13.7 Assumptions of Multiple Linear Regression

Multiple linear regression analysis makes several key assumptions:

• There must be a linear relationship between the outcome variable and the independent variables. Scatterplots can show whether there is a linear or curvilinear relationship.

• Multivariate Normality–Multiple regression assumes that the residuals are normally distributed.

• No Multicollinearity. Multiple regression assumes that the independent variables are not highly correlated with each other.

• Homoscedasticity. This assumption states that the variance of error terms are similar across the values of the independent variables. A plot of standardized residuals versus predicted values can show whether points are equally distributed across all values of the independent variables.

Multiple linear regression requires at least two independent variables, which can be nominal, ordinal, or interval/ratio level variables.

References:

• Adapted from here