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A Technique for the Analysis of Unbalanced Data
Author(s) -
Singe Kuan P.,
Singh Umed
Publication year - 1989
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710310102
Subject(s) - regression analysis , proper linear model , regression diagnostic , linear regression , computer science , salient , regression , linear model , econometrics , confusion , statistics , interpretation (philosophy) , mathematics , data mining , machine learning , artificial intelligence , polynomial regression , psychology , psychoanalysis , programming language
The regression methods with dummy variables have been shown to be effective in preventing confusion in the analysis of linear models. In particular, this model simplifies interpretation of parameters and clarifies hypothesis statements. All existing methods have been shown as special cases of the general linear hypothesis in regression setting. Three regression on dummy variables methods are examined critically to bring out the salient features of each method. The choice of a method should be based on the way definitions of the parameters are desired. The linear models are considered in a regression model setting. This has been done by defining appropriate dummy variables in a regression model which often is desirable, if not mandatory, when dealing with unbalanced data involving two or more factors.