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10.1155/2015/360752 http://scihub22266oqcxt.onion/10.1155/2015/360752 C4458545!4458545!26101543     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26101543|t=2015. Inside of the Linear Relation between Dependent and Independent Variables |pdf=|usr=}}{{26101543}} gab.com Text Inside of the Linear Relation between Dependent and Independent Variables JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26101543 #FOAvip 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. Twit Text FOAVip Inside of the Linear Relation between Dependent and Independent Variables ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26101543 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26101543 #FOAvip English Wikipedia <ref>{{Cite pmid|26101543}}</ref> Inside of the Linear Relation between Dependent and Independent Variables #MMPMID26101543 Jäntschi L; Pruteanu LL; Cozma AC; Bolboac? SD Comput Math Methods Med 2015[]; 2015 (ä): ä PMID26101543show ga 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. äInside of the Linear Relation between Dependent and Independent Variab(epmc).pdf DeepDyve Pubget Overpricing