Inside of the Linear Relation between Dependent and Independent Variables

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...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2015-01, Vol.2015 (2015), p.1-11
Main Authors: Bolboacă, Sorana D., Cozma, Alina C., Pruteanu, Lavinia L., Jäntschi, Lorentz
Format: Article
Language:English
Subjects:
Algorithms
Computational Biology
Humans
Likelihood Functions
Linear Models
Models, Statistical
Quantitative Structure-Activity Relationship
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Summary: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.
ISSN:1748-670X
1748-6718
DOI:10.1155/2015/360752