Background & Aim: Questionnaires are used mostly as a tool in medical research. Due to the different varieties of questionnaires, we may face different score distributions. In many cases multiple linear regression assumptions are violated. Beta-binomial regression model has the high flexibility and compatibility with this situation. In previous studies there were no comparison between beta-binomial accuracy and other models to fitting quality of life data. So in this study, our aim is to compare the accuracy of models to prediction. Methods & Materials: In this cross-sectional study we collected the quality of life data from 511 healthy women in Qazvin, Iran. The data were used to compare accuracy of betabinomial model and with some other models. Since beta-binomial considers the discrete response variable, so it should be compared with other similar models which are mostly used such as Multinomial, dirichlet-Multinomial and ordinal regression models. The main method that we used in our study was cross-validation to determine the accuracy of different models. To compare the different aspects, vast variety of situations were made and considered. Results: Regarding to the accuracy of models that were obtained by cross-validation in different situations, beta-binomial model had better accuracy among all models. Conclusion: According to the results, we have concluded that beta-binomial model is more accurate in prediction and fitting to the quality of data than the other models. The main advantages of this model are its simplicity, more efficacy and accuracy than the similar models.

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary, Multinomial or ordinal variables. In this paper, profiles with Multinomial response are studied. For this purpose, Multinomial log it regression (MLR) is considered as the basis. Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.

Organic agriculture is one of the sustainable farming systems that emphasizes economic, social, and environmental aspects to achieve sustainability targets. This study aims to identify the economic, social, and environmental factors affecting organic product production from the perspective of experts and 150 farmers in Jiroft County. The results revealed that low government financial support for organic farmers (average rating of 3.8133) is the most effective factor, while lack of adequate marketability (average rating of 2.6733) is the least effective factor among the barriers to achieving organic agriculture. from the farmers' perspective. According to experts, organic farming serves as a tool for improving public health conditions, (average rank of 11.66), which it is determined as the most effective factor. However, it does not significantly contribute to self-sufficiency in agricultural production, with an average rank of 6.07, being the least influential factor among the factors affecting organic farming. Among the average ranks of the components of influential factors in organic farming (educational, economic, social-managerial indicators), the lowest ranking is the government's financial support for organic farmers (average rank of 3.13), making it the most influential. Whereas, the lack of suitable marketability with an average rank of 2.67 is the least influential factor among the factors affecting organic farming. Furthermore, the results show that holding promotional and educational classes has a positive and significant impact on the acceptance of organic cultivation, as following the recommendations of promoters and having the necessary education can increase production and reduce costs.