Before going to below detailed explanation of Box Plot chart implementation, i would like to say that – i haven’t personally used in real time scenario the Box Plot chart in my experience.. Will be happy to try below steps if given the chance to use in real business use case… Happy Reading!

**Overview**

A Boxplot (sometimes called a box and whisker plot) is a graph type designed to compare similar distributions at a glance. The goal of Boxplot graphs is to make the center, spread, and overall range of values immediately apparent. The Boxplot is useful for isolating the extreme values and identifying the range of middle values in your data. The box shows the median values of a variable, while the whiskers stretch to the greatest and lowest values of that variable. Boxplots can also help you identify symmetrical or skewed distributions among values. This technical note describes the details steps to implement the Boxplot in MicroStrategy.

**Requirements**

Five metrics holding the following values must appear on the report template in the following order:

- Minimum value: The smallest observed value. In MicroStrategy graphs, this is depicted as a whisker on the bottom of a column.
- Lower quartile or 25th percentile: The median of the lower half of the data.
- Median: The middle of the data when it is arranged in order from least to greatest.
- Upper quartile or 75th percentile: The median of the upper half of the data.
- Maximum value: The largest observed value. In MicroStrategy

**Object Design**

**Each metric could be defined as follows, and the metric level needs to be defined if necessary.**

- Minimum value: Min(fact)
- Lower quartile or 25th percentile: Percentile(fact, 0.25)
- Median: Median(fact)
- Upper quartile or 75th percentile: Percentile(fact, 0.75)
- Maximum value: Max(fact)

**Demonstration**

**The screenshots below display the definition for the first quartile, a Boxplot against the Category attribute, and the preference.**

**: MSTR KB Link**

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