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相关概念视频

Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
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Updated: Jul 12, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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多组张量法定相关性分析 多组张量法定相关性分析

Zhuoping Zhou1, Boning Tong1, Davoud Ataee Tarzanagh1

  • 1University of Pennsylvania, Philadelphia, USA.

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|October 25, 2023
PubMed
概括
此摘要是机器生成的。

多组TCCA (MG-TCCA) 解决了张量分析中的数据异质性. 这种新方法改善了在阿尔茨海默氏病研究中识别特定性别的脑成像相关性.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的疾病.规范相关性分析 规范相关性分析神经成像是一种神经成像.张量分解 张量分解

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科学领域:

  • 神经科学是一个神经科学.
  • 生物统计学 生物统计学
  • 医疗成像医学成像

背景情况:

  • 传统的张量法定关联分析 (TCCA) 与异质的张量数据扎,可能导致组分析中的偏见结果.
  • 现实世界的数据,比如来自不同人群 (性别,种族) 的脑部成像,表现出现有的TCCA模型无法充分处理的异质性.
  • 这种限制阻碍了对复杂数据集的准确分析,特别是在神经退行性疾病研究中.

研究的目的:

  • 引入多组TCCA (MG-TCCA),这是一个用于在张量数据集中联合分析多个子组的新方法.
  • 解决数据异质性,并利用跨组信息来识别张量数据中的一致信号.
  • 量化共享和个体结构,减少维度,并使复杂的张量数据的视觉探索.

主要方法:

  • 开发了多组TCCA (MG-TCCA),结合了双稀疏结构.
  • 在MG-TCCA框架内使用块坐标上升算法进行高效的计算.
  • 应用MG-TCCA来分析阿尔茨海默病队列中AV-45和FDG PET成像模式之间的相关性.

主要成果:

  • 通过使多个子组的联合分析,MG-TCCA有效地解决了张量数据的异质性.
  • 该方法成功地确定了阿尔茨海默病患者的AV-45和FDG PET数据之间的性别特异性交叉模式成像相关性.
  • 与传统的TCCA相比,MG-TCCA在检测这些微妙的相关性方面表现优越.

结论:

  • MG-TCCA为分析异质张量数据提供了一个强大的方法,优于传统方法.
  • 这些发现突显了MG-TCCA在发现特定群体模式方面的实用性,例如阿尔茨海默病中性别特异的成像相关性.
  • 这种方法为表征多式成像生物标志物和理解疾病异质性提供了宝贵的见解.