictor variables or upload a CSV file containing their data.
2. Multicollinearity Diagnostics: Automatically compute metrics such as Variance Inflation Factor (VIF), correlation matrix, condition index, and eigenvalues.
3. Interactive Charts: Visualize correlations between variables using heatmaps and scatter plots for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in variables affect multicollinearity diagnostics.
5. PDF Export Functionality: Generate downloadable reports summarizing multicollinearity diagnostics for presentations or sharing with stakeholders.
6. Modern and Stylish Design: A sleek interface with vibrant colors, animations, and clear typography to enhance user engagement.
7. Fully Responsive: Optimized for all devices, ensuring seamless functionality on desktops, tablets, and mobiles.
8. Self-Contained Container: The tool operates within its own container, ensuring no interference with the page header or footer.

Use Cases:

• Researchers diagnosing multicollinearity in regression models for academic studies.
• Data scientists improving predictive models by addressing multicollinearity issues.
• Academics teaching concepts like VIF, correlation matrices, and eigenvalue decomposition.

How It Works:
Users input predictor variables for a regression model or upload a CSV file containing their data. The tool computes multicollinearity diagnostics such as VIF, correlation matrix, condition index, and eigenvalues. Results are displayed in tables and visualizations, allowing users to interpret the severity of multicollinearity. Users can customize inputs dynamically, analyze scenarios, and download detailed reports in PDF format for further analysis or sharing with stakeholders.