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Answer: Bar chart, where each category is represented by a separate bar, the height of which corresponds to the frequency of issues, making comparisons straightforward.
**Correct Option: C. Bar chart** A bar chart is the most suitable for comparing frequencies across different categories due to its clear representation of each category with a separate bar, where the height corresponds to the category's frequency. This makes it straightforward to compare the frequencies of different categories at a glance. **Why other options are less suitable:** - **A. Scatter plot**: Best for showing relationships between two continuous variables, not for comparing categorical frequencies. - **B. Line plot**: Primarily used to display data trends over time or continuous intervals, not for static comparison across categories. - **D. Box plot**: Useful for summarizing the distribution of a numerical variable within categories, not for comparing the frequencies of issues across categories.
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You are working on a project that requires analyzing customer feedback data to identify the most common issues reported across different product categories. The dataset includes categorical data representing product categories and the frequency of each reported issue. Your goal is to visualize this data to facilitate easy comparison of issue frequencies across categories, considering the need for clarity and immediate visual impact. Which visualization technique should you use? (Choose one correct option)
A
Scatter plot, as it can show the relationship between two continuous variables, helping to identify correlations between product categories and issue frequencies.
B
Line plot, which is ideal for tracking changes in issue frequencies over time for each product category.
C
Bar chart, where each category is represented by a separate bar, the height of which corresponds to the frequency of issues, making comparisons straightforward.
D
Box plot, useful for summarizing the distribution of issue frequencies within each product category, highlighting outliers and median values.