One of the most efficient statistical tools for modeling the relationship between a dependent variable and several independent variables is regression‎. ‎In practice‎, ‎observations relating to one or more variables‎, ‎or the relationship between variables‎, ‎may be vague or non-specific‎. ‎In such cases‎, ‎classic regression methods will not have enough capability to model data‎, ‎and one of the alternative methods is regression in a fuzzy environment‎. ‎The fuzzy logistic regression model provides a framework in the fuzzy environment to investigate the relationship between a binary response variable and a set of covariates‎. ‎The purpose of this paper is to attempt to develop a fuzzy model that is based on the idea of the possibility of success‎. ‎These possibilities are characterized {by several} linguistic phrases‎, ‎including low‎, ‎medium‎, ‎and high‎, ‎among others‎. ‎Next‎, ‎we {use a set of precise explanatory variable observations to model the logarithm transformation of‎ "‎possibilistic odds.‎" ‎We assume that the model's parameters are triangular fuzzy numbers.} We use the least squares method in fuzzy linear regression to estimate the parameters of the provided model‎. ‎We compute three types of goodness-of-fit criteria to evaluate the model‎. ‎Ultimately‎, ‎we model suspected cases of Systemic Lupus Erythematosus (SLE) disease based on significant risk factors to identify the model's application‎. ‎We do this due to the widespread use of logistic regression in clinical studies and the prevalence of ambiguous observations in clinical diagnosis‎. ‎Furthermore‎, ‎to assess the prevalence of diabetes in the community‎, ‎we will collect a sample of plasma glucose levels‎, ‎measured two hours after a meal‎, ‎from each participant in a clinical survey‎. ‎The proposed model has the potential to rationally replace an ordinary model in modeling the clinically ambiguous condition‎, ‎according to the findings‎.

In macroeconomics, aggregate consumption assumes a pivotal role as a fundamental component of total demand. It directly influences production and employment while indirectly contributing to overall economic growth through savings and investment. Insights gleaned from consumption function estimations and the study of the marginal propensity to consume (MPC) inform economic policy decisions. Economists have diligently explored the intricate relationship between consumption behavior and influential factors.The primary objective of the research you mentioned is to examine the nexus between consumption, income, and wealth during the period spanning from the first quarter of 1369 to the third quarter of 1401, utilizing quantile regression techniques. By analyzing a diverse set of comprehensive models grounded in macroeconomic principles, the study aims to identify an optimal model for Iran's economy. The empirical results reveal that income and wealth have a positive and statistically significant impact on consumption, with consistent effects observed across different quantiles. Furthermore, the Brownian habit persistence model aligns well with Iran's economic dynamics, implying that government interventions related to pricing and taxation can effectively shape community consumption patterns and contribute to overall economic growth

Given the growing role of home-based businesses in the country's economic and social dynamics, accurately identifying the factors influencing the development of this sector is of paramount importance. This research aims to identify key factors affecting the growth of home-based businesses to facilitate policy-making in this area and maximize the potential of home-based businesses in the country. This study employs a mixed-methods approach (qualitative and quantitative) with an exploratory and applied research design. To this end, 44 interviews were conducted with policymakers, executive officials in the home-based business sector, university professors, and entrepreneurship researchers, as well as entrepreneurs and pioneers in the home-based business field. The data obtained from the interviews were coded and thematically analyzed using MAXQDA 2023 software. After coding the data and identifying key categories and subcategories, the factors were ranked using the method of interaction analysis. The results showed that the capacity of development and support institutions, the role of unions and associations, networking, clustering and sustainability, education, a supportive-developmental approach, positive discrimination, financial facilities and facilitation, virtual facilities and facilitation, and physical-spatial facilities and facilitation are the key factors in the home-based business system in Iran. It is expected that these findings, which have led to the identification of challenges and key points related to home-based businesses in the country, will result in the provision of practical solutions to improve productivity, enhance working conditions, develop more effective policies, and ultimately increase the share of home-based businesses in the national economy

In this study, two pairs of OEM brake disc-pads have been used. One of these discs belongs to a passenger car, and the other one belongs to a light commercial vehicle. The disc-pad pair of the passenger car (PC) has been subjected to global brake effectiveness test by full scale inertia dynamometer according to SAE J2522 test standard; and the other one has been subjected to the tests by full-scale inertia dynamometer according to FIAT 7-H4020 and 7-H2000 standards. During these tests, 13 variables for passenger car disc-pad pair and 11 variables for light commercial vehicle disc-pad pair have been measured and recorded. Interrelation of the parameters has been analyzed with multiple regression method and importance levels have been determined. In this study, dependent variables in multiple regression method are selected as braking time, friction coefficient, disc final temperature, brake speed and brake pressure for each braking pair. In multiple regression analysis for PC, for each unit increase in deceleration and friction coefficient, braking time decreases with 7. 3 and 60. 9 units, respectively. Also, for each unit increase in brake pressure and friction coefficient for LCV, braking time increases with 1. 267 and 91. 887 units, respectively.

Potential evapotranspiration (ET0) rates are needed for irrigation scheduling. ET0 rates are commonly from weather parameters. The Penman-Monteith, is now accepted for computation of ET0. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, the suitability of fuzzy regression was examined for estimating daily potential evapotranspiration with grass reference crop and compared with Artificial Neural Networks (ANN) and Penman-Monteith methods. The daily climatic data of the Ekbatan station in Hamadan, including maximum and minimum temperature, maximum and minimum relative humidity, wind speed and sunny hours are introduced as input data and ET0 as output data. ET0 values estimated from the fuzzy regression method were compared with direct ET0 measurements from lysimeters, and with ET0 estimations obtained using the Penman-Manteith equation and the ANN method. The estimated ET0 values from a fuzzy regression model using five input parameters, including maximum and minimum temperature, mean relative humidity, wind speed and sunny hours were obtained with RMSE=0.69 mm/day, R2=0.88. The estimated ET0 values from an artificial neural networks model using the same input parameters were obtained with RMSE=0.74 mm/day, R2=0.84. The estimated ET0 values from Penman-Monteith model were obtained with RMSE=1.21 mm/day, R2=0.84. Thus, in this study the fuzzy regression is the best method.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.