quantiles
简明释义
n. [计]分位数；分位点（quantile 的复数形式）
英英释义
单词用法
第一分位数 | |
第三分位数 | |
分位数范围 | |
分位数函数 | |
计算分位数 | |
确定分位数 | |
分布的分位数 | |
绘制分位数 |
同义词
反义词
例句
1.There already exist Median, Quartiles and Tenth quantile etc. Based on this, the authors put out the concept and (calculation) method for tri-sectional quantiles in this article.
统计中已有确定中位数、四分位数、十分位数等的方法，文章在此基础上提出三分位数的概念及其确定的方法。
2.Quantile regression is a basic tool for estimating conditional quantiles of a response variable Y given a vector of regressors X.
分位数回归是给定 回归变量X，估计响应变量Y条件分位数的一个基本方法。
3.This parameter controls the number of quantiles that will be collected when the WITH DISTRIBUTION option is specified on the RUNSTATS command.
该参数控制在RUNSTATS命令上指定WITHDISTRIBUTION选项时将收集的分位数(quantile)数目。
4.In this paper, the preferences on stochastic payoffs are defined by quantiles, and the Nash equilibrium of the bimatrix game with stochastic payoffs is given base on the preferences.
首先，本文将引人中位数来定义随机支苟值的偏好，并在此偏好的基础上进一步定义带随机支付双矩阵博弈的纳什均衡。
5.The Bahadur representations for this quantile estimator are established in order to derive asymptotic properties of the sequential fixed-width confidence intervals estimation for quantiles.
基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计，研究了序贯置信区间估计的渐近性质。
6.We assign a likelihood (high, medium or low; percentage points; statistical quantiles) to various events we are concerned about.
我们将一种可能性(高、中或低；百分比；统计数量)分配给我们所关心的事件之上。
7.In the end, the average, variance and quantiles of profit margin of drugstores are obtained from survey data.
最后根据调查数据，利用随机和模型给出了药店毛利百分数的均值、方差以及分位点。
8.The Bahadur representations for this quantile estimator are established in order to derive asymptotic properties of the sequential fixed-width confidence intervals estimation for quantiles.
基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计，研究了序贯置信区间估计的渐近性质。
9.The theory of quantile regression, Copula quantile regression, extremal quantiles and applications of quantile regression in many fields are discussed in this paper.
本文主要对分位数回归的理论、Copula分位数回归、极端分位数以及分位数回归在各个领域的应用进行了深入研究。
10.You can specify the number of frequent values and quantiles on the RUNSTATS command.
只需要指定RUNSTATS命令的频率值和分位数。
11.We can use quantiles (分位数) to identify outliers in a dataset.
我们可以使用quantiles（分位数）来识别数据集中的异常值。
12.When analyzing income data, we often look at the top 10% quantiles (分位数) to understand wealth distribution.
在分析收入数据时，我们通常查看前10%的quantiles（分位数）以理解财富分配。
13.The first quantile (分位数) represents the value below which 25% of the data falls.
第一个quantile（分位数）表示25%的数据低于该值。
14.The median is the second quantile (分位数) and divides the dataset into two equal halves.
中位数是第二个quantile（分位数），将数据集分成两个相等的部分。
15.In statistics, we often divide data into different quantiles (分位数) to analyze its distribution.
在统计学中，我们常常将数据划分为不同的quantiles（分位数）以分析其分布。
作文
In statistics, the concept of quantiles (分位数) plays a crucial role in data analysis and interpretation. Understanding quantiles is essential for anyone looking to make sense of large datasets, as they help summarize and describe the distribution of data points. In simple terms, quantiles are values that divide a dataset into equal-sized intervals. For instance, if you have a set of test scores from a class of students, you can use quantiles to determine how many students scored below a certain score or how many scored above it.The most common types of quantiles include quartiles, deciles, and percentiles. Quartiles divide the data into four equal parts, while deciles split it into ten, and percentiles into one hundred. This division allows us to understand the spread and central tendency of the data. For example, the first quartile (Q1) represents the 25th percentile, meaning that 25% of the data points fall below this value. Similarly, the median is the second quartile (Q2), which indicates the midpoint of the dataset.Using quantiles can also help identify outliers in data. Outliers are extreme values that differ significantly from other observations. By examining the interquartile range (IQR), which is the difference between the first and third quartiles (Q3 - Q1), we can determine whether a data point is an outlier. Any point that lies more than 1.5 times the IQR above Q3 or below Q1 is typically considered an outlier. This method is particularly useful in fields such as finance, where understanding the risk associated with investments is crucial.Moreover, quantiles are widely used in various applications, including economics, healthcare, and social sciences. In economics, for example, analysts often use quantiles to assess income distribution among different segments of the population. This analysis helps policymakers understand wealth inequality and implement measures to address it. In healthcare, quantiles can be used to evaluate patient outcomes based on treatment effectiveness, allowing for better resource allocation and improved patient care.In conclusion, mastering the concept of quantiles (分位数) is vital for effective data analysis. Whether you are a student, researcher, or professional, understanding how to calculate and interpret quantiles can provide valuable insights into the data you are working with. By breaking down complex datasets into manageable segments, quantiles enable us to make informed decisions and draw meaningful conclusions. As we continue to navigate an increasingly data-driven world, the importance of understanding statistical concepts like quantiles cannot be overstated.
在统计学中，quantiles（分位数）的概念在数据分析和解释中起着至关重要的作用。理解quantiles对于任何想要理解大型数据集的人来说都是必不可少的，因为它们有助于总结和描述数据点的分布。简单来说，quantiles是将数据集划分为相等大小区间的值。例如，如果你有一组来自班级学生的考试成绩，你可以使用quantiles来确定有多少学生的成绩低于某个分数，或者有多少学生的成绩高于这个分数。最常见的quantiles类型包括四分位数、十分位数和百分位数。四分位数将数据分成四个相等的部分，而十分位数将其分成十个，而百分位数则分成一百个。这种划分使我们能够理解数据的分散程度和集中趋势。例如，第一个四分位数（Q1）表示第25百分位数，这意味着25%的数据点低于这个值。类似地，中位数是第二个四分位数（Q2），表示数据集的中点。使用quantiles也可以帮助识别数据中的异常值。异常值是与其他观察值显著不同的极端值。通过检查四分位距（IQR），即第三四分位数和第一四分位数之间的差（Q3 - Q1），我们可以判断一个数据点是否是异常值。任何超出Q3以上或Q1以下1.5倍IQR的点通常被视为异常值。这种方法在金融等领域尤其有用，因为理解投资相关风险至关重要。此外，quantiles在经济学、医疗保健和社会科学等多个应用中被广泛使用。例如，在经济学中，分析师经常使用quantiles来评估不同人群收入分配。这种分析帮助政策制定者理解财富不平等并实施应对措施。在医疗保健中，quantiles可以用来评估基于治疗效果的患者结果，从而实现更好的资源分配和改善患者护理。总之，掌握quantiles（分位数）这一概念对于有效的数据分析至关重要。无论你是学生、研究人员还是专业人士，理解如何计算和解释quantiles都能为你正在处理的数据提供有价值的见解。通过将复杂的数据集分解为可管理的部分，quantiles使我们能够做出明智的决策并得出有意义的结论。随着我们继续在一个日益以数据驱动的世界中前行，理解统计概念如quantiles的重要性不容小觑。
