MLS-C01 Exploratory Data Analysis Practice Question
Which TWO techniques can be used to detect multicollinearity among numerical features during exploratory data analysis? (Choose two.)
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
✓
Compute a correlation matrix and look for pairs with absolute correlation > 0.8.
Multicollinearity indicates high correlation between predictors. Option B: Compute a correlation matrix and look for pairs with absolute correlation > 0.8 directly reveals linear dependencies. Option D: Variance Inflation Factor (VIF) measures how much the variance of a coefficient is inflated due to collinearity; VIF > 5 or 10 suggests multicollinearity. Option A (PCA) reduces dimensionality but does not directly detect collinearity. Option C (t-test) tests mean differences, not associations. Option E (chi-square) tests categorical independence, not applicable to numerical features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply Principal Component Analysis (PCA) and examine loadings.
Why it's wrong here
PCA is a remedy, not a detection method.
- ✓
Compute a correlation matrix and look for pairs with absolute correlation > 0.8.
Why this is correct
High correlation indicates multicollinearity.
- ✗
Perform a t-test between each pair of features.
Why it's wrong here
t-tests compare means, not association.
- ✓
Calculate Variance Inflation Factor (VIF) for each feature.
Why this is correct
VIF > 10 indicates high multicollinearity.
- ✗
Use a chi-square test of independence.
Why it's wrong here
Chi-square is for categorical variables.
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