quartiles

简明释义

英[ˈkwɔː.taɪlz]美[ˈkwɔr.taɪlz]

n. 四分位（quartile 的复数形式）；[数]四分位数

英英释义

单词用法

同义词

反义词

例句

1.People in the two highest quartiles had a 2.2 times increased risk of heart disease compared to those in the lowest.

有两个最高四分位数的人比那些最低的人患心脏病的风险增加2.2倍。

2.Applying the median, lower and upper quartiles as measures of location.

根据度量的程度来应用中等、低级和高级质量。

3.Participants in the highest vs. the lowest quartile demonstrated a 4.3-fold greater risk of death, and the intermediate quartiles demonstrated intermediate risks, " the researchers write."

处于最高四分位的患者的死亡风险比最低四分位患者的死亡风险要高4.3倍，而中间两个四分位的患者的死亡风险则处于中间水平。

4.The distribution of bonuses will be skewed towards top performers, leaving little or no money for those in the lower quartiles.

奖金的分配是偏向业绩的佼佼者，业绩差的部分极少或更本就没有。

5.Quartiles divide the data into four equal parts.

四分位点把资料分成四等分。

6.Odds ratios (ORs) were calculated for the lowest quartile of each nutrient using the other 3 quartiles combined as the reference group.

用其他三个四分位数联合作为对照，通过每个营养素的下四分位数计算出优势比(ORs)。

7.Each group of statistics are indicated with medians and quartiles;

各组内数据描述用中位数和四分位数表示；

8.If you divide the 20th century into quartiles, the period when the initial yield gap was highest (ie, bonds yielded more than cash) saw a subsequent ten-year real return from gilts of just 0.3%.

如果你把二十世纪分成四个时期，初始收益差最高的时期(例如，债券比现金收益大)之后会遇到十年金边债券净收益只有0.3%的情况。

9.In statistics, we often divide data into four equal parts using quartiles (四分位数) to analyze its distribution.

在统计学中，我们经常使用四分位数（quartiles）将数据分成四个相等的部分，以分析其分布。

10.The third quartile (第三四分位数) indicates that 75% of the data points fall below this value.

第三四分位数（quartile）表示75%的数据点低于这个值。

11.Using quartiles (四分位数), we can identify outliers in a data set more effectively.

使用四分位数（quartiles），我们可以更有效地识别数据集中的异常值。

12.To better understand the test scores, we calculated the quartiles (四分位数) to see how students performed in different ranges.

为了更好地理解考试成绩，我们计算了四分位数（quartiles），看看学生在不同范围内的表现如何。

13.The first quartile (第一四分位数) represents the 25th percentile of the data set.

第一四分位数（quartile）表示数据集的第25百分位数。

作文

In the field of statistics, understanding data distribution is crucial for making informed decisions. One of the key concepts that help in analyzing data sets is the concept of quartiles. The term quartiles refers to the values that divide a data set into four equal parts. Each part contains 25% of the data points when arranged in ascending order. This division allows statisticians and researchers to gain insights into the spread and center of the data, which is essential for various analyses.To better understand quartiles, let's consider an example. Imagine you have a data set representing the test scores of students in a class: 56, 67, 78, 80, 85, 90, 92, 95, 98. First, we need to arrange the scores in ascending order, which they already are in this case. Next, we calculate the first quartile (Q1), which is the median of the first half of the data. In this example, the first half is 56, 67, 78, and 80. The median of these values is (67 + 78) / 2 = 72.5. Thus, Q1 = 72.5.Next, we find the second quartile (Q2), which is simply the median of the entire data set. For our scores, the median is (85 + 90) / 2 = 87.5. Therefore, Q2 = 87.5. Finally, we calculate the third quartile (Q3), which is the median of the second half of the data: 85, 90, 92, 95, 98. The median here is (90 + 92) / 2 = 91. Hence, Q3 = 91.Now that we have our quartiles: Q1 = 72.5, Q2 = 87.5, and Q3 = 91, we can analyze the spread of the data more effectively. The interquartile range (IQR), which is the difference between Q3 and Q1, helps us understand the variability in the middle 50% of the data. In this case, IQR = Q3 - Q1 = 91 - 72.5 = 18.5. This indicates that the central half of the test scores varies by 18.5 points.Understanding quartiles is essential not only in academic settings but also in various fields such as finance, healthcare, and social sciences. For instance, in finance, analysts often use quartiles to assess the performance of investment portfolios. By dividing returns into quartiles, they can identify which investments are performing well and which are underperforming. Similarly, in healthcare, quartiles can help researchers understand patient outcomes by comparing different treatment groups.Moreover, visual representations, such as box plots, often incorporate quartiles to summarize data distributions effectively. A box plot displays the minimum, Q1, median (Q2), Q3, and maximum values of a data set, providing a clear visual summary of its dispersion and central tendency.In conclusion, the concept of quartiles is a powerful statistical tool that aids in data analysis. By dividing data into four equal parts, quartiles enable researchers to understand the distribution and variability of data sets. Whether in education, finance, or healthcare, mastering the use of quartiles is vital for making data-driven decisions and drawing meaningful conclusions from numerical information.

在统计学领域，理解数据分布对于做出明智的决策至关重要。帮助分析数据集的关键概念之一是四分位数的概念。术语四分位数指的是将数据集划分为四个相等部分的值。每个部分在按升序排列时包含25%的数据点。这种划分使统计学家和研究人员能够深入了解数据的分布和中心，这对于各种分析至关重要。为了更好地理解四分位数，我们来看一个例子。假设你有一个代表班级学生测试分数的数据集：56、67、78、80、85、90、92、95、98。首先，我们需要将分数按升序排列，而在这种情况下它们已经排列好了。接下来，我们计算第一个四分位数（Q1），即数据前半部分的中位数。在这个例子中，前半部分是56、67、78和80。这些值的中位数是（67 + 78）/ 2 = 72.5。因此，Q1 = 72.5。接下来，我们找到第二个四分位数（Q2），这只是整个数据集的中位数。对于我们的分数，中位数是（85 + 90）/ 2 = 87.5。因此，Q2 = 87.5。最后，我们计算第三个四分位数（Q3），即数据后半部分的中位数：85、90、92、95、98。这里的中位数是（90 + 92）/ 2 = 91。因此，Q3 = 91。现在我们有了我们的四分位数：Q1 = 72.5，Q2 = 87.5，Q3 = 91，我们可以更有效地分析数据的分布。四分位距（IQR），即Q3与Q1之间的差异，帮助我们理解数据中间50%的变异性。在这个例子中，IQR = Q3 - Q1 = 91 - 72.5 = 18.5。这表明测试分数的中间一半变动了18.5分。理解四分位数不仅在学术环境中重要，在金融、医疗保健和社会科学等各个领域也至关重要。例如，在金融领域，分析师通常使用四分位数来评估投资组合的表现。通过将回报划分为四分位数，他们可以识别哪些投资表现良好，哪些表现不佳。同样，在医疗保健中，四分位数可以帮助研究人员通过比较不同的治疗组来理解患者的结果。此外，箱形图等可视化表示通常结合四分位数来有效总结数据分布。箱形图显示数据集的最小值、Q1、中位数（Q2）、Q3和最大值，提供了数据分散性和集中趋势的清晰可视化摘要。总之，四分位数的概念是一个强大的统计工具，有助于数据分析。通过将数据划分为四个相等的部分，四分位数使研究人员能够理解数据集的分布和变异性。无论是在教育、金融还是医疗保健，掌握四分位数的使用对于做出数据驱动的决策和从数值信息中得出有意义的结论至关重要。