# Regression Modeler

Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating...

- Repository: https://github.com/zebbern/claude-code-guide (4691 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add zebbern/claude-code-guide --skill regression-modeler -g -a claude-code -y`
Codex: `npx skills add zebbern/claude-code-guide --skill regression-modeler -g -a codex -y`
Cursor: `npx skills add zebbern/claude-code-guide --skill regression-modeler -g -a cursor -y`
Gemini CLI: `npx skills add zebbern/claude-code-guide --skill regression-modeler -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
# 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

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

### Specifying Regression Type

```bash
# 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

```bash
# 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

The rest: https://github.com/zebbern/claude-code-guide/blob/HEAD/skills/regression-modeler/SKILL.md