Question 948 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?
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
✓
Multicollinearity will make coefficient estimates unstable
A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity inflates the variance of coefficient estimates, making them unstable and difficult to interpret. Option A is wrong because redundant information leads to multicollinearity, not underfitting; underfitting occurs when the model is too simple. Option B is wrong because heteroscedasticity refers to non-constant variance of errors, not correlation between features. Option C is wrong because overfitting is more associated with model complexity and variance, not directly with redundant features; in fact, redundant features can cause numerical instability but not necessarily overfitting.
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 model will underfit because of redundant information
Why it's wrong here
Underfitting is due to insufficient model capacity.
- ✗
Heteroscedasticity will be introduced
Why it's wrong here
Heteroscedasticity is about non-constant variance of errors, not correlation.
- ✗
The model will overfit due to redundant features
Why it's wrong here
Overfitting is more about model complexity; correlation can inflate variance but not necessarily overfit.
- ✓
Multicollinearity will make coefficient estimates unstable
Why this is correct
High correlation between predictors leads to multicollinearity, increasing standard errors.
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Last reviewed: Jun 20, 2026
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