首页> 外文期刊>Indian Journal of Animal Research >Application of multiple regression analysis to morphometric characters obtained from Serranus cabrilla (linnaeus, 1758) by using stepwise method
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Application of multiple regression analysis to morphometric characters obtained from Serranus cabrilla (linnaeus, 1758) by using stepwise method

机译:使用逐步法测量分析对从Serranus Cabrilla(Linnaeus,1758)获得的形态学字符的应用

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摘要

In fisheries science, high number of morphometric measures (independent variables) taken from different parts of the fish complicates the estimation of the body weight (dependent variable). Therefore, the researchers are seeking for a solution facilitating the interpretation of the equations of correlation between the characteristics. One way to deal with this challenge is the dimension reduction by means of stepwise multiple regression analysis. The aim of this study is to explain total variation with the same accuracy by using fewer independent variables. To accomplish this, 12 morphometric measures from 210 individuals of Serranus cabrilla were measured to estimate the body weight. Firstly, the 95% of the variation was explained by means of multiple regression analysis by using all variables. Then, by step-wise method, the same results were achieved with fewer independent variables. Finally, the variables with inter-multicollinearity eliminated and with two remaining independent variables determination coefficients resulted as 95%. The result showed that using more variables does not create significant distinction for accuracy to estimate the body weight although; the total length and body dept was the most effective features for weight.
机译:在渔业科学中,从鱼类的不同部分取出的大量形态学措施(独立变量)使体重(依赖变量)的估计复杂化。因此,研究人员正在寻求一种解决方案,促进了对特征之间的相关性的释义的解释。处理这一挑战的一种方法是通过逐步多元回归分析减少维度。本研究的目的是通过使用更少的独立变量来解释具有相同精度的总变化。为实现这一点,测量了来自210个曲折的Serranus Cabrilla的形态测量措施,以估计体重。首先,通过使用所有变量来解释95%的变型通过多元回归分析来解释。然后,通过逐步方法,通过较少的独立变量实现相同的结果。最后,消除了具有间多晶间性的变量,并且具有两个剩余的独立变量确定系数导致95%。结果表明,使用更多变量不会产生显着的区别,以准确估计体重估计体重;总长度和身体部门是重量最有效的特征。

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