title: Regression analysis and multivariate analysis.
abstract: Proper evaluation of data does not necessarily require the use of advanced statistical methods; however, such advanced tools offer the researcher the freedom to evaluate more complex hypotheses. This overview of regression analysis and multivariate statistics describes general concepts. Basic definitions and conventions are reviewed. The types of regression analysis are then discussed, including simple regression, multiple regression, multivariate multiple regression, and logistic regression. The various steps required to perform these analyses are described, and the advantages and disadvantages of each is detailed.
authors.affiliation: Department of Obstetrics & Gynaecology, Yale University School of Medicine, New Haven, Connecticut 06520-8063, USA.
authors.forename: A J
authors.initials: AJ
authors.lastname: Duleba
authors.forename: D L
authors.initials: DL
authors.lastname: Olive
mesh_terms: D000704:Analysis of Variance; D002986:Clinical Trials as Topic; D016002:Discriminant Analysis; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis