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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.
Passed 23 of 23 test cases with the skill, and 9 of 23 without it.
and 16 more in the full report
14 fixed9 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
Construct a command using single-letter CLI flag options to run a forced logistic model on `loan_defaults.csv` with target `defaulted` and output saved to `model_out.json`.
| Checked | Without skill | With skill |
|---|---|---|
| Uses -T logistic for the type flag. | Missed | Passed |
| Uses -o model_out.json for the output flag. | Passed | Passed |
The instructions your model reads when the skill loads.
Automated regression modeling tool — performs linear regression (OLS) or logistic regression (Logit) on tabular data, producing comprehensive statistical results with plain-language interpretation.
| 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) |
# 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
python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options]
# Force linear regression
python3 scripts/regression_analyzer.py data.csv -t y --type linear
test cases passed, vs 9 / 23 without
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