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A business analyst is working with retail data for consumer products. The customer is interested in the following: * Ability to look for outliers on sales volume and margin % * Ability to understand the clustering of products visually * Ability to see products that are above the median sales volume Which action should the business analyst take to implement these requirements?
Correct Answer: D
A scatter plot is the best choice for visualizing outliers, clustering, and products that are above the median sales volume. Scatter plots are excellent for showing relationships between two variables (like sales volume and margin %) while providing the ability to highlight outliers. By using K-Means clustering to color the products, the business analyst can visually group similar products. Additionally, adding a median sales volume reference line makes it easy to identify which products are above or below the median. Key Concepts: Scatter Plot: This visualization is ideal for detecting outliers and understanding the distribution and clustering of data points. K-Means Clustering: This technique groups similar data points (products) based on their values, which helps to identify patterns visually. Median Reference Line: Adding a reference line for median sales volume ensures that the analyst can easily see which products are performing above or below the median. Why the Other Options Are Less Suitable: A . Pivot table: While a pivot table could show aggregated data, it is not as effective for visualizing outliers or clustering as a scatter plot. B . Treemap: A treemap can show hierarchical relationships but is not as effective for identifying clusters or outliers. C . Combo chart: A combo chart is not as well-suited for visualizing clustering and outliers as a scatter plot. References for Qlik Sense Business Analyst: Outliers and Clustering Visualization: Qlik Sense recommends scatter plots for visualizing relationships between two measures and for detecting outliers and clustering in the data. Thus, creating a scatter plot with K-Means clustering and a median sales volume reference line is the best approach, making D the verified answer.
