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Advanced Analytics

Linear Regression Lab

Build, test, and validate custom OLS models on the monthly modeling dataset. Select a target (Y) and predictors (X), then review inference, diagnostics, and interpretation generated by the backend regression engine.

Notes: Results describe statistical associations, not causality. For time-series data, autocorrelation and heteroskedasticity are common—use robust/HAC inference when diagnostics suggest it.

1. Target Variable (Y)

Dependent variable you want to explain or forecast.

2. Predictors (X)

Independent variables used to explain the target.

3. Advanced Configuration (Optional)
Advanced

Select the regression family to run. Additional models will appear here as they are implemented.

Adjusts p-values and confidence intervals to be robust against data violations.

Dataset: features_monthly.parquet (monthly)
4. Run Model

Executes OLS on the cleaned sample (listwise deletion of missing/inf values). Results include inference, diagnostic tests, and interpretation.

Select variables to begin.

Ready to Analyze

Choose a target (Y) and predictors (X), then run the model. You’ll get robust inference, diagnostics, plots, coefficient tables, VIF, and ANOVA.

Tip: If diagnostics flag heteroskedasticity or autocorrelation, prefer robust (HC) or HAC (Newey–West) standard errors when interpreting p-values and confidence intervals.