analyzing-regression-diagnostics

Validate fitted regression diagnostics for linear and generalized linear models.

2|Updated May 23, 2026

Search Tags:#diagnostics #regression #vif #glms #residuals #cooks-distance #heteroscedasticity

Ready to run

npx skills add https://github.com/rocklambros/rcs --skill analyzing-regression-diagnostics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyzing-regression-diagnostics
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/analyzing-regression-diagnostics
Command: npx skills add https://github.com/rocklambros/rcs --skill analyzing-regression-diagnostics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill checks whether a fitted linear or generalized linear regression is safe to interpret before you report coefficients, p-values, or confidence intervals, reducing the risk of misleading conclusions from violated assumptions.

Core Features & Use Cases

Runs the standard diagnostic battery for linearity, residual normality, homoscedasticity, independence, leverage, Cook's distance, and multicollinearity.
Explains what each failing diagnostic means and routes you to the right remediation, such as robust standard errors, transformation, refitting, or handling a justified influential point.
Use it when you have an OLS, GLS, ridge, lasso, or GLM fit and need to determine whether the model is trustworthy for inference or coefficient interpretation.

Quick Start

Use this skill to review my fitted regression model, run the full diagnostic checklist, and tell me which assumption failures need remediation.

Frequently Asked Questions about analyzing-regression-diagnostics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I check regression assumptions before reporting coefficients and p-values?▼

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.

What does high Cook's distance and heteroscedasticity mean for my GLM model?▼

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.

Can I run residual diagnostics on ridge, lasso, and GLS models?▼

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.

What is the best way to fix multicollinearity detected by VIF analysis?▼

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.

When should I run a Durbin-Watson test during regression diagnostics?▼

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.

Why do my residual plots show failed normality tests and how do I remediate them?▼

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.