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Inside of the Linear Relation between Dependent and Independent Variables.

ABSTRACT: 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.

SUBMITTER: Jantschi L

PROVIDER: S-EPMC4458545 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Inside of the Linear Relation between Dependent and Independent Variables.

Jäntschi Lorentz L Pruteanu Lavinia L LL Cozma Alina C AC Bolboacă Sorana D SD

Computational and mathematical methods in medicine 20150525

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 accordin ...[more]

PMID: 26101543

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