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.

Singular value decomposition (SVD) of rectangular datasets has proved to be a useful multivariate data decomposition approach because of its ability to decompose both square and rectangular matrices. Choi & Huh (1996) SVD approach utilizes the median as a robust location estimate other than the mean estimate used in the ordinary SVD. Since insufficient dataset and in addition, inefficiency of the median estimate seems to be a major setback of the existing robust SVD systems, this study envisaged an integrated algorithm that incorporated the BootSVD of Fisher (2016) and the sample Myriad estimate in cropping a new SVD system, the Robust Bootstrapped SVD (RobBootSVD). The new RobBootSVD was appraised alongside the existing ones using the Principal Component Analysis biplot quality measures and the new RobBootSVD T2 measure. Applications on tobacco process datasets with both short and long runs and simulated datasets with various percentages of outliers showcases the viability of the new approach. JEL Classification: C11, C15, C19, C55

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Tobacco manufacturers see the tobacco moisture content as one of the determining factors in the quality of the finished tobacco product. During primary processing stage, the Particle Size Distribution (PSD) of the cut tobacco is a good measure of the tobacco moisture content. This paper presents statistical analyses of a two month PSD data using graphical techniques from noteworthy statistical multidimensional scaling (MDS) approaches in characterizing the tobacco moisture quality ratio. At the end, the evaluation within the investigated months fosters an indicative process audit, control and predictive monitoring that is capable of providing valuable impacts to future production.