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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

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, and...
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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...
Calculating and Interpreting the Linear Correlation Coefficient01:11

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Correlation and Regression00:53

Correlation and Regression

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 negative...
Coefficient of Correlation01:12

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Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Missing Data Methods for Partial Correlations.

Gina M D'Angelo1, Jingqin Luo, Chengjie Xiong

  • 1Division of Biostatistics, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, USA.

Journal of Biometrics & Biostatistics
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an improved method for analyzing brain imaging data in dementia research, particularly when data is missing. The expectation-maximization (EM) algorithm offers a more accurate approach than traditional methods for understanding relationships between brain measures.

Keywords:
Alzheimer’s diseaseExpectation-maximization algorithmFisher-z transformationMissing at randomMissing dataPartial correlation

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Analyzing relationships between regional brain measures is crucial in dementia research.
  • Partial correlations are commonly used but can be unreliable with missing data.
  • Complete case analysis, a standard method for missing data, yields biased results when data is missing at random.

Purpose of the Study:

  • To extend the partial correlation coefficient for handling missing data.
  • To compare the performance of the expectation-maximization (EM) algorithm with multiple imputation and complete case analysis.
  • To apply these methods to regional imaging data from an Alzheimer's disease study.

Main Methods:

  • The expectation-maximization (EM) algorithm was extended to calculate partial correlations with missing data.
  • Simulation studies were conducted to compare the EM algorithm, multiple imputation, and complete case analysis.
  • The methods were illustrated using neuroimaging data from Alzheimer's disease patients.

Main Results:

  • The EM algorithm demonstrated superior performance in handling missing data for partial correlations.
  • Multiple imputation showed comparable performance to the EM algorithm.
  • Complete case analysis yielded biased and inefficient results.

Conclusions:

  • The extended partial correlation coefficient using the EM algorithm is a robust method for analyzing neuroimaging data with missing values in dementia research.
  • This approach provides more accurate and reliable insights into relationships among regional brain measures.
  • The findings have significant implications for Alzheimer's disease research and other conditions involving missing neuroimaging data.