Box Plot

1) Box Plot, or box-and-whisker plot, is a statistical tool that illustrates data distribution and variability. It highlights the median, quartiles, and outliers of data sets, making it essential for detecting anomalies and understanding data spread. 

2) Box plots are particularly useful in quality management, performance evaluations, and comparative analysis, providing a compact yet comprehensive overview of data characteristics. Employing box plots in work environments facilitates informed decision-making through clear, visual representation of statistical data.

3) Version

4) Application Scenarios：To analyze various indicators such as purchase prices, a box plot is an effective tool for examining a range of price-related statistics, from highest to lowest prices. This type of plot aids in understanding the distribution characteristics of data and in identifying outliers. Box plots are especially valuable for tasks that require robust statistical analysis, including quality management, personnel evaluation, and exploratory data analysis, providing a clear visual summary of data variances.

5) Basic Requirements

6) Characteristics：

Advantages: display statistical graphs of continuous data distribution.

Disadvantages: not ideal for large datasets, trend or percentage data representation.

7) Structure

The median (Q2) is represented by a line through the middle of the box. The upper and lower edges of the box represent the upper quartile (Q3) and lower quartile (Q1) of the data, encompassing 50% of the data. The height of the box indicates data fluctuation, while any points beyond the maximum or minimum values are considered outliers. To learn more about the three indicators Q1, Q2, and Q3 in box plot, refer to statistical concept quartiles, see Quartile.

Maximum and minimum values are calculated using the following formula:

Maximum value: Q3 + 1.5*IQR (IQR = Q3 - Q1)

Minimum value: Q1 - 1.5*IQR

Preparing Data

1. Log in to FineBI and click New Subject.

2. In the Select Data interface, click Local Excel > Upload to upload local excel files.

Example data: Sales.xlsx

3. Once uploaded, click OK.

Creating Components

1. Click Component.

2. Select Box Chart as Chart Type, and drag State to the horizontal axis, Sales to the vertical axis, and City to Fine-grained.

Customizing Components

Drag City to Color to add color differentiation.

Demonstration

Preparing Data

Example data: Contract Information.xlsx

Procedure of data preparation is the same as example one.

Creating Components

1. Click Component at the lower left corner.

2. Select Custom Chart as Chart Type and drag Contract Type to the horizontal axis, and Product to the vertical axis twice. In Graphic Properties, set the two Product indicators to display as box plot and point respectively.

3. Drag Customer ID to Fine-grained in All under Graphic Properties.

Customizing Components

1. Adjusting Point Size

Under Chart Properties, click open the indicator field which you set to display as points and adjust Radius (the size of the point in this case) in Size.

2. Adjusting the Maximum Value

Hover your cursor over the field dragged to the vertical axis, click  > Set Value Axis(Left-value Axis). In Display Range, select Custom and set the maximum value as 60.

Demonstration

You can clearly identify outliers in this chart.

In conclusion, the box plot is an indispensable tool for statistical analysis, offering a clear visualization of data distribution, variability, and outliers. It is particularly useful in diverse fields such as quality management, personnel evaluation, and exploratory data analysis. By providing a succinct graphical summary of multiple data points, box plots enable professionals to make informed decisions based on comprehensive statistical insights.