MLS-C01 Exploratory Data Analysis Practice Question
Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?
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 method
Options C and D are correct. The Z-score method identifies outliers by measuring how many standard deviations a data point is from the mean; points with |Z| > 3 are often considered outliers. The Interquartile Range (IQR) method defines outliers as points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. Option A (Cook's distance) is used in regression to identify influential points, not general univariate outlier detection. Option B (Mahalanobis distance) is a multivariate distance measure. Option E (DBSCAN) is a clustering algorithm that can identify outliers in multivariate space, but not specifically for univariate numeric data.
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.
- ✗
Mahalanobis distance
Why it's wrong here
Mahalanobis distance is for multivariate outlier detection.
- ✓
Z-score method
Why this is correct
Z-score flags points beyond a threshold (e.g., |z|>3).
- ✓
Interquartile range (IQR) method
Why this is correct
IQR flags points below Q1-1.5*IQR or above Q3+1.5*IQR.
- ✗
DBSCAN clustering
Why it's wrong here
DBSCAN is for multivariate density-based clustering.
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