SkillsAdd

zebbern/claude-code-guide

regression-modeler

Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation.

First seen May 31, 2026

Installation

$ npx skills add zebbern/claude-code-guide --skill regression-modeler
View on skills.sh ↗ GitHub repository ↗

Summary

  • Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation.
  • Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 4.6K
License LICENSE
Default branch main
Open issues 1
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

LicenseMIT
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,652 B
  • docs SUMMARY.md 351 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 20 installs

SKILL.md

regression-modeler

Automated regression modeling tool — performs linear regression (OLS) or logistic regression (Logit) on tabular data, producing comprehensive statistical results with plain-language interpretation.

Capabilities

Feature Description
Linear Regression OLS with coefficients, R², adjusted R², F-test, AIC/BIC, Durbin-Watson
Logistic Regression Logit with coefficients, Odds Ratio, Pseudo R², likelihood ratio test
Multicollinearity Detection VIF values for each predictor with warning levels
Plain-Language Interpretation Clear explanations of what each metric and coefficient means
Auto Detection Automatically switches to logistic regression when the target is binary (0/1)

Quick Start

# Linear regression: predict price using all numeric columns as predictors
python3 scripts/regression_analyzer.py data.csv --target price

# Logistic regression: predict churn (0/1) with specified features
python3 scripts/regression_analyzer.py users.csv --target churn --features "age,income,tenure"

# Save results to JSON
python3 scripts/regression_analyzer.py data.csv --target sales --output result.json

Detailed Usage

Basic Invocation

python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options]

Specifying Regression Type

# Force linear regression
python3 scripts/regression_analyzer.py data.csv -t y --type linear

# Force logistic regression
python3 scripts/regression_analyzer.py data.csv -t label --type logistic

# Auto-detect (default)
python3 scripts/regression_analyzer.py data.csv -t y --type auto

Selecting Feature Columns

# Manually specify (comma-separated)
python3 scripts/regression_analyzer.py data.csv -t price -f "sqft,bedrooms,bathrooms"

# Omit to automatically use all numeric columns
python3 scripts/regression_analyzer.py data.csv -t price

Parameters

Parameter Short Required Default Description
input — Yes — Input file path (CSV/TSV/Excel/JSON)
--target -t Yes — Target variable (dependent variable) column name
--features -f No All numeric columns Predictor column names, comma-separated
--type -T No auto Regression type: linear / logistic / auto
--output -o No stdout Output JSON file path
--no-const — No false Do not add an intercept term
--keep-na — No false Keep rows with missing values (for debugging)

Output Structure (JSON)

{
  "type": "linear",
  "r_squared": 0.8523,
  "r_squared_adj": 0.8471,
  "f_statistic": 162.34,
  "f_p_value": 0.0,
  "coefficients": {
    "sqft": {"coefficient": 135.42, "p_value": 0.0001, ...},
    "bedrooms": {"coefficient": 8021.5, "p_value": 0.032, ...}
  },
  "vif": {"sqft": 2.31, "bedrooms": 1.87},
  "interpretation": {
    "model_summary": ["R² = 0.8523 (good model fit...)"],
    "variable_analysis": ["sqft: coefficient = 135.42... positive effect..."]
  }
}

Dependencies

  • Python 3.8+
  • pandas
  • numpy
  • statsmodels
  • scipy
pip install pandas numpy statsmodels scipy