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Multiple Linear Regression Calculator
Model the relationship between one continuous dependent variable and multiple independent predictors. Paste your data below to instantly calculate your coefficients, $R^2$, Adjusted $R^2$, and the multiple regression equation.
How to use this calculator
- Enter Y Data (Dependent Variable): Paste your outcome values into the top box.
- Enter X Data (Independent Variables): Paste your predictor values into the $X_1$ and $X_2$ boxes. A third predictor ($X_3$) is optional.
- Verify your counts: Ensure all boxes have the exact same number of data points. The calculator pairs the first number in every box together to form a single “row” of data.
Multiple Linear Regression
Analyse up to 3 independent variables simultaneously.
Multiple Regression Equation
Y = 0
R-Squared (R²)
0
Adjusted R²
0
Observation Count (n)
0
| Variable | Coefficient |
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Understanding Your Results
Multiple linear regression expands on simple regression by allowing you to isolate the effect of one variable while controlling for others.
- Regression Equation: The formula used to predict your outcome ($Y$) based on your inputs.
- Coefficients ($b_1, b_2 \dots$): These tell you how much $Y$ is expected to change for a one-unit increase in that specific $X$ variable, assuming all other variables are held constant.
- $R^2$: The percentage of the variance in $Y$ that is explained by your entire model of $X$ variables working together.
- Adjusted $R^2$: Standard $R^2$ artificially goes up every time you add a new variable, even if the variable is useless. Adjusted $R^2$ corrects for this by penalizing the score for adding unnecessary variables. Always use this metric when evaluating a multiple regression model.