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

Correlation of Experimental Data01:23

Correlation of Experimental Data

459
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
459
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.5K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.5K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.0K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.0K
Correlations02:20

Correlations

35.7K
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...
35.7K
Correlation and Regression00:53

Correlation and Regression

3.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.0K
Coefficient of Correlation01:12

Coefficient of Correlation

8.1K
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...
8.1K

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

Lactic acid bacteria and endogenous ethanol mediate proton pump inhibitor-associated MASLD: a multicohort cross-sectional mediation analysis.

Gut microbes·2026
Same author

Tumor Whole-Genome Sequencing for Prediction of Venous Thromboembolism in Patients With Metastasized Solid Cancer.

Circulation. Genomic and precision medicine·2026
Same author

False Discovery Estimation in Record Linkage.

Statistics in medicine·2025
Same author

Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.

Neuro-oncology advances·2025
Same author

Alternatives to default shrinkage methods can improve prediction accuracy, calibration, and coverage: A methods comparison study.

Statistical methods in medical research·2025
Same author

Author Correction: Digital consults in heart failure care: a randomized controlled trial.

Nature medicine·2024

相关实验视频

Updated: Jan 6, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

多次测量与潜伏轨迹的稀疏正规相关性分析.

Nuria Senar1, Aeilko H Zwinderman1, Michel H Hof1

  • 1Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, Amsterdam, The Netherlands.

Biometrical journal. Biometrische Zeitschrift
|October 30, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的稀疏法定相关性分析 (CCA) 方法,用于分析高维欧米数据中的重复测量. 这种新的方法有效地建模了时间动态,提供了可解释的纵向轨迹,并减少了计算时间.

关键词:
准则的相关性分析.缩小尺寸缩小尺寸的方法高维数据是指高维数据.重复测量,重复的测量.

更多相关视频

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.4K

相关实验视频

Last Updated: Jan 6, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.4K

科学领域:

  • 多变量统计学 多变量统计学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 规范相关性分析 (CCA) 通过识别观察到的特征之间的相关性来整合高维的奥米克数据集.
  • 标准CCA需要独立的观察,限制其使用重复或纵向测量.
  • 现有的CCA扩展用于重复测量对于高维数据和纵向分析是不理想的.

研究的目的:

  • 开发一种稀疏CCA的新型扩展,其中包含时间动态,用于分析高维纵向数据.
  • 为了解决标准CCA在处理相关重复测量的局限性.
  • 为了提高可解释性和计算效率的CCA在奥米克研究.

主要方法:

  • 提出了一种新的稀疏CCA扩展,使用纵向模型在潜变量水平上结合时间动态.
  • 针对固定的稀疏度级别实施了$\ell _0$的罚款,提高了可解释性和计算效率.
  • 通过将模型配合低维潜变量来估计纵向轨迹,利用集群数据结构.

主要成果:

  • 新的CCA方法有效地处理重复测量,并将时间动态纳入高维数据集.
  • 该方法提供了可解释的纵向轨迹,揭示了共享的潜在机制.
  • 与高维分析的现有方法相比,显著减少了计算时间.

结论:

  • 拟议的CCA方法提供了一个高效和可解释的解决方案,用于分析高维的纵向奥米克数据,并进行重复测量.
  • 这种方法可以估计对集群数据的测量中的正规相关性,捕捉时间动态.
  • 这种方法适用于稀疏和不规则地观察到的数据,正如人类微生物组项目的数据所示.