Journal of Statistics and Data Science
Vol. 1 No. 1 (2022)

Partitioned Design Matrix Method for Two Factors Multivariate Design

Alvionita, Renny (Unknown)
Nugroho, Sigit (Unknown)
Chozin, Mohammad (Unknown)

Article Info

Publish Date
15 Mar 2022

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.

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Journal Info
Journal of Statistics and Data Science
Website

Abbrev

jsds

Publisher

Universitas Bengkulu

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

Description

Established in 2022, Journal of Statistics and Data Science (JSDS) publishes scientific papers in the fields of statistics, data science, and its applications. Published papers should be research-based papers on the following topics: experimental design and analysis, survey methods and analysis, ...