MLS-C01 Exploratory Data Analysis Practice Question
Which THREE of the following are common causes of multicollinearity in a linear regression model?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Including a polynomial term (e.g., x^2) along with the original variable
Options A, C, and D are correct. Dummy variable trap occurs when all categories are included without dropping one. Highly correlated predictors directly cause multicollinearity. Including polynomial terms creates correlation with the original variable. B (interaction terms) can also cause but is less common. E (outliers) does not cause multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Including a polynomial term (e.g., x^2) along with the original variable
Why this is correct
Polynomial terms are correlated with the original variable.
- ✗
Including interaction terms between independent variables
Why it's wrong here
Interaction terms can introduce correlation but are not a primary cause.
- ✓
Including all dummy variables for a categorical feature
Why this is correct
Perfect multicollinearity occurs when one dummy is a linear combination of others.
- ✓
Having two or more predictors that are highly correlated
Why this is correct
Direct correlation between predictors causes multicollinearity.
- ✗
Presence of outliers in the target variable
Why it's wrong here
Outliers affect model fit but not multicollinearity.
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