000			02906nam a22003253a 4500
001			UPMIN-00005727664
003			UPMIN
005			20221122153857.0
008			221122b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG 993.5 2010 _bA64 A44
100			_aAlegado, Marilou C.
245		2	_aA new approach in relating two data sets : _ban application to the study of interspecies relationship / _cMarilou C. Alegado.
260			_c2010
300			_a120 leaves.
500			_aCollege of Science and Mathematics
502			_aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2010
520	3		_aThe study of relationships between two data sets is a common concern among researchers. Numerous methods used to address this concern depending on the applicability that lies on the introduced techniques. However, data sets are generally multidimensional. An ordination technique like Principal Components Analysis (PCA) is the oldest and best known ordination technique used in summarizing and reducing the dimensionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. Synthetic variables obtained from data reduction are called principal components (PCs). The researcher used these PCs as new set of values in determining the linear relationships between two data sets through Pearson Correlation Analysis. furthermore, two sets of Permutation test were conducted to assess the significance of the detected relationships. The test constructed the reference distributions of the test statistic under the null hypothesis (correlations between two data sets are not significant). The new approach was then applied to particular data sets in ecology. The researcher extracted two PCs for each data set and obtained correlation coefficients among component pairs. In the case where the 'true' (observed) value in the distribution with the computed statistics through 99 random permutations was found inside H acceptance region, it was concluded that the obtained coefficient was not significant. Results of the analyses showed that two of the detected relationships were not significant hence were drawn only by chance. However, other coefficients were also assessed and found to be significant. Significant linear relationships seemed to follow patterns on co-occurrence while the others did not. The new approach offers new ways of relating two data sets.
650	1	7	_aPearson correlation coefficient
650	1	7	_aPermutation test.
650	1	7	_aPCA (Principal Component Analysis)
650	1	7	_aData sets.
650	1	7	_aPrincipal components.
650	1	7	_aPearson Correleation Analysis.
658			_aUndergraduate Thesis _cAMAT200, _2BSAM
905			_aFI
905			_aUP
942			_2lcc _cTHESIS
999			_c2552 _d2552