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相关概念视频

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.9K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
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).
7.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

8.1K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
8.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

507
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
507
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

920
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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相关实验视频

Updated: Jan 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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最大的顺序概率比率测试回归试验.

Ivair R Silva1,2, Joselito Montalban3, Fernando L P de Oliveira4

  • 1Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.

Biometrics
|December 29, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的顺序回归测试,用于监测药物和疫苗的安全性. 该方法考虑了混变量,提高了在市场后监测中检测不良事件的准确性.

关键词:
不良事件不良事件不良事件疫苗上市后的疫苗监测季节性的季节性.顺序回归是一种顺序回归.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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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: Jan 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
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

Published on: July 3, 2020

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科学领域:

  • 药物监督 药物监督 药物监督
  • 生物统计学 生物统计学
  • 流行病学 流行病学

背景情况:

  • 推销后对药物和疫苗安全性的连续监测至关重要.
  • 像MaxSPRT和CMaxSPRT这样的现有方法可能无法完全考虑对不良事件风险的共变效应.
  • 性别和年龄等混变量可以影响事件的质量和风险.

研究的目的:

  • 为分析安全数据引入一种新的顺序回归测试.
  • 在MaxSPRT和CMaxSPRT框架内容纳可观测的共变量.
  • 通过对混因素进行调整,提高在市场后监测中检测不良事件的准确性.

主要方法:

  • 为二项式和波桑数据开发一个序列回归测试.
  • 将回归结构应用于MaxSPRT和CMaxSPRT.
  • 历史和监测Poisson数据与异质基线率的比较.
  • 包括季节性和其他可观测的混共变量.

主要成果:

  • 拟议的顺序回归试验有效地纳入了共变量调整.
  • 该方法适用于MaxSPRT和CMaxSPRT,提高了它们的效用.
  • 使用真实世界的数据显示了监测疫苗不良事件的潜力.

结论:

  • 序列回归试验为药监测提供了更强大的方法.
  • 调整混变量导致更可靠的安全信号检测.
  • 该方法在公共卫生监测中具有实际应用,以曼尼托巴省的疫苗安全监测为例.