Question 1,710 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?
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
✓
Correlation matrix with heatmap
A correlation matrix quantifies linear relationships between numerical features, and a heatmap visualizes these correlations, making it effective for detecting multicollinearity. Option A is wrong because the chi-square test of independence is used for categorical variables, not numerical features. Option B is wrong because box plots show distributions and outliers, not relationships between features. Option D is wrong because pair plots provide a visual scatter plot matrix but do not offer a quantitative measure of multicollinearity like a correlation matrix does.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Chi-square test of independence
Why it's wrong here
Chi-square test is for categorical variables.
- ✗
Box plots for each numerical feature
Why it's wrong here
Box plots show distribution, not relationships between features.
- ✓
Correlation matrix with heatmap
Why this is correct
Correlation matrix shows pairwise linear correlations, indicating multicollinearity.
- ✗
Pair plot
Why it's wrong here
Pair plots are visual and not a quantitative measure of multicollinearity.
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Last reviewed: Jun 20, 2026
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