Enhancing Regression Diagnostics: Automated Residual Analysis Using Computer Vision and Statistical Insights

Residual analysis plays a pivotal role in validating regression models by identifying potential issues such as heteroscedasticity, non-linearity, and model misspecification. This study introduces a novel automated framework for residual diagnostics, integrating computer vision techniques with statistical inference. The proposed system evaluates residual plots, detects irregularities, and performs hypothesis testing to ensure model robustness. By combining image recog- nition algorithms with a user-friendly Shiny application, the approach eliminates subjective biases inherent in manual plot evaluation. The resulting tool enhances the scalability and reliability of regression diagnostics, offering data scientists a powerful resource forbuilding accurate andinterpretable models.