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Inside of the Linear Relation between Dependent and Independent Variables

机译:内部因变量和自变量之间的线性关系

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摘要

Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors.
机译:简单和多重线性回归分析是用于研究活性化合物的活性/性质与结构化学特征之间关系的统计方法。线性回归的一种假设是误差遵循正态分布。本文介绍了一种解决简单线性回归的新方法,该方法不对误差分布进行任何假设。所提出的方法根据随机误差最大化了观察事件的可能性。十种具有不同活性或特性的化合物说明了该方法的使用。所提出的方法被证明是可靠的,并且与误差的正态分布的简便方法相比,显示出适合于观测数据的正确性。

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