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相关概念视频

Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
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
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
Variance01:15

Variance

9.6K
 The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the...
9.6K

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相关实验视频

Updated: Jun 22, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.5K

使用自适应集群采样,提高对复杂环境群体差异估计的精度.

Muhammad Nouman Qureshi1,2, Marwan H Ahelali3, Soofia Iftikhar4

  • 1School of Statistics, University of Minnesota, Minneapolis, USA.

Heliyon
|July 4, 2024
PubMed
概括

估计罕见,集群种群的变异性是具有挑战性的. 这项研究引入了使用自适应集群采样和辅助数据的新通用估计器,为罕见和难以到达的种群提供了更高的精度.

关键词:
适应性抽样采集方式辅助信息 辅助信息 辅助信息聚类种群的群体群体.平均平方误差 平均平方误差差异估计估计差异估计.

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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相关实验视频

Last Updated: Jun 22, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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科学领域:

  • 统计 统计 统计 统计
  • 调查方法 调查方法
  • 生态采样 生态采样

背景情况:

  • 估计罕见,集群和难以接近的种群的种群参数存在重大统计挑战.
  • 传统的采样方法往往导致过高估计的差异,无法准确地代表人口分散.
  • 适应性集群采样 (ACS) 已被公认为其在减少这些种群的差异效率.

研究的目的:

  • 在罕见,隐藏,地理聚集和难以接触的种群中引入一个通用估计器用于差异估计.
  • 在自适应集群采样框架内,利用实际和转换的辅助数据.
  • 与现有方法相比,为人口变异提供更精确的估计.

主要方法:

  • 开发一个包含辅助数据的通用差异估计器.
  • 适应性集群采样原则的应用.
  • 使用第一阶泰勒扩展推导近似偏差和平均平方误差.
  • 通过模拟研究和现实数据应用的验证.

主要成果:

  • 拟议的通用估计器有效地利用了原始和转换的辅助信息.
  • 该方法在对具有挑战性的种群类型的差异估计中显示出更高的精度.
  • 导出偏差和平均平方误差的分析表达式,证实了理论属性.

结论:

  • 新型通用估计器为罕见和集群种群的方差估计提供了更准确的方法.
  • 将辅助数据与自适应集群采样集成,提高了估计效率.
  • 这些发现对生态研究,资源管理和其他处理难以采样的人群的领域有影响.