Centering variables in Multiple Linear Regression helps to reduce the ___________ and ease the interpretation of interaction effects.
- complexity
- mean
- multicollinearity
- variance
Centering variables (subtracting the mean) helps to reduce multicollinearity, especially when interaction effects are included. This eases the interpretation of the coefficients and reduces potential issues related to multicollinearity with interaction terms.
Loading...
Related Quiz
- You are given a dataset of customer reviews but without any labels indicating sentiment. You want to group similar reviews together. Which type of learning approach will you employ?
- You are using KNN for a regression problem. What are the special considerations in selecting K and the distance metric, and how would you evaluate the model's performance?
- Imagine you're developing a model to recognize rare bird species from images. You don't have many labeled examples of these rare birds, but you have a model trained on thousands of common bird species. How might you leverage this existing model for your task?
- Explain the assumption of homoscedasticity in Simple Linear Regression.
- The ability of an individual or a group to understand and trust the model's decisions is often tied to the model's ________.