A distance based regression model for prediction with mixed data

A multiple regression method based on distance analysis and metric scaling is proposed and studied. This method allow us to predict a continuous response variable from several explanatory variables, is compatible with the general linear model and is found to be useful when the predictor variables are both continuous and categorical. Real data examples are given to illustrate the results obtained. 1 Introduction Many authors have considered the problem in regression or multivariate analysis of having both qualitative and quantitative variables. Some procedures have been used in regression and association (Young et al. 1976; Daudin 1980; Roskam 1980; Lauritzen and Wermuth 1989), principal components (Young et al. 1978; Kiers 1989a , 1989b) and discriminant analysis Krzanowski (1975, 1986; Knoke 1982). The methodologies are mainly based on optimal scaling, generalized correlation coefficients, the location model and distance-based analysis. Although statistical analysis on mixed data is ...