A gentle introduction to principal component analysis using tea-pots, dinosaurs, and pizza
Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions based on the data. As such, PCA can be an effective tool to increase students data literacy by providing a visual and intuitive way to understand and work with data. This article outlines a teaching strategy to introduce and explain PCA using basic mathematics and statistics together with visual demonstrations.
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Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | Correlation Covariance Data analysis Data literacy Data reduction Data visualization Teaching statistics Variance, correlation, covariance, data analysis, data literacy, data reduction, data visualization, teaching statistics, variance, |
Online Access: | https://research.wur.nl/en/publications/a-gentle-introduction-to-principal-component-analysis-using-tea-p |
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