regression-insight

To run OLS and logistic regression on a CSV dataset, load your numeric data and specify a target variable. The Skill reads CSV/Excel files, handles missing values automatically, and outputs full statistics including coefficients, p-values, and VIF to a structured JSON report.

An automated regression analysis report includes coefficients, R², adjusted R², p-values, AIC/BIC, and VIF. It also provides diagnostic information and a structured JSON plus plain-text summary with readable interpretations of variable impact directions.

Yes, you can use statsmodels for automatic linear and logistic regression type detection. The Skill leverages statsmodels and scipy to automatically select OLS or logistic regression based on target variable characteristics when no explicit type is specified.

Yes, pandas handles missing values during regression analysis by reading and cleaning numeric data before computation. The Skill relies on pandas and numpy to process the dataset, ensuring missing values do not interrupt the OLS or logistic regression output.

To interpret p-values and VIF from a regression analysis output, review the provided structured JSON and plain-text summary. The Skill generates a readable interpretation for each coefficient, explaining variable impact directions to help users understand model results.

The best way to export regression results for automated reporting is utilizing the structured JSON output format. The Skill generates both a JSON report containing full statistics and a plain-text summary, enabling seamless integration into automated data pipelines and reporting workflows.