How does adding more predictors to a multiple linear regression model affect its inferences?
- It always improves the model
- It always makes the model worse
- It can lead to overfitting
- It has no effect on the model
Adding more predictors to a model may increase the R-squared value, making it appear that the model is improving. However, if these additional predictors are not truly associated with the response variable, it may result in overfitting, making the model perform poorly on new, unseen data.
Loading...
Related Quiz
- Bayes' theorem is a method for updating ________ probabilities based on new data.
- What are the degrees of freedom in a Chi-square test for a 2x3 contingency table?
- The variance of each Principal Component corresponds to the _______ of the covariance matrix.
- Quantitative data can be broken down into two types: ________ and ________.
- What kind of data is best suited for the Wilcoxon Signed Rank Test?