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首页> 外文学位 >Something is Askew in Florida's Water: Arguing for a Better Descriptive Statistic for Positively Skewed Water-Quality Data.
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Something is Askew in Florida's Water: Arguing for a Better Descriptive Statistic for Positively Skewed Water-Quality Data.

机译：佛罗里达的水中有些歪斜：主张为正偏斜的水质数据提供更好的描述性统计数据。

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摘要

It may be of no surprise that water quality data is right-skewed, but what appears to be overlooked by some is that the arithmetic mean and standard deviation most often fail as measures of central tendency in skewed data. When using the arithmetic mean and arithmetic standard deviation with nutrient data, one standard deviation about the arithmetic mean can capture nearly all of the data and extend into negative values. Representing nutrient data this way can be misleading to viewers who are using the statistics, and making assumptions, to understand the characteristics of those waters. Through an in-depth statistical analysis of Florida's nitrogen and phosphorus data, I have found the geometric mean and multiplicative standard deviation capture a better representation of the central region of skewed data. Including the geometric mean and multiplicative standard deviation in the descriptive statistics of nutrient data is relatively simple with today's tools and helps to better describe the data. Adding these statistics can contribute to more effective understanding of nutrient concentrations, better application of data, and the development of better data-derived policy. While the suggestions of this paper are by no means original, it is with added evidence provided by the study of the skewness, distributions, and central regions of 53 nutrient data sets that I intend to help reiterate the argument that a few additional descriptive statistics can greatly empower the communication of data, and because of the ease with which they can now be calculated, there is no excuse to ignore them.
机译：水质数据右偏可能不足为奇，但似乎被某些人忽略的是，算术平均值和标准偏差最常作为偏斜数据集中趋势的度量而失败。当将算术平均值和算术标准偏差与营养数据一起使用时，关于算术平均值的一个标准偏差可以捕获几乎所有数据并扩展为负值。以这种方式表示营养数据可能会误导使用统计数据并进行假设以了解这些水质特征的观看者。通过对佛罗里达州氮和磷数据的深入统计分析，我发现几何均值和可乘标准偏差可以更好地表示偏斜数据的中心区域。使用当今的工具，将几何平均值和可乘标准差包括在营养数据的描述统计中相对简单，并且有助于更好地描述数据。添加这些统计信息可以有助于更有效地了解营养物浓度，更好地应用数据以及制定更好的基于数据的政策。尽管本文的建议绝非独创，但它是通过对53种营养素数据集的偏度，分布和中心区域的研究提供的补充证据，我打算帮助重申这一论点，即一些其他描述性统计数据可以极大地增强了数据通信的能力，并且由于现在可以轻松计算它们，因此没有任何理由忽略它们。

著录项

  • 作者

    West, Amie October.;

  • 作者单位

    University of South Florida.;

  • 授予单位 University of South Florida.;
  • 学科 Geology.;Environmental Sciences.;Hydrology.
  • 学位 M.S.
  • 年度 2012
  • 页码 131 p.
  • 总页数 131
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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