title: Judging the significance of multiple linear regression models.
abstract: It is common practice to calculate large numbers of molecular descriptors, apply variable selection procedures to reduce the numbers, and then construct multiple linear regression (MLR) models with biological activity. The significance of these models is judged using the usual statistical tests. Unfortunately, these tests are not appropriate under these circumstances since the MLR models suffer from "selection bias". Experiments with regression using random numbers have generated critical values (Fmax) with which to assess significance.
authors.affiliation: ChemQuest, Sandown, UK. davel@chemquest.uk.com
authors.forename: David J
authors.initials: DJ
authors.lastname: Livingstone
authors.forename: David W
authors.initials: DW
authors.lastname: Salt
mesh_terms: D000968:Antimycin A / Q000031:analogs & derivatives* / Q000737:chemistry; D016014:Linear Models; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: C032456:antimycin; D000968:Antimycin A