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Correlation Coefficient (Linear) (G Dataflow)

Version:
    Last Modified: January 9, 2017

    Computes the linear correlation coefficient between two sequences.

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    x

    The first input sequence.

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    y

    The second input sequence.

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    error in

    Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.

    Default: No error

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    correlation coefficient r

    Correlation coefficient between the two input sequences.

    Understanding the Values of This Output

    correlation coefficient r always is in the interval [-1, 1]. The following table explains the meaning of different correlation coefficient r values:

    correlation coefficient r Explanation
    1

    x and y have a complete positive correlation. The data points from x and y lie on a perfectly straight, positively-sloped line.

    -1

    x and y have a complete negative correlation. The data points from x and y lie on a perfectly straight, negatively-sloped line.

    0

    x and y have no correlation.

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    r^2

    Square of the correlation coefficient.

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    error out

    Error information. The node produces this output according to standard error behavior.

    Algorithm for Computing the Linear Correlation Coefficient

    The linear correlation coefficient also is known as the product-moment coefficient of correlation or Pearson's correlation. The following equation describes the linear correlation coefficient:

    r = Σ z x z y n

    where zx and zy are the standardized z-values of x and y. The standardized z-values indicate how many standard deviations x and y are above or below the mean.

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: Not supported


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