regression-analysis

To run regression analysis, provide your dataset and specify the target variable. The workflow handles data preprocessing, fits linear or non-linear models, and generates coefficient interpretations and summaries.

The best way to check regression assumptions is through diagnostic checks within the workflow. It evaluates linearity, homoscedasticity, and independence using model metrics and plots to ensure valid statistical inference.

Yes, this regression workflow applies to both small and large datasets across finance, biology, and social science. It handles model selection, diagnostics, and reporting regardless of your data scale.

Model selection between linear and non-linear regression depends on your data relationships. The workflow includes configurable parameters to fit the appropriate model based on your specific predictors and target variable.

No, this regression workflow requires no external dependencies or special components to run. It operates independently to perform data preprocessing, model fitting, diagnostic checks, and result reporting.

Regression reporting includes summaries, effect sizes, and actionable insights for decision-making. It interprets coefficients, reports significance levels, and generates clear outputs to quantify relationships in your data.