Normality Assessment of Several Quantitative Data Transformation Procedures

Dago Dougba Noel, Kablan Gnoan Aka Justin, Alui Konan Alphonse, Lallié Hermann Désiré, Dagnogo Dramane, Diarrassouba Nafan and Giovanni Malerba
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Kablan Gnoan Aka Justin: Department of Biochemistry and Genetic, Peleforo Gon Coulibaly University BP 1328 Korhogo, Cote d’Ivoire
Alui Konan Alphonse: Department of Geosciences, Peleforo Gon Coulibaly University BP 1328 Korhogo, Cote d’Ivoire
Giovanni Malerba: Department of Neurological Biomedical and Movement Sciences, University of Verona, Italyly

Biostatistics and Biometrics Open Access Journal, 2021, vol. 10, issue 3, 51-65

Abstract: Usually, quantitative data standardization and/or normalization procedures requested in biological and as well in biomedical data analysis with the purpose to infer about linear regression relationship between processed variables and/or conditions. Here, we embarked to understand performance of quantitative data transformation systems in terms of reducing data variability as well as assessing data distribution normality by a computational statistic approach. For this purpose, we performed several multivariate descriptive and analytical statistical tests. Even if results shown drastic reduction of data variability by applying presently data transformation procedures, it is noteworthy to underline the relative opposite attitude of Exponential (Expo) data standardization system in that sense. In addition although, results revealed variance homogeneity for data processed by both Maximum and Logarithm data transformation methods, it is noteworthy to underline a relative variance homogeneity with regard data submitted to Box-Cox, Z-score, Minimum-Maximum and Square Root data transformation methods. Further, findings exhibited high aptitude of Square Root, Box-Cox and Logarithm quantitative data standardization methods, in stabilizing processed data variability. Interestingly, results shown high performances of Logarithm and Box-Cox data standardization systems in term of adjusting data normal distribution. In addition, multiple comparison of mean by Turkey contrast test suggested the high performance in term of data normality with regard Box-Cox standardization method. In conclusion, even if our results revealed heterogenic performances of presently processed quantitative data transformation methods, it is noteworthy to underline the high performances of both Box-Cox and Logarithm methods, in adjusting and reducing data normality and variability respectively, allowing improving data aptitude for subjacent linear regression analysis.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:10:y:2021:i:3:p:51-65

DOI: 10.19080/BBOAJ.2021.10.555786

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