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相关概念视频

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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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,...
11.7K
Skewness01:06

Skewness

11.2K
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...
11.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.4K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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相关实验视频

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How to Create and Use Binocular Rivalry
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使用替代性马模型对右倾数据的更好的性能.

Peter Veazie1,2, Orna Intrator3,4, Bruce Kinosian5,6

  • 1Canandaigua Veterans Affairs Medical Center, 400 Fort Hill Ave., Canandaigua, New York, 14424, USA. peter_veazie@urmc.rochester.edu.

BMC medical research methodology
|December 15, 2023
PubMed
概括

一个新的马分布模型,其中变量与平均值成比例,为经济学和医疗保健中的右倾数据提供了改进的估计. 这种替代规范减少了偏差,并提高了与标准模型相比的预测准确性.

关键词:
玛分布是指玛分布的一般化的线性模型最大的概率估计估计.右倾斜的变量是右倾斜的

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相关实验视频

Last Updated: Jul 8, 2025

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14:34

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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科学领域:

  • 计量经济学 计量经济学
  • 生物统计学 生物统计学
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 对于玛分布的标准最大概率估计器 (MLE) 假设差异与平均值的平方成比例.
  • 这种假设在对右倾经济和医疗保健数据 (如成本和等待时间) 的建模中很普遍.
  • 提出了一个替代的玛规范,其中变量与平均值成正比.

研究的目的:

  • 为了评估与标准模型相比,替代性玛规范的性能.
  • 用模拟来比较参数偏差,标准误差和分布偏差.
  • 通过使用现实世界医疗保健成本数据来评估模型适合性和预测准确性.

主要方法:

  • 进行模拟实验,以调查两种玛规格的有限样本特性.
  • 利用美国退伍军人事务部 (VA) 的医疗保健成本数据进行实证比较.
  • 基于R平方,根平均平方误差和平均余值的评估模型.

主要成果:

  • 模拟表明,与标准模型相比,替代性玛规范产生了较少的参数偏差,较低的标准误差和较少的斜率.
  • 对VA医疗保健成本的实证分析表明,对于替代规范来说,模型适合性优越 (R-squared更高) 和预测性性能 (RMSE更低,残留量更小).
  • 替代模型显示,对右倾连续变量来说,估计准确度更高.

结论:

  • 替代性马规范为经济学和医疗保健中的右倾连续变量建模提供了有价值的工具.
  • 这种方法在估计准确性和预测能力方面比传统的马模型具有优势.
  • 研究人员应该考虑这种替代规范,以改善对偏斜数据的分析.