Run Multiple Linear Regression Online
Paste your dataset below. First column is the dependent variable (Y), and the remaining columns are independent variables (X1, X2, ...). You can include a header row.
What this multiple regression calculator does
This tool estimates a standard multiple linear regression model using ordinary least squares (OLS). It fits an equation of the form:
Y = b0 + b1X1 + b2X2 + ... + bkXk
Where Y is your outcome variable, each X is a predictor, and each coefficient b shows the expected change in Y for a one-unit increase in that predictor (holding all other predictors constant).
How to format your data
Accepted input formats
- Comma-separated values (CSV)
- Tab-separated values
- Space-separated or semicolon-separated values
Required structure
- Each row = one observation
- First column = dependent variable (Y)
- Remaining columns = predictors (X variables)
- Optional header row is allowed
Example:
Sales,AdSpend,Price
120,15,9
134,18,8
128,17,9
How to read the results
Model summary
- R²: proportion of variance in Y explained by your predictors.
- Adjusted R²: R² corrected for number of predictors; better for comparing models of different sizes.
- SSE: sum of squared residual errors (lower is better).
- MSE: mean squared error; average squared prediction error.
- F-statistic: overall test of whether predictors improve fit over an intercept-only model.
Coefficient table
- Coefficient: estimated impact of each predictor on Y.
- Std. Error: uncertainty of the coefficient estimate.
- t-stat: coefficient divided by its standard error.
Best practices for reliable regression analysis
- Use enough observations relative to number of predictors.
- Check for outliers that can overly influence coefficients.
- Avoid severe multicollinearity (predictors that are nearly duplicates).
- Inspect residual behavior if you need strict inference assumptions.
- Remember: association does not automatically imply causation.
Common use cases
Multiple regression is used in finance, economics, marketing, healthcare, and social science. Typical examples include forecasting sales from ad channels, estimating housing prices from property features, modeling exam scores from study habits, and studying how multiple factors jointly predict performance.
FAQ
Can I run simple linear regression here?
Yes. If you provide only one predictor column after Y, the calculator behaves as a simple linear regression tool.
Does this calculator include an intercept?
Yes. The intercept term is included by default.
Can I predict new values after fitting?
Yes. Enter predictor values in the “Predict new Y” field using the same predictor order as your dataset.