Regression Analysis: Beyond the Basics

Regression Analysis: Beyond the Basics is a practical Data Science course for learners who want to move past simple fitted lines and build models they can defend. You will learn how to model real-world relationships with rigor, diagnostics, and practical statistical judgment while improving both prediction and interpretation.

Build Stronger Regression Models For Data Science

• Move from basic regression formulas to a structured modeling mindset for real-world Data Science problems.
• Diagnose residuals, outliers, leverage, heteroskedasticity, and other issues that can weaken your conclusions.
• Design better model forms using transformations, polynomial terms, interactions, and categorical predictors.
• Compare, validate, regularize, and communicate regression results with appropriate caution and clarity.

This course teaches advanced regression analysis techniques for building reliable, interpretable, and practical statistical models.

In Regression Analysis: Beyond the Basics, you will develop the judgment needed to choose, refine, and explain regression models in applied Data Science settings. The course begins with multiple regression structure, interpretation, assumptions, and estimators, then shows what can go wrong when those assumptions are ignored.

You will practice model diagnostics and repair strategies, including residual analysis, influential observation detection, robust standard errors, and approaches for handling unstable or misleading results. You will also learn how transformations, curved relationships, interaction effects, contrasts, and reference groups can help your models better represent the relationships in your data.

The course then expands into model reliability and selection, covering multicollinearity, AIC, BIC, adjusted R-squared, cross-validation, predictive performance, and regularization with ridge, lasso, and elastic net. You will also go beyond ordinary least squares with logistic regression, count models, robust regression, and quantile regression.

By the end of this course, you will be able to model real-world relationships with rigor, diagnostics, and practical statistical judgment. You will leave with a stronger Data Science workflow for building regression models, reviewing their limitations, and communicating results without overclaiming.