This function can be accessed by the command Correlate. It can be found in the Analysis Menu when a table is selected. The correlation function, also known as the covariance function is used to test the similarity of two signals x(t) and y(t). It is computed by:
in which and are the mean values of the signals x(t) and y(t) respectively.
If the number of points is N, the function will be computed between -N/2 and N/2. The abscissae are therefore point numbers and nott values.
Figure 6-2. An example of a correlation between two sinus functions.
The first plot shows the two signals, the second one is the correlation function between the two signal which shows that there are correlations, and the third one is the Fourier transform which is done to extract the caracteristic frequencies of the correlation function.
The correlation of a signal with itself can also be used in spectral analysis (it is then called autocorrelation or autocovariance function).