Multicollinearity

Multicollinearity occurs when two or more independent variables in a statistical model are highly correlated, meaning they provide overlapping information about a dependent variable. This makes it difficult to determine the individual effect of each variable on the outcome. In practical terms, if you were trying to understand how different factors affect the price of a house—like size and number of bedrooms—high multicollinearity might confuse their specific contributions, leading to unreliable results. It’s crucial for accurate data analysis to identify and address multicollinearity in models.