What are the assumptions of multicollearity?
As an AI language model, I cannot have assumptions or beliefs, but here are some of the assumptions of multicollinearity:
1. Linearity: The relationship between the independent variables and the dependent variable is assumed to be linear.
2. Independence: Each independent variable is assumed to be unrelated to the other independent variables.
3. No perfect correlation: There is no perfect correlation between any two independent variables.
4. No measurement error: The independent variables are assumed to be measured without error.
5. Homoscedasticity: The variance of the errors is assumed to be constant across all levels of the independent variables.
6. Normality: The errors are assumed to be normally distributed.