MLS-C01 Exploratory Data Analysis Practice Question
Which TWO of the following are common techniques for detecting outliers in a numerical feature?
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
✓
Interquartile Range (IQR)
Z-score and IQR are standard outlier detection methods. PCA can detect outliers but is not a common direct method. Chi-square is for categorical association. Standard deviation alone is not a method.
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
Why it's wrong here
Chi-square test is for categorical variables.
- ✗
Standard deviation
Why it's wrong here
Standard deviation alone doesn't define a threshold.
- ✓
Interquartile Range (IQR)
Why this is correct
Outliers are defined as points beyond 1.5*IQR from Q1 or Q3.
- ✓
Z-score
Why this is correct
Points with z-score > 3 or < -3 are often considered outliers.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction, not primarily for outlier detection.
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