Distribution analysis (box plots) in Tableau - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ dax_lod_result

intermediate

Calculate the Interquartile Range (IQR) in Tableau

You have a dataset with sales values. You want to calculate the Interquartile Range (IQR) using Tableau calculations to create a box plot. Which calculation correctly computes the IQR?

A WINDOW_MEDIAN(SUM([Sales])) - WINDOW_AVG(SUM([Sales]))

B PERCENTILE([Sales], 0.75) - PERCENTILE([Sales], 0.25)

C MEDIAN([Sales]) - AVG([Sales])

D WINDOW_PERCENTILE(SUM([Sales]), 0.75) - WINDOW_PERCENTILE(SUM([Sales]), 0.25)

💡 Hint

Remember that IQR is the difference between the 75th and 25th percentiles over a window.

WINDOW_PERCENTILE calculates percentiles over the current partition. Subtracting the 25th percentile from the 75th percentile gives the IQR.

❓ visualization

intermediate

Identify the correct box plot visualization setup

You want to create a box plot in Tableau to analyze the distribution of sales by region. Which of the following steps is correct to build the box plot?

A Drag Region to Columns, Sales to Rows, then add Sales to Detail and use Analytics pane to add Box Plot.

B Drag Sales to Columns, Region to Rows, then create a calculated field for median sales and plot it as a bar chart.

C Drag Region to Filters, Sales to Tooltip, then create a histogram of sales.

D Drag Region to Rows, Sales to Columns, then use Show Me to select a scatter plot.

💡 Hint

Box plots require grouping by category and a measure on the axis, then adding the box plot from Analytics.

Dragging Region to Columns and Sales to Rows sets up the categories and measure. Adding Sales to Detail ensures data points are considered. The Analytics pane lets you add a box plot visualization.

🧠 Conceptual

advanced

Understanding outliers in box plots

In a box plot, how are outliers typically determined and displayed?

A Outliers are points outside 1.5 times the IQR above the third quartile or below the first quartile, shown as individual dots.

B Outliers are points beyond the maximum and minimum values, shown as bars.

C Outliers are points exactly at the median, shown as a line.

D Outliers are points within the whiskers, shown as shaded areas.

💡 Hint

Recall the standard rule for detecting outliers in box plots.

Outliers are values that lie beyond 1.5 times the IQR from the quartiles. They are shown as individual dots outside the whiskers.

🔧 Formula Fix

advanced

Fix the Tableau calculation for whisker limits

You wrote this Tableau calculated field to find the upper whisker limit for a box plot:
IF SUM([Sales]) < WINDOW_PERCENTILE(SUM([Sales]), 0.75) + 1.5 * (WINDOW_PERCENTILE(SUM([Sales]), 0.75) - WINDOW_PERCENTILE(SUM([Sales]), 0.25)) THEN SUM([Sales]) ELSE NULL END
But the whisker is not displaying correctly. What is the main issue?

A The 1.5 multiplier should be applied to the sum of percentiles, not their difference.

B WINDOW_PERCENTILE cannot be nested inside IF statements in Tableau.

C The calculation should compare individual row sales, not SUM([Sales]) aggregated over the window.

D The calculation should use MAX instead of IF to find the maximum value within the limit.

💡 Hint

Think about what level of aggregation is needed for the comparison.

The calculation compares the aggregated SUM([Sales]) which is the total over the partition, not individual row values. To find whisker limits, compare individual row sales values to the threshold.

🎯 Scenario

expert

Designing a dashboard with box plots for multiple categories

You need to build a Tableau dashboard showing box plots of sales distribution for multiple product categories and subcategories. Users want to filter by region and time period. Which approach best ensures accurate box plots and responsive filtering?

A Create separate worksheets for each category, add box plots individually, then combine them in the dashboard without filters.

B Create a single worksheet with Category and Subcategory on Columns, Sales on Rows, add box plot from Analytics, then add Region and Date filters to the dashboard with 'Apply to all worksheets'.

C Create a worksheet with Category on Rows, Sales on Columns, use a histogram instead of box plots, and add filters only for Region.

D Create a worksheet with Subcategory on Rows, Sales on Rows, add box plot, and use context filters for Region and Date.

💡 Hint

Think about how to build a flexible, filterable dashboard with multiple categories.

Using a single worksheet with both Category and Subcategory allows consistent box plots. Adding filters with 'Apply to all worksheets' ensures responsive filtering across the dashboard.