A new approach in relating two data sets : (Record no. 2552)

MARC details
000 -LEADER
fixed length control field 02906nam a22003253a 4500
001 - CONTROL NUMBER
control field UPMIN-00005727664
003 - CONTROL NUMBER IDENTIFIER
control field UPMIN
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221122153857.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221122b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Transcribing agency UPMin
Modifying agency upmin
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
090 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) LG 993.5 2010
Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) A64 A44
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Alegado, Marilou C.
245 #2 - TITLE STATEMENT
Title A new approach in relating two data sets :
Remainder of title an application to the study of interspecies relationship /
Statement of responsibility, etc. Marilou C. Alegado.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2010
300 ## - PHYSICAL DESCRIPTION
Extent 120 leaves.
500 ## - GENERAL NOTE
General note College of Science and Mathematics
502 ## - DISSERTATION NOTE
Dissertation note Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2010
520 3# - SUMMARY, ETC.
Summary, etc. The 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 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Pearson correlation coefficient
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Permutation test.
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element PCA (Principal Component Analysis)
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data sets.
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Principal components.
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Pearson Correleation Analysis.
658 ## - INDEX TERM--CURRICULUM OBJECTIVE
Main curriculum objective Undergraduate Thesis
Curriculum code AMAT200,
Source of term or code BSAM
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a FI
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a UP
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Status Collection Home library Current library Shelving location Date acquired Source of acquisition Accession Number Total Checkouts Full call number Barcode Date last seen Koha item type
    Library of Congress Classification   Not For Loan Preservation Copy University Library University Library Archives and Records 2014-03-06 donation UAR-T-gd1965   LG 993.5 2010 A64 A44 3UPML00033546 2022-10-05 Thesis
    Library of Congress Classification   Not For Loan Room-Use Only College of Science and Mathematics University Library General Reference 2011-06-21 donation CSM-T-gd2747   LG 993.5 2010 A64 A44 3UPML00012785 2022-10-05 Thesis
 
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