A Box Plot is the visualization design we recommend if your goal is to display quartiles, mean, and **outliers** attributes in data.

*So, what is an outlier?*

**An outlier** is a value that lies in the extremes of a data series and thus can affect the overall observation. Outliers are also termed as **extremes** because they lie on either end of a data.

A Box Plot Outliers detector (Box and Whisker Graph) is easy to interpret, even for non-technical audiences.

Excel lacks Box Plot Charts. And this means you’ve got to use other pricey tools or plot the chart manually. So, if your goal is to display high-level insights, you’ve got to think beyond Excel.

We’re not recommending you do away with Excel, especially if your goal is to access ready-made and visually appealing Box Plots.

Download and install a particular add-in (which we’ll mention later) into your Excel to access the ready-to-go Box Plot Outliers detector.

In this blog, you’ll learn:

- What is a Box Plot?
- What are Box Plot Outliers?
- What is the 1.5 IQR Rule?
- How to Calculate Box Plot Values?
- How to Create the Chart Using Box Plot Outliers Generator?
- Video Tutorial: How to create Box and Whisker Column Chart in Excel?
- Advantages of Box Plot Outliers
- Wrap Up

Before addressing the how-to guide, let’s address the following question: what is a Box Plot?

A Box Plot is a visualization design that uses box shapes to display insights into data.

The chart simplifies bulky and complex data sets into quartiles and averages. Also, you can use the chart to pinpoint outliers in your data. The Box Plot segments key variables in quarters or (quartiles).

For instance, you can draw boxes to connect the first quartile to the third quartile. In this case, the boxes will represent the average values of key data points.

Whiskers are lines that identify numbers outside of the average data points.

The chart displays your data’s shape, variability, and center (or median) information. Also, you can leverage the chart to determine the skewness of data points.

Essentially, a Box and Whisker Chart shows the following points of data:

*Minimum range**Lower quartile**Median**Upper quartile**Maximum range*

Besides the five summary numbers, the visualization displays the following:

- Minimum score
- Median
- Maximum Score
- Lower Quartile
- Upper Quartile

*Let’s check out the elements in detail.*

The minimum score is the lowest score, excluding outliers (shown at the end of the left whisker).

Lower Quartile: 25% of all variables fall below the lower quartile value.

The median is the mid-point of the data and is shown by the line that divides the box into two parts (sometimes known as the second quartile). Half the scores are greater than or equal to this value, and half are less.

The upper quartile is 75% of all variables that fall below the upper quartile value (also known as the third quartile). Thus, 25% of data points are above the value.

The maximum score is the highest score, excluding outliers (shown at the end of the right whisker).

Whiskers: The upper and lower whiskers represent scores outside the middle 50% (i.e., the lower 25% of scores and the upper 25% of scores).

The interquartile range (IQR) ranges between the 25th and 75th percentile).

A Box and Whisker Graph can help you to visualize large datasets. More so you can easily detect the symmetry of the data at a glance by using the chart. Unlike other data visualization techniques, the Box Plot displays outliers.

The visualization design is best-suited for comparing distributions between key groups in data. The charts are compact in design to help you display a ton of information without clutter.

Data sets can sometimes contain outliers that are suspected to be anomalies (perhaps because of data collection errors or just plain old flukes).

If outliers are present, the whisker on the appropriate side is drawn to 1.5 * IQR rather than the data minimum or the data maximum. Small circles or unfilled dots are drawn on the chart to indicate where suspected outliers lie. On the other hand, filled circles are used for known outliers.

Keep reading because we’ll show you how to spot Box Plot Outliers in the coming sections.

**Definition**: An outlier is a value that lies in both extremes of data. In other words, it’s a value that lies outside the overall distribution pattern and thus can affect the overall data series.

These anomalies are treated as abnormal values that can distort the final insights.

Data visualization experts agree that a value should be regarded as an outlier if it’s 1.5 times bigger or smaller than the expected observation.

In the coming section, we’ll address the following question: what is the 1.5 IQR rule?

To understand the 1.5 IQR rule, we’ll cover the interquartile range, abbreviated as the IQR.

The interquartile range is just the width of the box in the chart.

In other words, **IQR = Q3 – Q1.**

The IQR measures how key data points are spread out. Therefore, an outlier is **1.5 multiplied by the IQR** value of your data.

Keep reading to discover how to use the Box Plot Diagram to identify outliers. You don’t want to miss this.

Box and Whisker Plot show the distribution of key data points along a number line.

You can generate the chart by ordering a data set to find the median, upper and lower quartiles, and upper and lower extremes.

To calculate values, such as mean, follow the steps below:

- Order the data from least to greatest.
- Find the median or middle value that splits the data set into two equal groups. If there is no middle value, use the average of the two middle values as the median.
- Find the median for the lower half of the data set.
- Find the median for the upper half of the data set.
- Use these five values to construct a Box Plot displaying the following:
*lower extreme, lower quartile, median, upper quartile, upper extreme.* - Draw vertical lines through the lower quartile, median, and upper quartile.
- Form a box by connecting the vertical lines from the lower quartile, median, and upper quartile.
- Plot the whiskers from the extremes of the box.

Keep reading to learn how to identify Box Plot Outliers effortlessly. Also, you’ll discover how to use Box Plot Diagram to identify outliers.

