Usporedba analize glavnih komponenti i faktorske analize

master's thesis

• Author: Krunoslav Rajčić
• Mentor: Anamarija Jazbec (mentor)
• Committee member: Anamarija Jazbec (predsjednik povjerenstva)
• Committee member: Miljenko Marušić (član povjerenstva)
• Committee member: Pavle Goldstein (član povjerenstva)
• Committee member: Ljiljana Arambašić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2015-09-25, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

Ove se tehnike obično koriste za analizu skupine koreliranih varijabli. Analiza glavnih komponenti i faktorska analiza koristite se za svođenje brojnih varijabli na nekoliko faktora. Razlika između ova dva pristupa ima veze s varijancom koja se analizira. Kod PCA, analiziraju se sve promatrane varijance, dok se faktorskom analizom analizira samo zajednička varijanca.

Abstract (english)

These techniques are typically used to analyze groups of correlated variables. Principal components analysis and factor are used for reducing the number of variables on several factors. The difference between these two approaches has to do with the variance that is analyzed. In PCA, all of the observed variance is analyzed, while in factor analysis it is only the shared variances that is analyzed.

• Keywords: analiza glavnih komponenti; faktorska analiza; analiza varijance
• Keywords (english): principal components analysis; factor analysis; variance analysis
• Language: croatian
• URN:NBN: urn:nbn:hr:217:174751
• Study programme: Title: Mathematical Statistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-02-05 11:33:01