When the independent variables are correlated with one another in a multiple regression analysis, this condition is called:

A) heteroscedasticity
B) homoscedasticity
C) multicollinearity
D) causality
E) collinearity

Multicollinearity occurs when the independent variables in a multiple regression model are highly correlated with each other, which can lead to issues with interpreting the coefficients and making accurate predictions. Heteroscedasticity refers to unequal variance in the residuals, homoscedasticity refers to equal variance in the residuals, causality refers to a direct relationship between two variables, and collinearity is a more general term that can refer to any type of correlation between variables.