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'''Discriminant function analysis''' is a statistical analysis to predict a [[categorical variable|categorical]] [[dependent variable|dependent]] [[Variable (mathematics)#Applied statistics|variable]] (called a grouping variable) by one or more [[continuous variable|continuous]] or [[Binary variable|binary]] [[independent variable|independent]] variables (called predictor variables). The original dichotomous discriminant analysis was developed by Sir Ronald Fisher in 1936.<ref name="cohen">Cohen et al. Applied Multiple Regression/Correlation Analysis for the Behavioural Sciences 3rd ed. (2003). Taylor & Francis Group.</ref> It is different from an [[ANOVA]] or [[MANOVA]], which is used to predict one (ANOVA) or multiple (MANOVA) continuous dependent variables by one or more independent categorical variables. Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership.<ref name="green">Green, S.B. Salkind, N. J. & Akey, T. M. (2008). Using SPSS for Windows and Macintosh: Analyzing and understanding data. New Jersey: Prentice Hall.</ref>
Discriminant analysis is used when groups are known a priori (unlike in [[cluster analysis]]). Each case must have a score on one or more quantitative predictor measures, and a score on a group measure.<ref name="buy">BÖKEOĞLU ÇOKLUK, Ö, & BÜYÜKÖZTÜRK, Ş. (2008). Discriminant function analysis: Concept and application. Eğitim araştırmaları dergisi, (33), 73-92.</ref> In simple terms, discriminant function analysis is classification - the act of distributing things into groups, classes or categories of the same type.
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