Box and Whiskers Charts - Definition and Free Tool to Create Them

This document will discuss the uses of Box and Whiskers Charts, and will show how you can create them in Google Sheets and InetSoft. It will also provide access to a free online tool for creating Box and Whiskers Charts and complete functioning business intelligence dashboards.

ContentsDefinition of a Box and Whiskers PlotWhy Use a Box and Whiskers Chart?How to Make a Box and Whiskers ChartHow to Create a Box Plot in Google SheetsTool to a Box and Whiskers Chart Online for Free

Definition of a Box and Whiskers Plot

A Box and Whiskers Chart, also known as a Box Plot, is a graphical representation of statistical data that displays the distribution of a dataset. It is created by dividing the data into four quartiles, with the top and bottom quartiles representing the upper and lower "boxes" of the plot. The middle line represents the median of the data, and the "whiskers" extending from the box represent the range of the data, excluding any outliers. Outliers, which are data points that are significantly different from the rest of the data, are typically represented by small circles or crosses outside the range of the whiskers.

Why Use a Box and Whiskers Chart?

Box and Whiskers charts are useful for visualizing the spread of a dataset and identifying any potential outliers. They are commonly used in statistical analysis and data visualization. There are several situations in which you might want to use a Box Plot:

  1. Visualize the spread of a dataset: A Box Plot can show the range, interquartile range, and distribution of the data, which can help you understand the overall shape of the data and identify any potential outliers.
  2. Compare multiple datasets: Box Plots can be used to compare multiple datasets side by side, which can be useful for identifying differences or similarities between the datasets.
  3. Identify outliers: Box Plots can help you identify any potential outliers in your data. Outliers are data points that are significantly different from the rest of the data and can indicate a problem with the data collection or an unusual event.

To summarize, a Box Plot can provide a quick summary of a dataset, including the median, range, and quartiles, which can be useful for comparing datasets or communicating the key characteristics of a dataset to others.

How to Make a Box and Whiskers Chart in InetSoft

To create a Box Plot in InetSoft, drag a Chart component from the Toolbox panel into a dashboard in Visual Composer , and then press the Edit button to open the Chart Editor.

Press the Select Chart Style button. Choose the Box Plot style. Press the Apply button. Drag a desired dimension to the 'X' or 'Y' region.

From the Measures folder of the Data Source panel, drag a measure to the 'X' or 'Y' region. This places the selected field onto the chart as a measure.

The measure is represented in terms of the "box" (lower quartile, median, upper quartile), "whiskers" (minimum and maximum, excluding outliers), and individual outliers. An outlier is a value that is less than the lower quartile or greater than the upper quartile by more than 1.5 times the inter-quartile range. You can of course add colors and other formatting as desired, but the basic Box and Whiskers plot is complete. Press the 'Finish' button to close the Editor.

How to Create a Box Plot in Google

Google Sheets does not provide a Box and Whiskers chart type, so it is necessary to manually compute the quartiles, max, min, and outliers for every group that you wish to include on the chart. This can be very tedious and is better left to other software.

Tool to Make a Box and Whiskers Chart Online for Free

To easily and quickly create Box Plots online for free,create a Free Individual Accounton the InetSoft website. You will then be able to upload a spreadsheet data set, as shown below:

Once you have done that, you will be able to proceed to the Visualization Recommender , which will get you started creating a dashboard. To start with a Box Plot, select a dimension and measure that you want to use, and press the Box Plot button in the top bar of the Recommender.

Then press the Full Editor button at the top right. Proceed to modify the Chart or add other components using the Visual Composer options shown earlier.

When Are Box and Whiskers Charts Used?

Box and whisker charts (also known as box plots) are used in data analysis and statistics to visualize the distribution, central tendency, and variability of a dataset. They are particularly valuable in highlighting outliers, medians, and quartiles in a way that is compact and easily interpretable. Here's a detailed look into when and why they are used:

1. Comparing Distributions Across Groups

Box plots are exceptionally useful when you want to compare the distribution of values across multiple groups or categories. For example:

  • In education : Comparing test scores of students across different classrooms or schools.
  • In business : Comparing customer satisfaction scores across different regions or branches.
  • In healthcare : Comparing patient recovery times across treatment groups.

Each box in the chart shows the interquartile range (IQR), with the median marked inside the box, and whiskers extending to show the range (excluding outliers). This enables side-by-side visual comparison of multiple datasets.

2. Identifying Outliers

One of the standout features of a box and whisker chart is its ability to highlight outliers —data points that are significantly higher or lower than the rest of the data. This is particularly valuable in:

  • Quality control : Spotting defects or anomalies in manufacturing.
  • Finance : Detecting unusual transactions or returns.
  • Environmental science : Identifying extreme temperature or pollution readings.

By making outliers visually distinct (usually as dots outside the whiskers), these charts help analysts quickly pinpoint unusual behavior.

3. Summarizing Large Data Sets

Box plots efficiently summarize a large set of data points with just five statistics :

  • Minimum (excluding outliers)
  • First quartile (Q1)
  • Median (Q2)
  • Third quartile (Q3)
  • Maximum (excluding outliers)

Because they compress a lot of data into a small space, they're ideal for exploratory data analysis where you want a high-level overview of data shape and spread .

4. Checking for Skewness and Symmetry

The shape of a box plot also gives quick insights into whether a distribution is symmetric, skewed left, or skewed right :

  • If the median is centered and the whiskers are roughly equal in length, the distribution is likely symmetric.
  • If the median is closer to the bottom or top of the box, or one whisker is longer, this indicates skewness .

This is helpful in determining whether you should apply data transformations or different statistical models .

5. Pre-Modeling Data Analysis

Before performing regression, machine learning, or other statistical modeling, box plots help you understand:

  • The range and variability of input features
  • Potential issues with scale or normalization
  • Whether variables need to be adjusted or removed

They're a staple in the EDA (exploratory data analysis) stage of most data science projects.

6. Visualizing Temporal or Geographic Trends

When combined with time-series or geospatial data, box plots help:

  • Summarize data over time , such as monthly sales across years
  • Show variability across geographical units , like states or countries

For example, you could visualize monthly temperatures over 10 years using box plots for each month, helping you see seasonality and changes in variability.

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