scattergram

简明释义

英[ˈskætəɡræm]美[ˈskætərɡræm]

n. 散点图；分布图；点状图

英英释义

单词用法

同义词

反义词

例句

1.Graph plotting part consists of histogram, cross section drawing, clustering spectral pattern, 2-dimensinoal factor scattergram.

图形绘制包括：直方图、因子平面散点图、聚类谱系图、剖面图。

2.Graph plotting part consists of histogram, cross section drawing, clustering spectral pattern, 2-dimensinoal factor scattergram.

图形绘制包括：直方图、因子平面散点图、聚类谱系图、剖面图。

3.You can create a scattergram using Excel to analyze the relationship between study hours and exam scores.

您可以使用Excel创建一个散点图来分析学习时间与考试分数之间的关系。

4.The scattergram revealed several outliers that needed further investigation.

该散点图揭示了几个需要进一步调查的异常值。

5.The researchers plotted the data points on a scattergram to visualize the correlation between the two variables.

研究人员在一个散点图上绘制数据点，以可视化两个变量之间的相关性。

6.In the meeting, we used a scattergram to demonstrate how customer satisfaction varies with service speed.

在会议中，我们使用了一个散点图来展示客户满意度如何随着服务速度而变化。

7.The scattergram indicated a positive trend in sales as advertising expenditure increased.

该散点图显示出随着广告支出的增加，销售额呈正趋势。

作文

In the field of statistics and data analysis, visual representation of data is crucial for understanding relationships and trends. One such visual tool is the scattergram, which is commonly used to display the relationship between two quantitative variables. A scattergram consists of a set of points plotted on a two-dimensional graph, where each point represents an observation from the dataset. The x-axis typically represents one variable, while the y-axis represents another variable. This graphical representation allows researchers and analysts to quickly identify correlations, clusters, and outliers in their data.For instance, consider a study examining the relationship between hours studied and exam scores among students. By creating a scattergram with hours studied on the x-axis and exam scores on the y-axis, one can visualize how the two variables interact. If the points on the scattergram tend to rise together, it suggests a positive correlation: as study hours increase, so do exam scores. Conversely, if the points are scattered randomly without any discernible pattern, it may indicate no significant correlation between the two variables.Moreover, a scattergram can also help identify outliers—data points that deviate significantly from other observations. For example, if a student studied for a very long time but still received a low exam score, this point would stand out on the scattergram and warrant further investigation. Understanding these outliers is essential as they can provide insights into anomalies or unique cases within the dataset.In addition to identifying correlations and outliers, scattergrams can also be used to explore potential causal relationships. While correlation does not imply causation, observing a strong correlation in a scattergram can lead researchers to hypothesize about possible causal links. For instance, if a scattergram shows that increased physical activity correlates with lower body mass index (BMI), researchers might investigate whether increasing exercise leads to weight loss or if other factors are involved.Furthermore, the use of colors and shapes in a scattergram can enhance its informative value. By differentiating data points using color coding or different shapes, analysts can represent additional categorical variables. For instance, in a scattergram plotting students' study hours against exam scores, different colors could represent students from various majors. This added layer of information can reveal trends specific to certain groups, allowing for more nuanced interpretations of the data.In conclusion, the scattergram is an invaluable tool in data analysis, providing a clear and effective way to visualize relationships between variables. Its ability to highlight correlations, outliers, and potential causal links makes it a favorite among statisticians and researchers alike. As we continue to collect and analyze vast amounts of data in various fields, mastering the use of tools like the scattergram will be essential for drawing meaningful conclusions and making informed decisions based on data. The versatility and clarity of the scattergram ensure that it will remain a staple in the toolkit of anyone working with quantitative data.

在统计学和数据分析领域，数据的可视化表示对于理解关系和趋势至关重要。其中一种常用的可视化工具是散点图，它通常用于显示两个定量变量之间的关系。散点图由一组点组成，这些点绘制在二维图上，每个点代表数据集中一个观察值。x轴通常表示一个变量，而y轴则表示另一个变量。这种图形表示使研究人员和分析师能够快速识别数据中的相关性、聚类和异常值。例如，考虑一项研究，考察学生学习时间与考试成绩之间的关系。通过创建一个以学习时间为x轴，以考试成绩为y轴的散点图，可以直观地看到这两个变量之间的相互作用。如果散点图上的点趋于一起上升，这表明存在正相关：学习时间增加，考试成绩也提高。相反，如果点随机分散，没有明显的模式，则可能表明这两个变量之间没有显著的相关性。此外，散点图还可以帮助识别异常值——那些显著偏离其他观察值的数据点。例如，如果一名学生学习了很长时间，但仍然得到了低分，这个点在散点图上会显得突出，需要进一步调查。理解这些异常值至关重要，因为它们可以提供有关数据集中异常或独特案例的见解。除了识别相关性和异常值外，散点图还可以用于探索潜在的因果关系。虽然相关性并不意味着因果关系，但在散点图中观察到强相关性可以促使研究人员假设可能的因果联系。例如，如果散点图显示增加的身体活动与较低的体重指数（BMI）相关，研究人员可能会调查增加锻炼是否导致体重减轻，或者是否涉及其他因素。此外，在散点图中使用颜色和形状可以增强其信息价值。通过使用颜色编码或不同形状区分数据点，分析人员可以表示额外的类别变量。例如，在一个绘制学生学习时间与考试成绩的散点图中，不同的颜色可以代表来自不同专业的学生。这一附加信息层可以揭示特定群体的趋势，从而使对数据的解释更加细致。总之，散点图是数据分析中不可或缺的工具，提供了一种清晰有效的方式来可视化变量之间的关系。它突出相关性、异常值和潜在因果关系的能力使其成为统计学家和研究人员的最爱。随着我们在各个领域继续收集和分析大量数据，掌握像散点图这样的工具将对基于数据得出有意义的结论和做出明智的决策至关重要。散点图的多样性和清晰度确保它将在任何处理定量数据的人的工具箱中保持重要地位。