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Related Concept Videos

Correlations02:20

Correlations

36.6K
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...
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlation01:09

Correlation

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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.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Correlation and Regression00:53

Correlation and Regression

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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...
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Coefficient of Correlation01:12

Coefficient of Correlation

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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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Related Experiment Video

Updated: Feb 15, 2026

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
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Tracking moving targets behind a scattering medium via speckle correlation.

Chengfei Guo, Jietao Liu, Tengfei Wu

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    |February 6, 2018
    PubMed
    Summary

    This study introduces a novel method to track hidden moving objects through scattering media using speckle correlation. This technique enables tracking in lateral and axial directions and recognizes target rotation, benefiting biomedical applications.

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    Area of Science:

    • Optics and Photonics
    • Biomedical Imaging
    • Computer Vision

    Background:

    • Tracking objects through scattering media is difficult due to light scattering, which obscures direct imaging.
    • Speckle patterns, resulting from multiple scattering, contain hidden target information detectable through correlation analysis.

    Purpose of the Study:

    • To develop a simple yet effective method for tracking moving objects obscured by scattering media.
    • To demonstrate the capability of speckle correlation for determining an object's lateral, axial, and rotational movement.

    Main Methods:

    • Utilizing speckle correlation, inspired by computer vision and deformation detection principles.
    • Conducting simulations and experiments to validate the proposed tracking methodology.
    • Employing speckle autocorrelation to recognize the rotation state of the moving target.

    Main Results:

    • Successfully demonstrated tracking of hidden objects in both lateral and axial directions using speckle correlation.
    • Validated the ability to determine the rotation state of a moving target via speckle autocorrelation.
    • Confirmed the derivation of target information from speckle patterns passing through scattering layers.

    Conclusions:

    • Speckle correlation offers a viable approach for tracking moving objects behind scattering media.
    • This method has significant potential for biomedical applications, including micro-object dynamics analysis.
    • The technique advances the acquisition of dynamical information for obscured micro-objects.