Predict a continuous variable from multiple predictors.
Definition
Multiple linear regression models the relationship between a continuous dependent variable and several independent variables. It quantifies the effect of each predictor while controlling for the others.
When to use it
Predict a continuous variable (e.g. blood pressure, score)
Identify factors associated with an outcome
Control for confounding variables
Requirements
Continuous dependent variable
Linear relationship between predictors and dependent variable
Approximately normal residuals
No strong multicollinearity between predictors
What StatsLab computes
Unstandardized β coefficients with 95% CI
Standardized β coefficients
R² and adjusted R²
ANOVA table of the model (global F)
p-value per predictor
Residuals plot
Forest plot of coefficients
Worked example
Context : Predicting performance score (0-100) from age, experience, and stress level.
Result : R² = 0.61, F(3, 96) = 50.2, p < 0.001
Interpretation : The model explains 61% of score variance. Experience (β = 0.45, p < 0.001) and stress (β = −0.38, p < 0.001) are the most important predictors. Age is not significant (p = 0.12).