Abstract
The functional principal components analysis combines the advantages of the standard principal components analysis and enables analysing data of a dynamic nature. The main difference in both methods is the type of data: the PCA is based on multivariate data, whereas the FPCA on the functional data including curves and trajectories, i.e. a series of individual observations, not a single observation, as usual. The purpose of the article is to apply functional principal components analysis to the problem of student’s achievements. The article was compared the level of knowledge presented by students during various stages of education in 2009-2017. The analysis covers the average exam results after the second, third and fourth stage of education. The functional principal components analysis, based on functional data, will be used in the analysis. This method allows the analysis of dynamic data
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