*You don’t want to miss this.*

Excel is one of the go-to data visualization tools for businesses and professionals.

However, this freemium spreadsheet tool does not natively support Box Plot Outliers Diagram. In other words, you’ll never find this visualization design in Excel.

Well, you don’t have to do away with the spreadsheet app.

You can turn Excel into a reliable data visualization tool loaded with ready-made and visually stunning Box and Whisker plot examples by installing third-party apps, such as ChartExpo.

How to read a Box Plot in Excel should never be a stressful task. Keep reading to learn more.

**Why ChartExpo?**

ChartExpo is an add-in you can easily install in your Excel.

With a large number of advanced visualizations, ChartExpo turns your complex, raw data into *compelling, **easy-to-decode, visual renderings* that tell the story of your data.

The application produces simple, ready-to-go, and clear visualization designs with just a few clicks.

Yes, ChartExpo generates ready-made Box Plots that are amazingly easy to interpret, even for non-technical audiences.

In the coming section, you’ll get to see How to Make a Box and Whisker Plot Using ChartExpo to find outliers. You don’t want to miss this.

This section will use the box and whisker chart in Excel to find the outlier in the data below.

Gender |
Age |

Male | 29 |

Male | 34 |

Male | 37 |

Male | 28 |

Male | 45 |

Male | 55 |

Male | 36 |

Male | 28 |

Male | 43 |

Male | 35 |

Male | 45 |

Male | 34 |

Male | 31 |

Male | 32 |

Male | 34 |

Male | 30 |

Male | 36 |

Male | 42 |

Male | 32 |

Male | 48 |

Male | 48 |

Male | 27 |

Male | 31 |

Male | 24 |

Male | 78 |

Female | 22 |

Female | 38 |

Female | 40 |

Female | 25 |

Female | 23 |

Female | 36 |

Female | 37 |

Female | 49 |

Female | 44 |

Female | 26 |

Female | 41 |

Female | 30 |

Female | 38 |

Female | 45 |

Female | 40 |

Female | 25 |

Female | 38 |

Female | 26 |

Female | 44 |

Female | 39 |

Female | 43 |

Female | 44 |

Female | 48 |

Female | 31 |

Female | 33 |

Female | 38 |

Female | 37 |

Female | 38 |

Female | 43 |

Female | 67 |

Female | 76 |

Female | 80 |

Click here to install ChartExpo into your Excel. Once you’re done, follow the easy steps below to create box and whisker chart in Excel.

- Open your Excel and paste the table above.
- Open the worksheet and click the
**Insert Menu**button. - Click the
**My Apps**button and then click the**See All,**as shown below.

- Click the
**Insert**button to get started with ChartExpo.

- Look for the
**“Box and Whisker Bar Chart****”**in the list of charts. - Once the Chart pops up, click on its icon to get started, as shown below.

- Select the sheet holding your data and click the
**Create Chart From Selection.**

- Check out the final
**Box Plot outlier Chart**below.

- You can easily identify the outliers in this chart as shown below.

- The ages between 68 and 85 years were outliers.
- The maximum and minimum ages among the males were 26 and 57 years, respectively.
- The maximum and minimum ages among the females were 20 and 48 years, respectively.
- The median age of male respondents is 39 years. Conversely, the median age of females is 42 years.

In this video, you’ll learn how to create Box and Whisker Column Chart in Excel.

Check out the benefits of the chart below:

- A Box and Whisker Graph can help you to visualize large datasets.
- You can easily detect the symmetry of the data at a glance by using the chart.
- Unlike other data visualization techniques, the Box Plot displays outliers.

John Tukey was the first person to use Box Plot outliers to display insights into data. He came up with the 1.5 IQR requirement to pinpoint outliers.

The IQR measures how key data points are spread out. Therefore, an outlier is **1.5 multiplied by the IQR** value of your data.

An outlier is a value that lies in both extremes of data.

In other words, it’s a value that lies outside the overall distribution pattern and thus can affect the overall data series.

Outliers are caused by the following:

*System behavior**Fraudulent behavior**Human and instrument errors*

As we said, a Box Plot is the visualization design we recommend if your goal is to display quartiles, mean, and **outliers** attributes in data.

*So, what is an outlier?*

**Outlier** is a value that lies in the extremes of a data series and thus can affect the overall observation. Outliers are also termed as **extremes** because they lie on either end of a data.

A Box Plot Outliers detector (Box and Whisker Graph) is easy to interpret, even for non-technical audiences.

*Excel lacks Box Plots. *

The only viable options are using other pricey data visualization tools or plotting the chart manually.

Think beyond the spreadsheet application if you intend to display attributes, such as mean, outliers, and quartiles in your data.

*So, what’s the solution?*

We recommend installing third-party apps, such as ChartExpo, into your Excel to access ready-made Box and Whisker Charts.

ChartExpo is an add-in you can easily download and install in your Excel app. Besides, this tool comes loaded with insightful and easy-to-interpret Box Plots. You don’t need programming skills to visualize your data using ChartExpo.

How to use a Box Plot Diagram to identify outliers should never stress you.

Sign up for a 7-day free trial today to access easy-to-interpret and ready-made Box and Whisker Charts.