Principal component analysis is a statistical method, which lowers the number of important variables in a data set. The use of this method for the bursts spectra and afterglows is discussed in this paper. The analysis indicates that three principal components are enough among the eight ones to describe the variablity of the data. The correlation between spectral index alpha and the redshift suggests that the thermal emission component becomes more dominant at larger redshifts.
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