This study examines the relationship between food waste and gender, income, and where students live. The research aligns with the United Nations Sustainable Development Goals 2, 11, and 12; moreover, the massive global food shortage in 2022 due to the conflict between Russia and Ukraine makes saving food a priority. A questionnaire was used to collect data from 201 students at the United Arab Emirates University (UAEU). A large proportion of the respondents were female (71.6%). The most common reasons cited by the respondents for food waste were over purchasing (31% of respondents), attitude (26.5%), and poor management (24%), while the most common reasons for having extra-cooked food were expecting guests (46%) and wanting to eat the food that had been prepared (35%). The majority of the respondents (57%) agreed that young people waste more food than older people. The methodology used in this study could be adopted by other researchers around the globe, and the output may help in developing policies and designing educational material for food waste intervention programs. Beneficiaries may therefore include food producers/consumers, environmental departments, and charitable organizations. The research contributes to the knowledge about food waste, perception, and intervention programs.

In this paper, we focused on developing copula-based modeling procedures that effectively capture the dependence between response and explanatory variables. Building upon the work of Noh et al. (J. Am. Stat. Assoc. 2013, 108, 676–688) we extended copula-based regression to accommodate both continuous and discrete covariates. Specifically, we explored the construction of copulas to estimate the conditional mean of the response variable given the covariates, elucidating the relationship between copula structures and marginal distributions. We considered various estimation methods for copulas and distribution functions, presenting a diverse array of estimators for the conditional mean function. These estimators range from non-parametric to semi-parametric and fully parametric, offering flexibility in modeling regression relationships. An adapted algorithm is applied to construct copulas and simulations are carried out to replicate datasets, estimate prediction model parameters, and compare with the OLS method. The practicality and efficacy of our proposed methodologies, grounded in the principles of copula-based regression, are substantiated through methodical simulation studies.