multikolilaarisuus
Multikolilaarisuus, or multicollinearity, is a phenomenon in statistical modeling, particularly in regression analysis, where two or more predictor variables in a model are highly correlated with each other. This means that one predictor variable can be linearly predicted from the others with a substantial degree of accuracy. When multicollinearity is present, it can make it difficult to interpret the individual coefficients of the affected variables.
The presence of multicollinearity does not necessarily bias the predictions of the model as a whole. However,
Several methods can be used to detect multicollinearity, such as examining correlation matrices between predictor variables,
Addressing multicollinearity involves several strategies. These can include removing one or more of the correlated predictor