养蜂中成对和多重相关和回归模型的创建和使用前景

O. Galatiuk, A. Lakhman, T. Romanishina, V. Behas
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引用次数: 1

摘要

在包括养蜂在内的畜牧业中,有越来越多的独立咨询服务机构分析该行业在疾病监测状况和预防措施方面的表现,以保持蜜蜂家庭的适当健康。为了提供专家建议,这些服务必须始终以高质量的数据和准确的统计分析为后盾。它将对如何解释处理时获得的结果给出明确的说明,并显示改善疾病预防的方向。目前,由于各种自然和人为因素对养蜂业的经济效益有多向的影响,因此在改善蜜蜂传染病控制方面存在一些问题。人们还关注传染性动物和昆虫疾病的控制,这是由自然和人为因素引起的一系列多方面的原因,这些因素对养蜂的经济效益有多媒介影响。因此,通过构建两两和多元依赖关系及其统计解释,将不同类型的相关和回归分析在该行业的实验应用是本文的目的。所研究的相关回归模型包含四组特征:结果变量(y) - 20个不同蜂房在一个季节的蜂蜜量;因子变量:x1 -蜂房的空气温度;x2 -添加一定量的益生菌“肠诺胺碘+硒”,以刺激免疫系统作为预防方法之一;X3 -每个蜂房的蜂箱数。回归模型中各因子与蜂房生产力呈线性比例关系。相关回归分析结果显示,蜂房温度与产蜜呈中等连接关系(r1 = 0.666),每架益生菌用量与产蜜呈紧密(密切)连接关系(r2 = 0.813),蜂箱数与产蜜呈中等连接关系(r3 = 0.633)。回归系数显示了在等式中其他因素不变的情况下,当每个因素变化一个时,蜂房的蜂蜜产量是如何变化的。因此,温度每升高1℃,每个蜂房的蜂蜜产量就会增加216公斤,而“肠诺明碘+硒”的浓度每增加1立方厘米,每个蜂房的蜂蜜产量就会增加1.12公斤。多重决定系数(R2 = 0,954163)表明所建立的模型中存在密切关系(95%的调查因素决定了蜂箱性能)。因此,在养蜂业中,采用线性和多元相关及回归分析的形式进行建模是可行的。关键词:养蜂,建模,系统分析,因素和结果特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prospects for the creation and use of paired and multiple correlation and regression models in beekeeping
In animal husbandry, including beekeeping, there are a growing number of independent consultancy services to analyse the performance of the industry in relation to disease monitoring status and preventive measures to maintain proper bee family health. In order to provide expert advice, these services must always be backed up by quality data and accurate statistical analysis. It would give clear instructions on how to interpret the results obtained when processing them, and show directions for improving disease prevention. Currently, there are problems related to improving the control of infectious diseases in bees, as various natural and anthropogenic factors have a multidirectional effect on the economic performance of beekeeping. There are also concerns about the control of infectious animal and insect diseases, which is a multifaceted series of causes due to natural and anthropogenic factors that have a polyvector effect on the economic performance of beekeeping. Therefore, the experimental application of different types of correlation and regression analysis in this industry by constructing pairwise and multivariate dependencies and their statistical interpretation was the aim of the paper. The correlation and regression model under study contains four sets of characteristics: result variable (y) - the amount of honey from 20 different apiaries in one season and factor variables: x1 - air temperature in the apiaries; x2 - amount of probiotic "Enteronormin Iodis + Se" to stimulate the immune system as one of the preventive methods; x3 - number of beehives in each apiary. Linear proportional relationships between apiary productivity and the factors included in the regression model are obtained. According to the results of the correlation-regression analysis, paired correlation coefficients showed that the relationship between air temperature in the apiary and produced honey is medium connection (r1 = 0,666), the relationship between the amount of probiotic applied per frame and produced honey is tight (close) connection (r2 = 0,813), the relationship between the number of beehives and produced honey is medium connection (r3 = 0,633). The regression coefficients show how the amount of honey produced in an apiary changes when each factor changes by one, with the other factors in the equation fixed. So, raising the temperature by 1 °C increases the honey production by 216 kg in each apiary, while increasing the concentration of "Enteronormin Iodis + Se" by 1 cm3 per beehive frame increases the nectar production by 1,12 kg for one hive. The coefficient of multiple determination (R2 = 0,954163) identifies a close relationship in the model created (95% of the factors investigated determine apiary performance). Therefore, modelling in the form of linear and multiple correlation and regression analysis is feasible in beekeeping. Key words: beekeeping, modeling, system analysis, factor and result characteristics.
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