In this paper, a generalized exponential-cum-exponential estimator is proposed utilizing the two auxiliary variables based on average values of the networks in adaptive cluster sampling. The exponential ratio-cumexponential ratio, exponential product-cum-exponential product, exponential ratio-cum-exponential product and exponential product-cum-exponential ratio type estimators are the special cases of proposed estimator using simple random sampling without replacement in adaptive cluster sampling. The expressions for the mean square error and bias of the proposed estimator have been derived. The class of special cases of proposed estimator may be used for estimating the finite population mean and comparable with estimators in case of high correlation but also useful when the correlation between study variable and auxiliary variables is low in the adaptive cluster sampling. The simulation studies have been carried out to demonstrate and compare the efficiencies of the estimators. It is shown that the proposed estimators are more efficient as compared to the mean per unit estimator in adaptive cluster sampling, modified ratio and modified product, exponential ratio and exponential product estimators in adaptive cluster sampling, under given conditions.
The purpose of this study is to explore the existing relationship among the quality of university efforts (teacher efforts and management efforts), the willingness of students to learn and customer satisfaction in order to ensure epistemological access to higher education in private universities of Pakistan. For this purpose, a survey was conducted with 339 students studying in three different faculties of a private university. It was aimed to collect their responses regarding their experiences at the campus about purposeful access to available resources and to measure their satisfaction level with the provided access. The data was collected through multistage sampling. It was found that there is a positive correlation among teacher efforts for epistemological access (TEEA), management efforts for epistemological access (MEEA), and customer satisfaction (CS); whereas, TEEA, MEEA, and CS are negatively correlated with student willingness (SW). It was also found that SW does not act as a mediator between UEQ and CS. The study contributes in the existing literature by accentuating the need for epistemological access by enhancing the willingness of students to learn and by providing quality university efforts to translate academic experiences into successful opportunities in the future.
Food colors are considered the most important component of foodstuff for enhancing the aesthetic appeal of the products. The rapid increase in population raised the demand for food materials, while wastewater from as-related processing industries is used for irrigation. This study was conducted to examine the genotoxicity of industrial wastewater on the plant growth-promoting rhizobacteria (PGPR). Three predominantly used synthetic food colors, including Azorubine E-122, Tartrazine E-102 and Allura Red AC E-129, were used during this project. Rhizobacteria were isolated from agricultural soils and treated with various concentrations of Azorubine E-122, Tartrazine E-102 and Allura red E-129 for a 24 and 48 h duration. DNA extraction and quantification were performed through a modified CTAB method, spectrophotometry and agarose gel electrophoresis. A comet assay was used to check DNA damage. According to the results, all the food colors had caused significant damage to DNA depending upon the concentration and exposure time. The extent of DNA damage caused by Azorubine E-122 was relatively greater compared with the other colors, so the fragmentation rate of 86% and 92% was obtained at 1.25% concentration for 24 and 48 h, respectively. The current results have revealed the devastation capacity of food colors by accelerating distortion risk to soil micro-flora, hence the fertility of the soil.
To estimate the variance for rare and cluster population has been the main problemin survey sampling. Three ratio type estimators were proposed for population variance utilizing thesingle auxiliary variable assuming the transformed population for adaptive cluster sampling, inpresented study. The expressions for the mean square error and bias of the proposed estimator werederived. The proposed estimatorswere used to estimate the finite population variance in adaptivecluster sampling. The simulationswereperformedon a real life data to reveal and evaluate the efficiencyof the estimators. The results showed that the proposed exponential ratio estimator was more efficientcompared to the usual sample variance estimator and the proposed ratio type variance estimators inadaptive cluster sampling, assuming given conditions. Hence, exponential ratio estimators wererecommended to estimate the population variance in adaptive cluster sampling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.