analyzing-regression-diagnostics

To check regression assumptions before reporting coefficients, run a diagnostic battery covering residual normality, homoscedasticity, independence, leverage, Cook's distance, and VIF to validate model trustworthiness for inference.

High Cook's distance and heteroscedasticity in a GLM model indicate influential outliers and non-constant variance, which violate regression assumptions and require remediation like robust standard errors or data transformation before inference.

Yes, you can run residual diagnostics on ridge, lasso, and GLS models. The diagnostic process applies to fitted OLS, GLS, ridge, lasso, and GLM models to evaluate residual behavior and determine if coefficient interpretation is safe.

The best way to fix multicollinearity detected by VIF analysis depends on the context, but standard remediation routes include refitting the model, applying variable transformations, or using regularization techniques to stabilize coefficient estimates.

You should run a Durbin-Watson test during regression diagnostics when your data is time ordered. It checks the independence assumption by detecting autocorrelation in residuals, ensuring your time-series model inference is valid.

Failed normality tests in residual plots indicate that model errors deviate from a normal distribution, requiring remediation such as transforming variables, refitting with a different GLM family, or applying robust standard errors.