DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset with missing values in a time series of daily temperatures. The missing values occur sporadically. Which imputation method is most appropriate to maintain the temporal trend?
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
✓
Interpolation
Interpolation estimates missing values by using surrounding data points and is suitable for time series with a trend. Forward-fill carries the last observation forward, which may not capture trend well. Mean imputation ignores order.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Forward-fill
Why it's wrong here
May be useful but less accurate if trend exists; interpolation is better.
- ✗
Mean imputation
Why it's wrong here
Does not consider temporal order.
- ✗
Median imputation
Why it's wrong here
Does not consider temporal order.
- ✓
Interpolation
Why this is correct
Correct: uses neighboring values to estimate missing points, preserving trend.
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