AL-Rafidain Journal of Computer Sciences and Mathematics

Abstract

Multivariate data analysis is one of the popular techniques, and among them is the Principal Component Analysis, or PCA, is a dimensionality-reduction method which is the process of converting a large number of related variables to a smaller number of unrelated factors, that still contains most of the information in the large set. Therefore, any phenomenon that consist of a large group of variables that are difficult to treat with in their initial form. The process of the interpreting these variables become complex process, so reducing these variables to a smaller is easier to deal with which is the aspiration of every researcher working in the field of principal component analysis. In this research, a multivariate data collection process was carried out which are relates to the nature of education and the relationship between the university student and the teacher, then studying and analyzing by Principal component analysis model, which is a technique used to summarize and condense data through the use of bonding software SPSS,2020.
Thus, it will be illustrious that this research will fall into a concept Data Mining, and is also abbreviated, and then it is realized using genetic algorithms procedure, in latest version MATLAB 2019B, Application of Genetic Algorithms using simulation software with latest release MATLAB 2019, using the Multiple linear regression equation method.
Multiple linear regression procedure to find the arrangement of independent variables within each factor of the factors obtained, by calculating the weight of the independent variable (Beta). Overall results were obtained for the eigenvalues of the stored correlation matrix, and the study required a Statistical analysis (PCA) method, and by reducing the number of the variables without losing much information about the original variables. The goal is to simplify their understanding. The disclosure of its structure and interpretation, in addition to reaching a set of conclusions that were discussed in detail, In addition to important recommendation.