Multiple regression

How it is used

One of the most used methods in social, health and business research. Each coefficient is interpreted 'holding the other variables constant' or 'controlling for' them. Problems to watch for include multicollinearity and too many predictors for the sample size (overfitting).

Example sentences

1. A multiple regression with age, income and education explained 41% of the variance in life satisfaction.

2. In the multiple regression, the effect of advertising shrank once price was included.

Common collocations

• multiple linear regression
• hierarchical multiple regression
• run a multiple regression
• predictors in the model

Related terms

• linear regressionA regression method that models the outcome as a straight-line function of one or more predictors, estimating an intercept and one slope per predictor.
• regression coefficientA number in a regression model showing how much the outcome is expected to change when a predictor increases by one unit, with the other predictors held constant.
• multicollinearityA situation in regression where two or more predictors are strongly correlated with each other, which makes their separate effects hard to estimate.
• control forTo remove or account for the influence of a variable in an analysis, usually by including it in a model, so that the effect of interest is not mixed up with it.
• R-squaredThe share of the variation in the outcome variable that a regression model explains, ranging from 0 to 1; also called the coefficient of determination.

More terms: Regression and Correlation

• correlation
• regression
• correlation coefficient
• residual
• least squares
• causation
• logistic regression
• goodness of fit