What is the implication of multicollinearity in polynomial regression?
- It increases the fit of the model to the training data
- It increases the interpretability of the model
- It reduces the complexity of the model
- It reduces the precision of coefficient estimates
Multicollinearity in polynomial regression can reduce the precision of the coefficient estimates and cause them to be highly sensitive to minor changes in the model. This can lead to unstable and unreliable estimates, making it difficult to interpret the model and infer about the relationships between variables.
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