Identification of raw material characteristics towards yarn count variation was studied by using statistical techniques. Regression analysis is used to meet the objective. Stepwise regression is used for model selection, and coefficient of determination and mean squared error (MSE) criteria are used to identify the contributing factors of cotton properties for yarn count. Statistical assumptions of normality, autocorrelation and multicollinearity are evaluated by using probability plot, Durbin

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... atson test, variance inflation factor (VIF), and then model fitting is carried out. It is found that, invisible (INV), nepness (Nep), grayness (RD), cotton trash (TR) and uniformity index (UI) are the main contributing cotton properties for yarn count variation. The results are also verified by Pareto chart.