Which data visualization type allows for graphical depiction of quartiles as well as minimum and maximum values across categories?

The box plot is a powerful data visualization tool that effectively summarizes data distribution across different categories. It specifically depicts five key statistics: the minimum value, the first quartile (25th percentile), the median (50th percentile), the third quartile (75th percentile), and the maximum value. This visualization is particularly useful for comparing distributions across multiple categories, as it allows viewers to quickly assess the spread and skewness of the data.

The box plot's structure offers insights into the central tendency and variability of the data, making it easier to identify outliers and understand how data points are distributed within the quartiles. This feature is advantageous in exploratory data analysis and when comparing groups, as it visually highlights differences and similarities across categories.

Other options, such as scatter plots, heat maps, and line charts, serve different purposes. Scatter plots are ideal for showing relationships between two quantitative variables but do not summarize data distribution. Heat maps can illustrate data density or intensity across two dimensions but lack the quartile representation. Line charts are well-suited for visualizing trends over time but are not designed to depict quartiles or statistical summaries effectively. Therefore, the box plot is the superior choice for visualizing quartiles along with minimum and maximum values across various categories.