慧学（37）：Origin散点图绘图学习

摘要：散点图是一种常见的数据可视化工具，它通过在二维坐标平面上以点的方式展示变量的数值，从而揭示两个变量之间的相关关系与分布特点。利用散点图，可以清晰地观察数据的变化趋势、聚集现象以及可能存在的异常点。此外，它还常被结合到回归分析中，用于验证或拟合变量之间的关系。

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“慧学（37）：Origin散点图绘图学习”

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"Hui Xue (37): Origin scatter plot drawing learning"

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一、思维导图（Mind mapping）

二、散点图简介（Introduction to scatter plots）

散点图是一种常见的数据可视化工具，它通过在二维坐标平面上以点的方式展示变量的数值，从而揭示两个变量之间的相关关系与分布特点。利用散点图，可以清晰地观察数据的变化趋势、聚集现象以及可能存在的异常点。此外，它还常被结合到回归分析中，用于验证或拟合变量之间的关系。

A scatter plot is a common data visualization tool. It displays the values of variables as dots on a two-dimensional coordinate plane, revealing the correlation and distribution characteristics between two variables. Using a scatter plot, one can clearly observe data trends, clustering, and possible outliers. Furthermore, it is often incorporated into regression analysis to verify or fit relationships between variables.

在OriginLab推出的Origin软件中，散点图功能设计相当完善。它不仅提供了多种点样式、误差线和数据标签的设置，还能够叠加回归曲线与置信区间，非常适合科研及工程领域的数据深入分析。凭借这些优势，Origin散点图已被广泛运用于自然科学与社会科学研究，成为探索变量关系和呈现实验成果的重要方式。

The scatter plot functionality in OriginLab's Origin software is exceptionally well-designed. It not only offers a variety of point styles, error bars, and data label settings, but also allows for overlaying regression curves and confidence intervals, making it ideal for in-depth data analysis in scientific research and engineering. Thanks to these advantages, Origin scatter plots have been widely used in both natural and social science research, becoming a crucial tool for exploring variable relationships and presenting experimental results.

三、绘制散点图的步骤（Steps to draw a scatter plot）

1、准备与录入数据（Preparing and entering data）

启动Origin后，首先新建一个工作簿。在表格的首行依次输入列标题，例如第一列设为“配送车辆数”，其余三列依次命名为“A配送中心日均订单量”“B配送中心日均订单量”和“C配送中心日均订单量”。接着，将实验数据按行录入表格。完成录入后，需要将“配送车辆数”列指定为X轴变量，其余三列则作为Y轴变量。最后，检查列的数据格式是否为数值型，并确认表格中不存在空白单元格或明显错误的数据点。

After launching Origin, first create a new workbook. Enter the column headings in the first row of the table. For example, set the first column to "Number of Delivery Vehicles" and name the remaining three columns "Average Daily Order Volume of Distribution Center A," "Average Daily Order Volume of Distribution Center B," and "Average Daily Order Volume of Distribution Center C." Next, enter the experimental data into the table row by row. After completing the entry, you need to designate the "Number of Delivery Vehicles" column as the X-axis variable and the remaining three columns as the Y-axis variables. Finally, check that the column data format is numeric and confirm that there are no blank cells or obviously incorrect data points in the table.

2、绘制初始散点图（Draw the initial scatter plot）

在数据表中，先选中“配送车辆数”列以及对应的三列配送中心日均订单量。然后，在顶部菜单栏选择“绘图”中的“基础2D图”，并点击“散点图”选项。此时，Origin会生成一幅散点图，三组订单量数据会以不同的散点序列显示，从而直观展现各配送中心日均订单量的差异和特点。

In the data table, first select the "Number of Delivery Vehicles" column and the corresponding three columns representing the average daily order volume for each distribution center. Then, from the top menu bar, select "Basic 2D Plot" under "Drawing" and click the "Scatter Plot" option. Origin will generate a scatter plot, displaying the three sets of order volume data as separate scatter point sequences, visually illustrating the differences and characteristics of the average daily order volume for each distribution center.

3、修改图例与点样式（Modify legend and point styles）

在生成的散点图中会自动出现图例，其内容通常对应表格中的列名。你可以通过右键点击图例并进行编辑，将其修改为更直观的说明，例如“A配送中心日均订单量”“B配送中心日均订单量”和“C配送中心日均订单量”。若需要进一步区分不同的数据组，可以双击任意一个数据系列，在“绘图细节”设置中调整颜色、点的形状及大小，从而使三组数据在视觉上更加清晰可辨。

A legend will automatically appear in the generated scatter plot, and its contents typically correspond to the column names in the table. You can right-click the legend and edit it to a more intuitive description, such as "Average Daily Orders for Distribution Center A," "Average Daily Orders for Distribution Center B," and "Average Daily Orders for Distribution Center C." To further distinguish the different data groups, double-click any data series and adjust the color, point shape, and size in the "Plot Details" settings to make the three data sets more visually distinct.

4、完善坐标轴与标签（Improve axes and labels）

为了增强图表的清晰度，可以双击坐标轴打开属性设置窗口。横坐标可命名为“配送车辆数”，纵坐标可设置为“配送中心日均订单量”。根据实际数据分布情况，适当修改纵轴的最大值及刻度间隔，使数据点分布更合理，整体布局更加紧凑。如果是用于论文或报告，还可以对字体样式和刻度方向进行调整，以提升图表的可读性与美观度。

To enhance the clarity of the chart, double-click the axis to open the property settings window. You can name the horizontal axis "Number of Delivery Vehicles" and the vertical axis "Average Daily Orders at the Distribution Center." Based on the actual data distribution, adjust the maximum value and scale interval of the vertical axis to achieve a more balanced distribution of data points and a more compact overall layout. If the chart is being used for a paper or report, you can also adjust the font style and scale orientation to improve readability and aesthetics.

5、图表美化与导出（Chart beautification and export）

在完成基础图形绘制后，可以在图表中加入标题或简要注释，例如设置标题为“车辆数与配送中心订单处理量散点图”。随后检查整体排版是否协调、美观，若结果令人满意，可将该图保存为模板，以便后续快速生成同类图表。最后，根据具体需求将图表导出为PNG、TIFF或PDF等格式，并确保分辨率符合论文或报告的使用标准。

After creating the basic graph, you can add a title or brief annotation to the chart, for example, "Scatter Plot of Vehicle Count and Distribution Center Order Volume." Then, review the overall layout for consistency and aesthetics. If satisfactory, save the chart as a template to quickly generate similar charts later. Finally, export the chart to a format such as PNG, TIFF, or PDF, depending on your needs, ensuring that the resolution meets the standards for use in a paper or report.