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

Question

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Multicollinearity occurs when independent variables in a multiple regression model are highly correlated with each other, and can lead to unstable or unreliable estimates of the regression coefficients. Heteroscedasticity refers to unequal variance of errors across different levels of the independent variables, homoscedasticity refers to equal variance of errors across different levels of the independent variables, and autocorrelation refers to correlation of error terms across observations in a time series or panel data context.

In Multiple Regression, When Two or More Independent Variables Are