regression-analysis

Fit linear and non-linear regression models and generate diagnostics.

Updated Jan 5, 2026

Search Tags:#workflow #diagnostics #statistics #regression #data-analysis #interpretation #model-fitting

Ready to run

npx skills add https://github.com/d3ming/dot-agents --skill regression-analysis-d3ming

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: regression-analysis
Source: https://github.com/d3ming/dot-agents/tree/main/codex/.codex/skills/public/regression-analysis
Command: npx skills add https://github.com/d3ming/dot-agents --skill regression-analysis-d3ming

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Regression analysis workflows enable data scientists to quantify relationships, estimate effects, and forecast outcomes from data by fitting models and interpreting results.

Core Features & Use Cases

Model fitting: Fit linear and non-linear regression models with appropriate parameter estimates.
Diagnostics: Check assumptions (linearity, homoscedasticity, independence) and evaluate model quality with metrics and plots.
Interpretation & reporting: Generate summaries, effect sizes, and actionable insights for decision-making.
Use Case: Example: analyze a dataset to predict a target variable from predictors and report coefficients and significance.

Quick Start

Provide your dataset and specify the target variable, and the skill will fit a regression model and generate diagnostics.

Frequently Asked Questions about regression-analysis

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

FAQPage Schema

How do I run a regression analysis on a dataset to fit models and interpret coefficients?▼

To run regression analysis, provide your dataset and specify the target variable. The workflow handles data preprocessing, fits linear or non-linear models, and generates coefficient interpretations and summaries.

What is the best way to check regression assumptions like linearity and homoscedasticity?▼

The best way to check regression assumptions is through diagnostic checks within the workflow. It evaluates linearity, homoscedasticity, and independence using model metrics and plots to ensure valid statistical inference.

Can I use this regression workflow for large datasets in finance or biology?▼

Yes, this regression workflow applies to both small and large datasets across finance, biology, and social science. It handles model selection, diagnostics, and reporting regardless of your data scale.

How do I choose between linear and non-linear model fitting for my data analysis?▼

Model selection between linear and non-linear regression depends on your data relationships. The workflow includes configurable parameters to fit the appropriate model based on your specific predictors and target variable.

Does this regression workflow require dependencies or special components to run?▼

No, this regression workflow requires no external dependencies or special components to run. It operates independently to perform data preprocessing, model fitting, diagnostic checks, and result reporting.

What is included in regression reporting and result interpretation?▼

Regression reporting includes summaries, effect sizes, and actionable insights for decision-making. It interprets coefficients, reports significance levels, and generates clear outputs to quantify relationships in your data.