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All Journal Journal of Statistics and Data Science
Alvionita, Renny
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Partitioned Design Matrix Method for Two Factors Multivariate Design Alvionita, Renny; Nugroho, Sigit; Chozin, Mohammad
Journal of Statistics and Data Science Vol. 1 No. 1 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i1.21010

Abstract

Factorial experiment often involves large data sets and the use of generalized inverse for the data analysis. It becomes less manageable as the data increased. The objective of this study is to evaluate the accuracy of partitioned design matrix method for two factors multivariate design. The design matrix is partitioned into several sub-matrices based on their source of variation. The partitioned design matrix method in two factors multivariate is much simpler than usual sigma summation method in calculating the sum of product matrix and the degrees of freedom. This method could also be used in explaining the derivation of the statistics for testing the hypothesis of the equality of the means which corresponds to the source of variation.
Co-Authors Mohammad Chozin, Mohammad Sigit Nugroho