Collinearity is a statistical phenomenon that occurs when two or more independent variables in a regression model are highly correlated. This can cause problems with the interpretation of the model, as it can be difficult to determine the individual effects of each variable. There are several ways to avoid collinearity, including:
One way to avoid collinearity is to carefully select the independent variables that are included in the model. By choosing variables that are not highly correlated, you can reduce the likelihood of collinearity. Another way to avoid collinearity is to use a regularization technique. Regularization techniques add a penalty term to the model that is proportional to the sum of the squared coefficients. This penalty term discourages the model from fitting the data too closely, which can help to reduce collinearity.
