Sec.B-Ch.5-Subsec.6：Plot Diagnostics for Linear Regression Models

In-Depth Interpretation of Plot Diagnostics for Linear Regression Models

Plot diagnostics for linear regression models are important steps in statistical analysis and data science, which can help researchers and analysts judge the applicability of models, diagnose potential problems with models, and ensure that the model’s interpretation of data is reasonable. The assumptions of linear regression models include linear relationship, normality of residuals, homoscedasticity, and independence. After the model is established, we test whether these assumptions are satisfied by drawing various plots. If these assumptions are not satisfied, it may affect the validity of the model, leading to biased or inaccurate results. This article will introduce in detail the common plotting methods for linear regression models, combined with specific operation steps in R language, and analyze how to diagnose models through these plotting methods. It focuses on the core content of Section 6, Chapter 12, including residual plots, Q-Q plots, scale-location plots, leverage value plots, etc.