ungrouped

简明释义

英[ʌnˈɡruːpt]美[ʌnˈɡrupt]

adj. 未分组的

英英释义

单词用法

同义词

反义词

例句

1.You won’t be able to update an ungrouped chart by changing its underlying data and you might lose special formatting.

您将无法通过改变底层数据来对一个分解后的图表进行更新，同时您将可能会失去某些格式。

2.You won’t be able to update an ungrouped chart by changing its underlying data and you might lose special formatting.

您将无法通过改变底层数据来对一个分解后的图表进行更新，同时您将可能会失去某些格式。

3.For simplicity, we assume that with grouping, there are no ungrouped views.

为简单起见，我们假定分组以后再没有未分组的视图。

4.When dealing with ungrouped 未分组的 numbers, it's important to calculate the mean accurately.

处理未分组的数字时，准确计算平均值非常重要。

5.The data was presented in an ungrouped 未分组的 format, making it difficult to analyze trends.

数据以未分组的格式呈现，这使得分析趋势变得困难。

6.In the statistics class, we learned how to convert ungrouped 未分组的 data into grouped data for easier interpretation.

在统计课上，我们学习了如何将未分组的数据转换为分组数据，以便于解释。

7.The survey results were ungrouped 未分组的, which required additional processing before drawing conclusions.

调查结果是未分组的，这需要额外处理后才能得出结论。

8.The ungrouped 未分组的 list of items made it challenging to prioritize tasks.

这个未分组的物品清单使得优先处理任务变得具有挑战性。

作文

In the study of data analysis, one often encounters various types of data sets. Among these, there are grouped and ungrouped data. Grouped data refers to data that is organized into classes or intervals, making it easier to analyze and interpret. On the other hand, ungrouped data, or 未分组 数据, consists of individual data points that are not categorized into any specific groups. Understanding the difference between these two types of data is crucial for anyone involved in statistical analysis. When dealing with ungrouped data, one must consider the implications of working with raw numbers. For instance, if a researcher collects the ages of a group of people without categorizing them, they have ungrouped data. This can be challenging because it requires more effort to find trends or patterns. However, it also offers a more detailed view of the data as every individual value is preserved. In contrast, when data is grouped, it simplifies the analysis process. For example, if the same researcher decides to group the ages into ranges (e.g., 0-10, 11-20, etc.), they are now working with grouped data. This allows for quicker visualizations and interpretations, such as creating histograms or frequency distributions. While grouped data can make analysis easier, it may also lead to a loss of important information about the individual data points. The choice between using ungrouped and grouped data often depends on the research goals. If the objective is to understand the overall trends within a population, grouped data may suffice. However, if the researcher aims to examine specific characteristics of individuals, ungrouped data is essential. Moreover, in certain statistical tests, the type of data used can significantly affect the results. For example, some parametric tests require data to be normally distributed, which is easier to assess with grouped data. Nevertheless, ungrouped data can provide more accurate results when analyzing smaller samples or unique cases. In conclusion, both grouped and ungrouped data have their advantages and disadvantages. While grouped data offers simplicity and ease of analysis, ungrouped data provides a more comprehensive view of the individual data points. Understanding when and how to use each type of data is vital for effective data analysis and interpretation. By recognizing the importance of ungrouped data, researchers can enhance their analytical skills and produce more insightful findings.

在数据分析的研究中，人们经常会遇到各种类型的数据集。在这些数据集中，有分组数据和未分组 数据。分组数据是指将数据组织成类或区间，使其更易于分析和解释。另一方面，未分组 数据由不属于任何特定组的单个数据点组成。理解这两种数据类型之间的区别对于任何从事统计分析的人来说都是至关重要的。在处理未分组 数据时，必须考虑使用原始数字的影响。例如，如果研究者收集了一组人的年龄而没有对其进行分类，那么他们就得到了未分组 数据。这可能会很具挑战性，因为需要更多的努力来寻找趋势或模式。然而，它也提供了对数据更详细的视图，因为每个个体值都被保留。相比之下，当数据被分组时，它简化了分析过程。例如，如果同一位研究者决定将年龄分组为范围（例如，0-10岁，11-20岁等），那么他们现在正在使用分组数据。这允许更快的可视化和解释，例如创建直方图或频率分布。虽然分组数据可以使分析更容易，但也可能导致丢失有关个体数据点的重要信息。在使用未分组 和分组数据之间的选择通常取决于研究目标。如果目标是了解人群中的整体趋势，则分组数据可能足够。然而，如果研究者旨在检查个体的特征，未分组 数据则是必不可少的。此外，在某些统计检验中，使用的数据类型可能会显著影响结果。例如，一些参数检验要求数据呈正态分布，而这在使用分组数据时更容易评估。然而，在分析较小样本或独特案例时，未分组 数据可以提供更准确的结果。总之，分组数据和未分组 数据各有其优缺点。虽然分组数据提供了简单性和分析的便利，但未分组 数据提供了对个体数据点更全面的视图。了解何时以及如何使用每种类型的数据对于有效的数据分析和解释至关重要。通过认识到未分组 数据的重要性，研究人员可以提高他们的分析技能并产生更具洞察力的发现。