When making assumptions about regression analysis, which of the following is not considered to be true?
A) Homoscedasticity
B) Independent errors
C) Perfect multicollinearity
D) Categorical or quantitative variable type

Question

Quizplus · Accepted Answer

Perfect multicollinearity is not considered to be true because it can cause issues with the interpretation of coefficients and the stability of the regression model. Ideally, variables in a regression model should have some degree of correlation with the dependent variable but not with each other to avoid multicollinearity. Homoscedasticity (equal variance of errors), independent errors, and having both categorical and quantitative variable types are assumptions that are generally considered to be necessary for regression analysis.

When Making Assumptions About Regression Analysis, Which of the Following