Statistical design of experimental methodology based on Taguchi orthogonal design has been used to study and optimize various parameters using multifactor analysis of variance (ANOVA).
Methods include linear regression, inference, model assumption evaluation, the likelihood approach, matrix formulation, generalized linear models, single-factor and multifactor analysis of variance (ANOVA), and a brief foray into nonlinear modeling.
It deals with the factorial experiments that are carried out within blocks, an analog of the multifactor analysis of variance (ANOVA), and classifies repeated measures designs by the number of between-subject and within-subject factors.
Statistical design of experimental (DoE) methodology based on Taguchi orthogonal design has been used to study and optimize compositional and process parameters using multifactor analysis of variance (ANOVA) analysis method and the adhesion strength of coatings to the substrate as per pull off test has been used as a response.
Shakya, M; Gottel, N; Castro, H; Yang, ZK; Gunter, L; Labbé, J; Muchero, W; Bonito, G; Vilgalys, R Tuskann, G; al, E, A Multifactor Analysis of Fungal and Bacterial Community Structure in the Root Microbiome of Mature Populus deltoides Trees, Plos One, vol. 8 no. 10 (2013), pp. e76382 [doi] [abs].
The indicators at different time points were analyzed by multifactor analysis of variance (ANOVA).
In this paper, we separately address the advantages and disadvantages of multifactor analysis and discriminant analysis and propose Multifactor Discriminant Analysis (MDA) by synthesizing both methods.
Section 3 first addresses the advantages and disadvantages of multifactor analysis and discriminant analysis individually, and then Section 4 proposes MDA with the combined virtues of both methods.
Multifactor analysis indicated significant effects of all individual parameters (time, treatment and tissues) on actin and big defensin expression and a significant effect of combined parameters on AP-1, lysozyme and TLR expression (Table 4).
Multifactor analysis combing the PCA plots of the two growth stages (R = 0.577) highlighted that Zebrina, Pisang Jari Buaya, Safet Velchi, Calcutta 4 and Mbwazirume showed the most difference between juvenile and pre-flowering stage causing the low correlation between the growth stages.
This seems to be because Multifactor Discriminant Analysis offers the combined virtues of both multifactor analysis methods and discriminant analysis methods.
