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This paper explains the feasibility of two-way prediction by developing direct models relating fiber to yarn and reverse models relating yarn to fiber using multivariate methods simultaneously. These models evaluate the dependencies of cotton yarn properties on fiber properties and vice versa with minimum random errors and maximum accuracy. To this end, cotton fiber properties were measured from rovings carefully untwisted. An HVI system and an evenness tester of premier were used to measure the various properties. The samples of cotton yarns (108 samples) produced yarn counts ranging from 16 to 32 Ne with optimum twist factor. In this study, effective variables were selected by multivariate statistical test ( m -test). Then, multivariate analysis of variance (MANOVA) was used for evaluating the significance of obtained models. Next, the optimal separate equations were determined through multivariate multiple regression. After solving the linear equation system, a reverse model was achieved. By selecting fiber properties and machine factors as appropriate variables, the relative importance of these factors was also investigated. The results showed that the obtained equations were significant at the significance level α = 0.01.Keywords: multivariate test ( m -test); multivariate analysis of variance (MANOVA); multivariate multiple regression; cotton spinning; quality properties of yarn; cotton fiber properties IntroductionPredicting the qualitative characteristics of yarns, such as tensile properties, unevenness, and hairiness, from the raw material properties (fiber properties) has been the main purpose of many studies in recent decades. Two main approaches used in these studies are statistical and mathematical approaches. Statistical models have relatively higher predictive power than mathematical models. One of the most common statistical approaches is the multiple regression method. So far, the statistical models for the prediction of cotton yarn properties from fiber properties have been established by

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