Multiplied by 100 it represents the percentage of variation in the outcome that can be explained by the model
What are the 4 basic assumptions when performing multiple regression?
No (perfect) multicollinearity: There should be no perfect linear relationship between two or more of the predictors
Variance inflation factor (VIF): can be used to assess and eliminate multicollinearity. VIF is a statistical value that identifies what independent variable(s) contribute to multicollinearity and should be removed. Any variable with VIF of greater than 10 should be removed.
Normally distributed errors: it is assumed that the residuals in the model are normally distributed values with a mean of 0, i.e. they are most frequently zero, close to zero and rarely much greater than zero
Homoscedasticity: at each level of the predictor variable(s), the variance of the residual terms should be constant
Linearity: The inclusion of each independent variable preserves the straight-line assumptions of multiple regression analysis