Correlation and convolution (function or number)

In summary, correlation is a relationship between random variables and can be represented as a number or a function depending on the context. A correlation between two random variables is a number, but if they are part of a stochastic process, it becomes a function. A convolution is a type of integration process and can result in a correlation if the involved functions are related.
  • #1
pinsky
96
0
Hy.

I have a problem about correlation depending whether it it observed as a measurement of linear fit of statistical data, and when observed as a relationship between two continuous functions.

Is a result of correlation a coefficient (Pearson's product-moment coefficient) or a function?
If the correlation is a number, what information does a autocorrelation correlogram represent?

And if correlation is a number, why is convolution a function then?

tnx
 
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  • #2
A correlation is a relationship between a pair of random variables.

If these are stand alone, it is a number.

If the random variables are elements of a stochastic process, the the correlation will be a function of the parameters of the stochastic process.

If the random variables are terms in the same stochastic process, then the correlation is an auto-correlation function.

A convolution is a particular kind of integration process. The result may be a correlation, depending on what functions are involved.
 

FAQ: Correlation and convolution (function or number)

What is correlation?

Correlation is a statistical measure that describes the degree of relationship between two variables. It determines how closely the variables are related to each other and whether there is a positive, negative, or no relationship between them.

What is convolution?

Convolution is a mathematical operation that combines two functions to produce a third function. It is used to describe the relationship between two signals, such as a stimulus and a response, or two sound waves. In signal processing, convolution is used to filter and extract useful information from a signal.

What is the difference between correlation and convolution?

Correlation and convolution are two different mathematical operations that are used to describe the relationship between two variables or signals. Correlation measures the degree of relationship between two variables, whereas convolution combines two functions to produce a third function.

How is correlation and convolution used in scientific research?

Correlation and convolution are important tools in scientific research, particularly in fields such as statistics, signal processing, and neuroscience. They are used to analyze data and determine the relationship between different variables or signals. They can also be used to extract useful information and patterns from complex data sets.

What are some real-life examples of correlation and convolution?

Correlation and convolution can be seen in many real-life scenarios, such as analyzing the relationship between smoking and lung cancer, or studying the response of neurons to a specific stimulus. In image processing, convolution is used to blur, sharpen, or enhance images. In finance, correlation is used to determine the relationship between different stocks in a portfolio.

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