Principal Component Analysis – Online Statistical Analysis Tool
O. P. Sheoran1, Vinay Kumar2, Hemant Poonia3, Komal Malik4
1O.P. Sheoran, Professor, Department of Mathematics and Statistics, CCS Haryana Agricultural University, Hisar.
2Vinay Kumar, Assistant Scientist, 3Department of Mathematics and Statistics, CCS Haryana Agricultural University, Hisar.
3Hemanat Poonia, Assistant Professor, 3Department of Mathematics and Statistics, CCS Haryana Agricultural University, Hisar.
4Komal Malik, Assistant Professor, Economics,,Govt.College, NalwaHisar.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3050-3054 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6014029320/2020©BEIESP | DOI: 10.35940/ijeat.C6014.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An online module to deal with PCA has been developed in ASP scripting language based on Server-Client Architecture. The module produces descriptive statistics via subprogram Descriptive Stats, computes eigenvalues and eigenvector using Mx Eigen Jacobisub-program, order eigenvector through Mx Eigsrtsub-program and finally produces eigenvalues, eigenvectors, output loadings and components scores through Output Eigenval, Output Loadings, Output Scoressub-programs. A user friendly interface has been developed for entering or pasting the data, entering various parameters such as number of variables, number of observations and selection of covariance/correlation matrix. A complete procedure for how to perform principal component has also been provided in help file.
Keywords: Principal Component, Eigenvalues. Eigenvectors, Component Scores, Loadings.