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master's thesis
Metode procjene i odabira linearnog regresijskog modela
Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:842312
Mucak, Josip
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Mucak, J. (2021). Metode procjene i odabira linearnog regresijskog modela (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:842312

Mucak, Josip. "Metode procjene i odabira linearnog regresijskog modela." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:842312

Mucak, Josip. "Metode procjene i odabira linearnog regresijskog modela." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:842312

Mucak, J. (2021). 'Metode procjene i odabira linearnog regresijskog modela', Master's thesis, University of Zagreb, Faculty of Science, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:842312

Mucak J. Metode procjene i odabira linearnog regresijskog modela [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:217:842312

J. Mucak, "Metode procjene i odabira linearnog regresijskog modela", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:842312

Metadata
TitleMetode procjene i odabira linearnog regresijskog modela
Title (english)Methods for estimating and selecting a linear regression model
AuthorJosip Mucak
MentorMiljenko Huzak (mentor)
Committee memberMiljenko Huzak (predsjednik povjerenstva)
Committee memberZoran Vondraček (član povjerenstva)
Committee memberEduard Marušić-Paloka (član povjerenstva)
Committee memberPavle Goldstein (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2021-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovome radu upoznali smo se sa principom i svrhom korištenja linearne regresije u različitim formama. Vidjeli smo kako funkcioniraju neke od osnovnih metoda za pronalazak koeficijenata (i mnogih drugih statistika) linearnog regresijskog modela, odnosno kako ponekad možemo izmijeniti (metode sažimanja) prvobitno dobivene koeficijente ili neke prediktore u potpunosti izbaciti (metode odabira podskupa) u svrhu poboljšanja snage predikcije. Zaključujemo da bez obzira na prednost neke metode s obzirom na druge u određenom problemu, ne postoji savršeni model koji bi u svakome problemu predikcije davao optimalno rješenje, već moramo svaki puta uzeti u obzir više metoda kako bismo pronašli model koji nam u tom trenutku najviše odgovara gledajući kriterij koji nas najviše zanima.
Abstract (english)
In this paper, we are introduced to the principle and purpose of using linear regression in different forms. We have seen how some of the basic methods for finding the coefficients (and many other statistics) of a linear regression model work, that is, how we can sometimes modify (shrinkage methods) originally obtained coefficients or eliminate some predictors completely (subset selection methods) to improve prediction power. We conclude that regardless of the advantage of some methods over others in a certain problem, there is no perfect model that would predict the optimal solution in each problem, instead each time we must consider several methods to find the model that suits us best at that time by looking at the criterion that interests us the most.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:842312
Study programmeTitle: Mathematical Statistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-10-29 12:29:53