MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a time-series dataset of website traffic. The dataset contains hourly page views for the past two years. The scientist wants to analyze seasonality and trends. Which THREE techniques are appropriate for this analysis? (Choose THREE.)
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
✓
Moving average smoothing
Decomposition separates time series into trend, seasonal, and residual components. Autocorrelation plot (ACF) helps identify seasonality. Moving average smooths to reveal trends. Linear regression is not typical for seasonal decomposition. Box plot by month can show seasonal patterns but is less common for trend.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Moving average smoothing
Why this is correct
Smoothing reveals underlying trend.
- ✗
Box plot by month
Why it's wrong here
Box plots show distribution but not trend over time.
- ✓
Time series decomposition (additive or multiplicative)
Why this is correct
Decomposition isolates trend and seasonality.
- ✗
Linear regression on time index
Why it's wrong here
Linear regression models trend but not seasonality directly.
- ✓
Autocorrelation (ACF) plot
Why this is correct
ACF shows periodic correlations.
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