DA0-002 Visualization and Reporting Practice Question
A retail company with 500 stores across North America wants to visualize its sales performance. The dataset includes store ID, region (Northeast, Southeast, Midwest, West), product category (Electronics, Clothing, Home Goods), monthly sales (in dollars), and date (from January 2018 to December 2023). The data has missing values for about 5% of store-month combinations, and a few stores have reported sales that are 10 times higher than the average for their region due to grand opening events. The goal is to create a dashboard that shows monthly sales trends for each region and product category, and allows users to identify which categories are driving growth. Which approach should the analyst take?
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
✓
Create a line chart with month on the x-axis, sales on the y-axis, and separate lines for each region and category; check for outliers and consider annotating them
A line chart with month on the x-axis and separate lines for each region and product category allows clear visualization of sales trends over time for each combination. This approach also suggests checking for outliers (e.g., grand opening spikes) and annotating them rather than removing them, preserving valuable data. Option A is incorrect: a stacked bar chart with all stores aggregated per month obscures individual category and regional trends, making it hard to identify which categories are driving growth. Option C is incorrect: removing outlier stores discards legitimate data from grand opening events, which may be important for understanding growth. Option D is incorrect: a scatter plot is not suitable for showing time series trends as it does not connect points sequentially and makes trend identification difficult.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a stacked bar chart showing total sales by month, with each bar segmented by region and category
Why it's wrong here
Stacked bar charts make it difficult to see trends for individual categories and regions.
- ✓
Create a line chart with month on the x-axis, sales on the y-axis, and separate lines for each region and category; check for outliers and consider annotating them
Why this is correct
Line charts excel at showing trends over time; grouping by region and category allows comparison; outliers should be investigated and annotated, not removed.
- ✗
Create a scatter plot of sales vs. month with dots colored by region
Why it's wrong here
Scatter plots are not designed for time series trends; they're for relationships between two variables.
- ✗
Remove all stores with outlier sales and then create a line chart of the cleansed data
Why it's wrong here
Grand opening events are valid; removing them loses important business insights.
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