Collinearity

Collinearity is a condition where some of the independent variables are highly correlated.

Differences

• Collinearity typically refers to the correlation between two variables.
• It is a simpler concept and is easier to detect because it involves only two variables.

Examples

• An example of collinearity could be a dataset of cars where the weight of the car and its engine size are two independent variables. These two variables could be highly correlated because heavier cars tend to have larger engines.

Multicollinearity

Multicollinearity, on the other hand, is a more general case where more than two predictor variables are inter-correlated.

Differences

• Multicollinearity refers to a situation where more than two independent variables are highly correlated.
• It is a more complex concept because it involves multiple variables and can be harder to detect.

Examples

• An example of multicollinearity could be a real estate dataset where the independent variables are the number of bedrooms, the size of the house in square feet, and the number of bathrooms. These variables could all be highly correlated with each other because larger houses tend to have more bedrooms and bathrooms.