MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist generates a pairplot of the dataset and observes that two features have a Pearson correlation coefficient of 0.95. Which TWO conclusions can the scientist draw from this observation? (Choose 2)
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
✓
The two features may be multicollinear
Options A and B are correct because a Pearson correlation coefficient of 0.95 indicates a very strong positive linear relationship between the two features. This strong linear relationship suggests potential multicollinearity if both features are used as predictors in a linear model. Option C is incorrect because a positive correlation means the features move in the same direction, not opposite. Option D is wrong because a high correlation implies dependence, not statistical independence. Option E is incorrect because correlation does not imply causation; it only measures the strength and direction of a linear relationship.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The two features may be multicollinear
Why this is correct
High correlation between features can cause multicollinearity in regression models.
- ✓
The two features have a strong linear relationship
Why this is correct
A correlation of 0.95 indicates a strong positive linear relationship.
- ✗
The two features move in opposite directions
Why it's wrong here
A positive correlation means they move in the same direction.
- ✗
The two features are statistically independent
Why it's wrong here
High correlation indicates dependence, not independence.
- ✗
One feature causes the other
Why it's wrong here
Correlation does not imply causation.
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