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35 with the first ordination axis. 937. 35. Hence, the Eigenvectors are a useful tool for interpreting the final ordination. 4 the variances of the attributes are also shown. 8. According to the definitions in PCA, the highest variance in the ordination is attributed to the first axis. 95 and is the first Eigenvalue in the Eigenanalysis. Because variance on any axis is a linear combination of the variance of many species, it generally exceeds the variance of any individual species. However, the total variance remains unchanged as the point pattern as a whole is not affected by PCA, and only its projection is adjusted.

This conforms with the basic concept of variance (the variance within one vector) and covariance (the variance shared by two vectors). 3. 3 Product moments. Types differ in the mode of implicit data transformation. Name Formula Transformation Scalar product Sj k = p h=1 Ahj Ahk Ahj = Xhj Centred scalar product Sj k = p h=1 Ahj Ahk Ahj = Xhj − X h Covariance Sj k = p h=1 Ahj Ahk √ Ahj = (Xhj − X h )/ n − 1 Correlation Sj k = p h=1 Ahj Ahk Ahj = (Xhj −Xh ) 1/2 n e=1 Xhe −X h The scalar product is the vector product with no further transformation involved.

We measure what we can measure and we omit what we cannot. Sometimes we also have a choice in the method we use to obtain some particular information, as for example in measuring the colour of light. ) or measure the wavelength of electromagnetic radiation. In the first case the measurement addresses a type of colour, in the second we get a number, representing a totally different data type. We need to distinguish different data types as their numerical analyses require different treatments. 1 An example of three data types.

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