MLS-C01 Exploratory Data Analysis Practice Question
Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)
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
✓
Z-score
Options C, D, and E are correct. Z-score (C) identifies outliers by standard deviations from mean, IQR method (D) uses quartiles to detect outliers beyond 1.5×IQR, and Modified Z-score using MAD (E) is a robust alternative. Cook's distance (A) is a regression diagnostic for influential points, not univariate outlier detection. DBSCAN (B) is a multivariate clustering algorithm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cook's distance
Why it's wrong here
Cook's distance is for identifying influential points in regression.
- ✗
DBSCAN clustering
Why it's wrong here
DBSCAN is used for clustering, not univariate outlier detection.
- ✓
Z-score
Why this is correct
Z-score measures how many standard deviations an observation is from the mean.
- ✓
Interquartile range (IQR) method
Why this is correct
Points outside 1.5*IQR from quartiles are outliers.
- ✓
Modified Z-score using median absolute deviation (MAD)
Why this is correct
MAD is robust to outliers and used for univariate detection.
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