Multiple Regression
A statistical technique used to model the relationship between one continuous dependent variable and two or more independent variables.
What is Multiple Regression?
Multiple regression estimates the partial effect of each independent variable on the dependent variable, holding all other variables in the model constant. It helps identify the unique contribution of each predictor while accounting for their intercorrelations.
Why Multiple Regression Matters
Because outcomes in the real world are rarely caused by a single factor, multiple regression allows researchers to build more realistic, complex models, control for confounding variables, and determine which predictors are most important.
Example
An economist uses multiple regression to predict housing prices using square footage, number of bedrooms, age of the house, and distance to the nearest city center as predictors.
Common Mistakes
- Including too many predictors relative to the sample size, leading to overfitting.
- Ignoring multicollinearity, where highly correlated predictors make the individual coefficients unstable and difficult to interpret.