What method does this tool use?

Ordinary least squares (OLS) via the normal equations: β = (XᵀX)⁻¹Xᵀy. All computation runs in the browser — no data leaves your device.

How many data rows do I need?

You need at least k + 2 rows, where k is the number of predictors. With 3 predictors, supply at least 5 rows. More rows give more reliable estimates.

What does R² mean here?

R² (coefficient of determination) measures the proportion of variance in Y explained by the predictors. A value of 0.85 means the model explains 85% of the variation.

Why might the matrix inversion fail?

Perfect multicollinearity — when one predictor is a linear combination of another — makes XᵀX singular and uninvertible. Remove or combine the redundant predictor.

Does this handle categorical predictors?

No. Encode categoricals as 0/1 dummy variables before entering them. All inputs must be numeric.

Is the intercept always included?

Yes. The tool always fits a model with an intercept (β₀), which is standard practice in OLS regression.

Multiple Regression Helper

Name: Multiple Regression Helper
Availability: InStock
Author: Nham Vu

Enter your data, choose 1–3 predictors, and get regression coefficients, R², and a predicted value instantly.

Number of Predictors

Data Rows

Minimum rows needed: 3

Predict New Value

Enter data and click Run Regression

Model Fit

R²

Adj. R²

RMSE

Coefficients

Term	Estimate (β)	Std Error	t-value

Fitted vs Actual

Row	Actual Y	Fitted Ŷ	Residual

Regression Equation

Summary

Enter your data, choose 1–3 predictors, and get regression coefficients, R², and a predicted value instantly.

How it works

Choose the number of predictor variables (X1, X2, or X3).
Enter your data rows — each row needs values for every predictor and the outcome (Y).
Click "Run Regression" to compute OLS coefficients via the normal equations.
Read the intercept (β₀) and slope(s) (β₁…β₃) from the results panel.
Optionally enter new predictor values to get a predicted Y using the fitted model.

Use cases

Explore how multiple factors jointly affect an outcome variable.
Estimate regression coefficients for a statistics homework or exam.
Quickly check OLS results before running full analysis in R or Python.
Teach students the effect of adding or removing predictors on R².
Validate hand-calculated normal equations against a reference tool.
Build intuition for multicollinearity by watching how coefficients shift.

Frequently Asked Questions

Last updated: 2026-06-11 · Reviewed by Nham Vu