MLS-C01 Exploratory Data Analysis Practice Question
Network Topology
Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?
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
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Correlation matrix (Pearson)
The correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because chi-square is for categorical variables, not numeric. Option C is wrong because VIF requires more variables than observations (or typically used after regression). Option D is wrong because covariance alone is scale-dependent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Correlation matrix (Pearson)
Why this is correct
A correlation matrix can reveal high pairwise correlations.
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Chi-square test of independence
Why it's wrong here
Chi-square is for categorical variables.
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Variance Inflation Factor (VIF)
Why it's wrong here
VIF is not reliable with such a small sample (5 rows).
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Covariance matrix
Why it's wrong here
Covariance is scale-dependent and harder to interpret.
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