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Related Concept Videos

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...
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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.
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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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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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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.
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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Enhancing ambient noise correlation processing using vector sensors.

Brendan Nichols1, James Martin1, Christopher Verlinden2

  • 1School of Mechanical Engineering, Georgia Institute of Technology, 771 Ferst Drive North West, Atlanta, Georgia 30332-0405, USA.

The Journal of the Acoustical Society of America
|July 1, 2019
PubMed
Summary
This summary is machine-generated.

Vector sensors improve ambient noise correlation by using directionality to reject noise, enhancing Green's function estimation over standard hydrophones. This reduces required averaging time, especially in dynamic environments.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Geophysics

Background:

  • Ambient noise cross-correlations estimate the Green's function between sensors.
  • Vector sensors offer directional capabilities beyond omnidirectional hydrophones.

Purpose of the Study:

  • Quantify performance gains of vector sensors over hydrophones for Green's function estimation.
  • Analyze the impact of various factors on correlation improvement.

Main Methods:

  • Derived time-domain analytical expressions for vector sensor component correlations.
  • Examined correlations with varying bandwidth, sensor separation, and noise levels.
  • Conducted experiments with drifting vector sensors in Long Island Sound.

Main Results:

  • Theoretical analysis and experimental results show modest gains for velocity channels over pressure channels.
  • Measured variance reduction in experiments aligned with theoretical predictions.
  • Vector sensors effectively reject irrelevant ambient noise sources.

Conclusions:

  • Vector sensors provide improved Green's function estimation through noise rejection.
  • The findings suggest reduced averaging times for noise correlation processing.
  • Vector sensors are particularly beneficial in fluctuating or mobile sensor scenarios.