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相关概念视频

Types of Skewness01:09

Types of Skewness

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If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
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Skewness01:06

Skewness

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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
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Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis01:24

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis

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Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
135
Normal Distribution01:11

Normal Distribution

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Applications of Normal Distribution01:22

Applications of Normal Distribution

4.9K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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多变量倾斜正常分布用于建模倾斜的空间数据.

Kassahun Abere Ayalew1, Samuel Manda2, Bo Cai3

  • 1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.

Spatial and spatio-temporal epidemiology
|November 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多变量倾斜正常空间模型,以更好地分析复杂的空间数据,在预测南非艾滋病毒率方面表现优于标准模型.

关键词:
在MICAR-正常的情况下.多变量是多变量的.斯克夫-正常分布空间模型的空间模型.空间随机效应的空间随机效应

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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科学领域:

  • 空间统计的空间统计.
  • 统计建模 统计建模
  • 生物统计学 生物统计学

背景情况:

  • 多变量空间数据通常使用共享空间组件和多变量内在条件自回归 (MICAR) 模型.
  • 这些模型通常假设正常分布的空间随机变量,这可能并不总是正确的.

研究的目的:

  • 引入一种新的多变量斜正态空间分布,用于建模多变量条件自回归模型.
  • 解决现有空间模型中正常性假设的局限性.

主要方法:

  • 开发了一个多变量斜正态空间模型.
  • 采用贝叶斯推理来进行参数估计.
  • 利用模拟和现实应用 (南非艾滋病毒感染率) 进行验证.
  • 将拟议模型与使用条件预测坐标 (CPO) 的标准MICAR模型进行比较.

主要成果:

  • 拟议的多变量倾斜空间模型展示了改进的预测能力.
  • 对于模拟和艾滋病毒数据,CPO值表明新模型的性能优于MICAR模型.
  • 该模型有效地处理非正常的空间结构.

结论:

  • 多变量倾斜正常空间模型为分析具有潜在非正常组件的多变量空间数据提供了更灵活,更准确的方法.
  • 这种方法提高了流行病学研究的预测准确性,例如估计艾滋病毒感染率.