Abstract
Multivariate statistical methods involve the simultaneous analysis of more than one outcome variable. In applied use, this definition is sometimes relaxed, but it typically includes methods such as principal component analysis, factor analysis, cluster analysis, and partial least-squares regression. Methods based on principal component analysis are frequently encountered in food science, and it is therefore compared with factor analysis, commonly used in biostatistics. The interpretation of results, especially in relation to the original variables, might be challenging, which could represent these methods’ weakest link from an applied point of view.
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Pripp, A.H. (2013). Application of Multivariate Analysis: Benefits and Pitfalls. In: Statistics in Food Science and Nutrition. SpringerBriefs in Food, Health, and Nutrition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5010-8_5
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