In multiple regression, when two or more independent variables are highly correlated with one another, the situation is known as ________.
A) heteroscedasticity
B) homoscedasticity
C) multicollinearity
D) autocorrelation

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Multicollinearity is the situation when two or more independent variables are highly correlated with one another, which can cause problems in the regression model, such as unstable coefficients, difficulty in determining the individual effects of the variables, and reduced predictive power. Heteroscedasticity refers to the situation where the variance of the errors is not constant across different levels of the predictor variables. Homoscedasticity, on the other hand, refers to the situation where the variance of the errors is constant across different levels of the predictor variables. Autocorrelation refers to the situation where the errors are correlated with one another over time or space.

In Multiple Regression, When Two or More Independent Variables Are