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- W1818158567 abstract "In this chapter, discriminant analysis (DA) is introduced as a tool to identify the most important variables that can predict child nutrition among a large pool of variables. The method is useful for predicting membership in naturally occurring groups and thus addresses whether a combination of variables (such as women's status, household income, and other community variables) can be used to predict group membership (such as child nutritional status). The method automatically determines some optimal combination of variables so the function can provide the overall discrimination between groups. An important advantage of this method is that the functions will be independent or orthogonal, so that their contribution to the discrimination between groups does not overlap. Women's status in the society has been one of the socioeconomic variables that have gained recognition as a good predictor of child nutritional status. This chapter provides a conceptual framework for briefly studying whether women's status is important for predicting child nutritional status. It also reviews some studies that attempt to identify good predictors of child nutrition status. In addition, there is a discussion of issues, data sets, and analytical methods, and results of these studies serve as a motivation for understanding the use and application of discriminant analysis. Last, this chapter provides concluding remarks and some implications of the results." @default.
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- W1818158567 date "2009-01-01" @default.
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- W1818158567 title "Predicting child nutritional status using related socioeconomic variables – application of discriminant function analysis" @default.
- W1818158567 doi "https://doi.org/10.1016/b978-0-12-374712-9.00011-0" @default.
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