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ols-regression

Guide researchers through OLS regression specification, diagnostics, and interpretation in Python, R, and Stata.

Updated Apr 15, 2026
Search Tags:#python#diagnostics#regression#r#stata#statistical-analysis#ols
One-click install
npx skills add https://github.com/sheehe/coase --skill ols-regression
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ols-regression
Source: https://github.com/sheehe/coase/tree/main/%E5%AE%9E%E8%AF%81%E7%A7%91%E7%A0%94%E6%8F%92%E4%BB%B6/econometrics/econometrics/skills/ols-regression
Command: npx skills add https://github.com/sheehe/coase --skill ols-regression

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides comprehensive guidance for OLS regression and linear models in empirical research, covering specification, diagnostics, and interpretation to help researchers make robust inferences.

Core Features & Use Cases

  • Model specification guidance: formulate Y ~ X correctly, choose functional forms, and decide on controls and fixed effects.
  • Diagnostics and interpretation: guidance on heteroskedasticity, autocorrelation, multicollinearity, and result interpretation across languages (Python, R, Stata).
  • Code templates and references: ready-to-use code templates and an extensive reference to deepen understanding.

Quick Start

Describe your research question and data, and I will guide you through specifying the model and running an example regression.

Frequently Asked Questions about ols-regression

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

FAQPage Schema
How do I run OLS regression diagnostics in Python or R?▼

OLS regression requires model specification, diagnostic testing, and results interpretation. The skill provides code templates and guidance to check heteroskedasticity, autocorrelation, and multicollinearity across Python, R, and Stata workflows.

How do I specify an OLS regression model with fixed effects and controls?▼

Specifying an OLS regression model requires formulating Y ~ X correctly, choosing functional forms, and deciding on controls and fixed effects. The skill provides guidance to structure your model accurately for empirical research.

What is the best way to interpret OLS regression results for social science research?▼

Interpreting OLS regression results requires evaluating diagnostic outputs for heteroskedasticity, autocorrelation, and multicollinearity. The skill provides guidance to understand results and make robust inferences in economics and social science research.

Do I need a specific dataset format to perform statistical analysis with OLS?▼

Performing statistical analysis with OLS requires a dataset and a modeling goal. The skill uses these inputs to return code templates and diagnostic guidance across Python, R, and Stata workflows.

When should I not use ordinary least squares for my empirical research?▼

Ordinary least squares is inappropriate when diagnostics reveal severe heteroskedasticity, autocorrelation, or multicollinearity. The skill guides researchers through testing these assumptions to prevent flawed empirical inferences.

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