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The introduction of a simple regression method for multivariate data presents a useful alternative to prior regression methods, which focus on individual samples rather than all samples of one trait. The main contribution of this paper is that it provides a method for avoiding mathematical underpinnings of multivariate regression, by simultaneously checking the reproducibility of the multiple regression and of the likelihood of covariates. Metabolomic data analysis is an equally useful method for assessing the abundance of foods, such as nuts and seeds or millet, for humans especially that its description is very reliable. Metabolomics is one of the central field of pharmacology for research into drugs and pharmaceuticales. It helps us to evaluate a hypothesis about the basis rather than a true substance in terms of a drug or a drug component, and also obviates the need to take into account the effects of other factors such as duration of growth and tissue maturity. Metabolomic data collection is part of population genetics research and community genetic research needs for validating the use of the metabolomics data for general and individual analytical and bioinformatics applications. In the study of lipidomics, Metabolomic data is the most efficient one in the field. We started our work research with the focus on the metabolomics results for *Aedes aegypti*. With this in mind, we compiled the results of our metabolomics work with a detailed description of the metabolomics data preparation process. Data acquisition To carry out the data analysis, a hybrid development described by Dixit P et al., 2008. The hybrid plan calls for the fusion of two processes of data generation: extraction and classification, and the development of parameters for extracting features. During the extraction, a dimensionless grid of features, $\Delta x$
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