Basic statistical analysis was conducted to inform the multivariate analysis selection process.
For this reason, it is necessary to apply an appropriate multivariate analysis, able to process very high-dimensional data, to pull out the significant and non-redundant information contained in the spectra [ 28- 30].
Multivariate analysis of the process indicator domain showed that all these variables remained independently associated with outcome.
For Townsend score and distance alike, the difference between the actual mean in the injured and the mean of randomly imputed values in those not injured is approximately correct to adjust for in the subsequent multivariate analysis, and the process incorporates the appropriate degree of variation at individual level.
This work is thus an example of quality by design (QbD) principles where a systematic mechanistic understanding combined with multivariate analysis is integral to process development.
Data were processed by multivariate analysis using the PLS toolbox and MatLab (MATLAB and Statistics Toolbox Release 2012b, The MathWorks, Inc., Natick, MA).
We used multivariate analysis to optimize the process of organic removal from wastewater in a cylindrical reactor, using UV radiation (254 nm) and peroxodisulfate as an oxidant.
In this paper, using a statistical methodological approach based on DoE technique and multivariate analysis of flame treatment process parameters, a mathematical model of SFE and wettability is obtained.
Test data were processed by the multivariate analysis of variance (MANOVA) method and the least significant difference (Fisher's LSD) method as a post hoc test to justify the statistical significance of the experimental data.
In order to justify the statistical significance of the results, the data of subjective listening tests are processed by the multivariate analysis of variance (MANOVA) [17] method, followed by the least significant difference method (Fisher's LSD) as a post hoc test.
