Principal component analysis summarizes high dimensional data into a few dimensions. Each dimension is called a principal component and represents a linear combination of the variables. The first ...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical option to classical least-squares linear model building. We consider the specific problem of generalized ...
A set of desert vegetation-environment data consisting of 22 concrete communities in Southern Sind was analyzed with two multivariate methods, viz. canonical correlation analysis (CCA) and principal ...
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