Augmented Latin Square Designs for Imprecise Data

Abstract

This paper addresses a novel approach for analyzing augmented Latin square design with
uncertain observations, the so-called neutrosophic augmented Latin square design (NALSD). The
contribution of our work lies in estimating the effects of rows, columns, control and new treatments, as
well as formulating their sums of squares. Moreover, by determining the neutrosophic hypotheses and
decision rule, the ð¹ð‘-statistic in NANOVA table is given. The performance of the proposed design is
evaluated using a numerical example and simulation study. In light of the results observed, it can find that
the NALSD performs better than the classic augmented Latin square design (ALSD) in the presence of
uncertainty.