CompTIA Data+ (DA0-002)Data AnalysisMedium
A data analyst is examining sales data for a new product launched three months ago. The daily sales figures show a clear upward trajectory, but with significant day-to-day fluctuations. The analyst wants to understand the underlying growth pattern, free from the daily noise. Which analytical technique would best help to identify this underlying growth?
- AOutlier Detection
- BSmoothing
- CClustering
- DFactor Analysis
Show answer & explanationAnswer & explanation
Correct answer: B. Smoothing
Smoothing techniques, such as moving averages or exponential smoothing, are used to remove short-term fluctuations from time series data, revealing underlying trends or cycles. This allows the analyst to see the overall growth pattern more clearly.
Why the other options are wrong
- A. Outlier detection identifies unusual data points, not underlying trends.
- C. Clustering groups similar data points, which isn't the primary goal here.
- D. Factor analysis identifies underlying latent factors in a dataset, not time-series trends.
Data Smoothing
Techniques used to remove noise or short-term fluctuations from data, especially time series, to reveal underlying trends or patterns more clearly.
- Common methods: moving averages, exponential smoothing.
- Reduces volatility and highlights long-term trends.
- Helps in forecasting and understanding overall patterns.
Memory trick: Smooth the bumps to see the path.