Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Student t Distribution01:31

Student t Distribution

5.8K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
5.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.6K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.1K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.1K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

5.1K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
5.1K
Central Limit Theorem01:14

Central Limit Theorem

14.4K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
14.4K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
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...
2.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Development of potent BChE/Nrf2 modulators for Alzheimer's disease treatment via dual suppression of ferroptosis.

European journal of medicinal chemistry·2026
Same author

High-level biosynthesis of gastrodin in engineered Escherichia coli.

Journal of biotechnology·2026
Same author

Impact of Post-exposure Prophylaxis on Pre-exposure Prophylaxis Initiation Among Men Who Have Sex with Men: A Nested Case-control Study.

AIDS and behavior·2026
Same author

Disturbance Observer-Based Model Predictive Control for Multi-Frequency Interference Suppression in Space Laser Communication Systems.

Sensors (Basel, Switzerland)·2026
Same author

Green synthesis of N-doped hierarchical porous biochar from Artemisia leaf for enhanced peroxydisulfate activation: Synergistic roles of edge nitrogen and carbonyl group.

Environmental research·2026
Same author

Low awareness of viral causes of Cancer among United States adults by smoking status: findings from health information national trends survey (2024).

Preventive medicine reports·2026

相关实验视频

Updated: May 31, 2025

Measuring Active and Passive Tameness Separately in Mice
07:13

Measuring Active and Passive Tameness Separately in Mice

Published on: August 10, 2018

7.1K

对于退化统计的自我规范化的中度偏差

Lin Ge1, Hailin Sang2, Qi-Man Shao3

  • 1Division of Arts and Sciences, Mississippi State University at Meridian, Meridian, MS 39307, USA.

Entropy (Basel, Switzerland)
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究分析了退化的U统计数据的自我正常化的中度偏差. 我们在这些统计数据的概率边界上建立了一个关键结果,并将其应用于代对数定律.

关键词:
退化的U统计数据.代对数的定律 代对数的定律中等偏差的偏差.自我正常化的自我正常化.

更多相关视频

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

913
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.8K

相关实验视频

Last Updated: May 31, 2025

Measuring Active and Passive Tameness Separately in Mice
07:13

Measuring Active and Passive Tameness Separately in Mice

Published on: August 10, 2018

7.1K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

913
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.8K

科学领域:

  • 可能性理论概率理论.
  • 统计 统计 统计 统计
  • 随机过程 随机过程

背景情况:

  • 专注于2级的退化U统计学,这是统计学理论中的一个复杂领域.
  • 检查对称的核心函数的属性,定义为函数的积的无限和.

研究的目的:

  • 为了研究退化U统计的自我正常化的中度偏差原理.
  • 在特定条件下,为这些统计数据推导出精确的概率边界.

主要方法:

  • 使用独立和相同分布 (i.i.d.) 的属性. 随机变量 随机变量 随机变量
  • 应用与核心函数的正常规律吸引领域相关的技术.
  • 在Lambda系数和截断函数属性的和上使用条件.

主要成果:

  • 确立了一个适度偏差定理,用于2级的退化U统计.
  • 导出了涉及这些统计数据的概率对数的非对称行为.
  • 作为直接应用,获得代对数定律.

结论:

  • 在研究U统计学方面提供了重要的理论进步.
  • 这些发现为复杂的统计估计器的概率行为提供了更深入的见解.
  • 代对数的衍生定律对相关过程的非对称正常性有影响.