Application of Principal Component Analysis in the Analysis of Nutritional Information
Abstract
The main objective of this article is to apply Principal Component Analysis (PCA) to reduce the variables associated with the quantities of 300 pizzas of ten different types of nutritional information provided by a certain entity. To achieve this, we used the SPSS software as support for data analysis and processing. Additionally, in this study, we present a comparative framework between Principal Component Analysis and Factor Analysis (FA). It is important to note that these techniques are often confused as if they were the same because the SPSS software uses the same technique for component extraction. All hypotheses formulated in this study use the rejection of the null hypothesis as a decision criterion if the p-value is less than 1% for the Mahalanobis distance and 5% for the remaining tests.
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References
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