MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is working with a time series dataset that shows increasing variance over time. To stabilize the variance before modeling, which transformation is most appropriate?
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
✓
Log transformation
The log transformation (option C) is appropriate when variance increases with the mean, which is common in time series data. It compresses the scale and stabilizes variance. First-order differencing (A) is used to remove trend or seasonality, not to stabilize variance. The Box-Cox transformation (B) can also stabilize variance, but it is a more general family that includes log as a special case; however, log is simpler and often preferred when the data are positive. Min-max scaling (D) rescales to a fixed range but does not address changing variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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First-order differencing
Why it's wrong here
First-order differencing removes trend or seasonality, but does not stabilize variance; it is used for making a time series stationary in mean, not variance.
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Box-Cox transformation
Why it's wrong here
Box-Cox transformation applies a single power transformation to the entire dataset, aiming to achieve global homoscedasticity and normality. While it stabilises variance generally, it does not specifically address variance that *increases over time* within a time series, which often indicates a multiplicative or proportional relationship requiring a logarithmic transformation. Box-Cox is tempting as it is a powerful general-purpose variance stabiliser, making it suitable for data where the variance is a consistent function of the mean across the entire dataset, rather than evolving temporally.
- ✓
Log transformation
Why this is correct
Log transformation is specifically used when variance increases with the mean; it compresses the scale and stabilizes variance, making it the most appropriate choice.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling rescales data to a range [0,1] but does not stabilize variance; it is a normalization technique, not a variance-stabilizing transformation.
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