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相关概念视频

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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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 +...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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
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
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
Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.3K
Sample Size Calculation01:19

Sample Size Calculation

3.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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相关实验视频

Updated: May 29, 2025

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
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Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

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对与分散变化的相关计数数据的样本大小估计.

Jintong Hou1, Leslie A McClure2, Savina Jaeger3

  • 1Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA.

Pharmaceutical statistics
|February 5, 2025
PubMed
概括
此摘要是机器生成的。

这项研究提供了在临床试验中采用重复计数测量的样本大小和功率计算的新公式. 这些方法考虑了数据分散和相关性的变化,提高了各种研究设计的准确性.

关键词:
在GEEEE中,GEEEE是指GEEEE.相关联计数测量测量结果负的二项式分布.没有劣势的非劣势.样本的大小 样本大小零膨胀的分配方式是零膨胀的分配方式.

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Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

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相关实验视频

Last Updated: May 29, 2025

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
09:16

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

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Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
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科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 纵向数据分析 纵向数据分析

背景情况:

  • 临床研究通常涉及重复测量,需要专门的样本大小和功率计算.
  • 临床试验中的计数数据 (例如出血事件) 可以随着时间的推移显示变化的分散和相关性.
  • 通用估计方程 (GEE) 是分析相关计数数据的常用方法.

研究的目的:

  • 调查GEE在变化分散的计数结果中的表现.
  • 为了获得准确的样本大小和功率计算公式,在GEE下对应计数数据.
  • 为负二项分布提出修改方法,以解决I型错误通货膨胀问题.

主要方法:

  • 用GEE来估计样本大小和功率的衍生闭式公式.
  • 公式可以适应不同的分散参数和分布 (Poisson,负二项式,零膨胀变量) 在配对测量中.
  • 进行模拟以评估实证功率和I型错误率.

主要成果:

  • 开发了适用于参与者内部比较,RCT和匹配对的样本大小和功率的一般公式.
  • 公式是强大的,即使干预前和干预后的测量遵循不同的分布.
  • 经过修改的方法在控制具有显著分散变化的负二项分布的I型错误方面表现出有效性.

结论:

  • 基于GEE的推导式为纵向计数数据提供了准确的样本大小和功率估计,变化分散.
  • 拟议的修改提高了负二项式模型的可靠性,这在许多临床环境中至关重要.
  • 该研究为设计具有复杂相关计数结果的临床试验的研究人员提供了实用工具 (R函数).