Durbin watson: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Criterion for measure of continuity. | Criterion for measure of continuity. | ||
===Synopsis=== | ===Synopsis=== | ||
:y = durbin_watson(x) | :y = durbin_watson(x) | ||
===Description=== | ===Description=== | ||
The | |||
The Durbin Watson criteria for the columns of <tt>x</tt> are calculated as the ratio of the sum of the first derivative of a vector to the sum of the vector itself. Low values means correlation in variables, high values indicates randomness. Input <tt>x</tt> is a column vector or array in which each column represents a vector of interest. Output <tt>y</tt> is a scalar or vector of Durbin Watson measures. | |||
====Inputs==== | |||
* '''x''': column vector or array where each column represents the vector of interest | |||
====Outputs==== | |||
* '''y''': scalar or vector of Durbin Watson measures | |||
===See Also=== | ===See Also=== | ||
[[coda_dw]] | [[coda_dw]] | ||
Latest revision as of 17:45, 8 October 2008
Purpose
Criterion for measure of continuity.
Synopsis
- y = durbin_watson(x)
Description
The Durbin Watson criteria for the columns of x are calculated as the ratio of the sum of the first derivative of a vector to the sum of the vector itself. Low values means correlation in variables, high values indicates randomness. Input x is a column vector or array in which each column represents a vector of interest. Output y is a scalar or vector of Durbin Watson measures.
Inputs
- x: column vector or array where each column represents the vector of interest
Outputs
- y: scalar or vector of Durbin Watson measures