Correlative Coherence Analysis: Setup

About

Correlative Coherence Analysis is a procedure designed to tease out stochastic noise from a chaotic signal. It is based on a Shannon-Weaver information measure and ranges from 0 for uncorrelated systems to 1 for perfectly correlated or anti-correlated systems. Applications for CCA include analysing population ecology, assessing to what degree a sector of the stock market is driven by particular economic indices, determining whether a set of neurons responds to selected sensory stimuli, and much, much more.

CCA was developed by Wayne Getz at UC Berkeley. A journal article discussing its details and uses can be read here.

Step 1: Enter Your Data

Data type: Lists Correlation Matrix

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Upload an Excel or text file:
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Manually enter data:
Uploaded data will take precedence over manually entered data. Use the Data Import Wizard to trim headings and format your data correctly. [How do I format my data?]

Step 2: Set Options

Carry out jackknifing. [What is this?]
Apply prune-fitting. [What is this?]
Employ temporal, correlation maximisation. Maximum K=. [What is this?]
Checking these will result in more useful data, but can significantly increase processing time.

Step 3: Analyze

Please check over your data and then click...