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An indicative feature of a principal component analysis (PCA) variant to the multivariate data set is the ability to transform correlated linearly dependent variables to linearly independent principal components. Back-transforming these components with the samples and variables approximated on a single calibrated plot gives rise to the PCA Biplots. In this work, the predictive property of the PCA biplot was augmented in the visualization of anthropometric measurements namely; weight (kg), height (cm), skinfold (cm), arm muscle circumference AMC (cm), mid upper arm circumference MUAC (cm) collected from the students of School of Nursing and Midwifery, Federal Medical Center (FMC), Umuahia, Nigeria. The adequacy and quality of the PCA Biplot was calculated and the predicted samples are then compared with the ordinary least square (OLS) regression predictions since both predictions makes use of an indicative minimization of the error sum of squares. The result suggests that the PCA biplot prediction merits further consideration when handling correlated multivariate data sets as its predictions with mean square error (MSE) of 0.00149 seems to be better when compared to the OLS regression predictions with MSE of 29.452.
Everitt B. Exploring multivariate data graphically: A briefly review with examples. Journal of Applied Statistics. 1994;21(3):63-92.
Gabriel KR. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 1971;453-467.
Gower JC, Hand DJ . Biplots. London,UK: Chapman & Hall; 1996.
Gower JC. Unified biplot geometry. Developments in Applied Statistics; 2003.
Greenacre M. Biplots in pratice. Madrid,Spain: BBVA Foundation; 2010.
Gower JC, Lubbe S, le Roux N. Understanding biplots. Wiley; 2010.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2018.
Revelle W. Psych: Procedures for personality and psychological research, Northwestern University, Evanston, Illinois, USA; 2016.
Available:https://CRAN.R-project.org/package=psych Version = 1.6.12.
Johnson R, Wichern D. Applied multivariate statistical analysis. Upper Sadle River: New Jersey: 5th edn. Prentice-Hall; 2002.
Pearson K. On lines and planes of closest fit to systems of points in space. Philosophicxal Magazine. 1901;2:559-572.
Hotelling H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology. 1933;24:417-441, 4498-520.
CoxT, Cox M. Multidimensional scaling. Boca Raton,FL: Chapman & Hall/CRC; 2001.
Eckart C, Young G. The approximation of one matrix by another of lower rank. Psychometrika. 1936;211-218.