Multiple Regression

Multiple Regression on STEM Calculators is a statistics chapter for modeling how a response variable depends on two or more predictors. It includes tools to fit a multiple linear regression equation, estimate regression coefficients, compute predicted values, and evaluate model performance with key measures such as R² and adjusted R², along with interpretation-focused outputs that explain how each predictor contributes while holding others constant.

This chapter is designed for intermediate to advanced learners who already understand simple linear regression and want to analyze more realistic, multi-factor data. Students can practice the full workflow used in statistics and data analysis courses, teachers can generate verified examples, self-learners can build intuition about multivariable effects and common pitfalls like multicollinearity, and advanced users can quickly validate computations for labs, research projects, and applied modeling tasks.

Enter a dataset with multiple predictor columns to get the fitted regression model, coefficient summaries, prediction results, and clear explanation of what the model implies in context. With step-by-step calculations and organized tables that support interpretation, this page helps you build accurate multivariable regression models, compare predictors, and communicate results confidently.