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

Weighted Mean00:57

Weighted Mean

5.1K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.7K
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...
7.7K
What are Estimates?01:06

What are Estimates?

5.0K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.0K
Trimmed Mean01:10

Trimmed Mean

2.9K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
2.9K
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

2.7K
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
2.7K

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

Luminescent heparin-functionalized carbon dots with potential applications in nanoparticle-protein interactions and cell imaging.

Scientific reports·2026
Same author

Marine-Derived Dual BTK-FGFR Inhibitors: Pioneering a New Era of Precision Oncology Therapeutics.

Anti-cancer agents in medicinal chemistry·2026
Same author

Deciphering the multi-organ anti-fibrotic mechanisms of pirfenidone and nintedanib via network pharmacology.

Personalized medicine·2026
Same author

Novel class of population mean estimators based on robust regression methods.

Scientific reports·2026
Same author

Unraveling the Multifunctional and Translational Paradigm of Nanoparticulate Systems against Colorectal Cancer.

ACS applied bio materials·2026
Same author

Quality-by-design-based fabrication of betaxolol hydrochloride-loaded nano-ocular carrier via ternary phase mapping of solid/liquid lipids and BBD modeling for enhanced transcorneal flux and IOP-lowering effect.

AAPS PharmSciTech·2026

相关实验视频

Updated: Jul 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

558

基于设计的合成归算方法用于域平均值.

Shashi Bhushan1, Anoop Kumar2, Rohini Pokhrel3

  • 1Department of Statistics, University of Lucknow, Lucknow, 226007, India.

Scientific reports
|February 20, 2024
PubMed
概括

本研究介绍了基于设计的合成归算方法,以改善缺少数据时的小面积估计 (SAE). 这些方法提高了样本调查中域平均估计的准确性.

关键词:
效率 效率是指效率是指效率.计入计算是指计入计算的方法.缺失的价值是错失的值.小面积估计 小面积估计

更多相关视频

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K

相关实验视频

Last Updated: Jul 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

558
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K

科学领域:

  • 统计 统计 统计 统计
  • 调查方法 调查方法
  • 计量经济学 计量经济学 计量经济学

背景情况:

  • 小面积估计 (SAE) 在数据不足以进行域特定估计时至关重要.
  • 缺少的数据显著影响调查准确性,特别是在SAE.

研究的目的:

  • 提出基于设计的合成归算方法,用于SAE下的域平均估计.
  • 为了应对SAE框架内缺少数据的挑战.

主要方法:

  • 开发用于简单随机抽样的合成归算技术.
  • 拟议方法的平均平方误差 (MSE) 表达式的导数 (第一阶近似).
  • 计算方法的效率条件的确定.

主要成果:

  • 提出的方法提供了一种可行的方法来处理SAE中缺少的数据.
  • 使用人工数据的模拟研究表明了归算技术的有效性.
  • 现实世界的数据应用验证了拟议方法的实际实用性.

结论:

  • 开发的归算方法在缺少数据的情况下提高了小面积估计的准确性.
  • 该研究为解决SAE中缺少数据的挑战做出了基础性贡献.
  • 这些发现得到了模拟和真实数据分析的支持,证实了实际适用性.