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Statements

Subject Item
n2:RIV%2F00216275%3A25410%2F07%3A00005480%21RIV08-MSM-25410___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Využití lineárního regresního modelu při statistických analýzách není zcela universální, musí být splněna řada předpokladů.Náhodné chyby se musí řídit normálním rozložení pravděpodobností a jejich rozptyly musí být konstantní.Pokud tyto předpoklady splněny nejsou, je obtížné odvodit rozdělení pravděpodobností odhadu regresních koeficientů. Metodou, která pomáhá řešit tuto situaci, je bootstrapový resampling.Aplikace této metody je popsána v článku. The linear regression model is one of the most important methods of statistical inference having wide practical use. This method is unfortunately used in the universal way without examining its assumptions. The essential assumption is that random errors have the normal distribution and their variances are constant values. The properties of the estimates obtained in this way are well known. In case when random errors are not normally distributed or their variances are not constant values (i.e., it changes depending on values of independent variable) it is seldom possible to derive the properties of distribution of regression coefficients estimates. The asymptotical properties of these estimates based on central limit theorem are usually assigned. They correspond with reality only in cases of large random samples. In reality we have small sample sizes so that the results obtained by these methods are not reliable enough. Other approximations can be used to determine the properties of estimates in some c The linear regression model is one of the most important methods of statistical inference having wide practical use. This method is unfortunately used in the universal way without examining its assumptions. The essential assumption is that random errors have the normal distribution and their variances are constant values. The properties of the estimates obtained in this way are well known. In case when random errors are not normally distributed or their variances are not constant values (i.e., it changes depending on values of independent variable) it is seldom possible to derive the properties of distribution of regression coefficients estimates. The asymptotical properties of these estimates based on central limit theorem are usually assigned. They correspond with reality only in cases of large random samples. In reality we have small sample sizes so that the results obtained by these methods are not reliable enough. Other approximations can be used to determine the properties of estimates in some c
dcterms:title
Problém regresní analýzy a jeho nekonvenční řešení Problem of Regression Analysis and its Unconventional solution Problem of Regression Analysis and its Unconventional solution
skos:prefLabel
Problem of Regression Analysis and its Unconventional solution Problem of Regression Analysis and its Unconventional solution Problém regresní analýzy a jeho nekonvenční řešení
skos:notation
RIV/00216275:25410/07:00005480!RIV08-MSM-25410___
n3:strany
144
n3:aktivita
n9:S
n3:aktivity
S
n3:dodaniDat
n4:2008
n3:domaciTvurceVysledku
n10:4031105 n10:1657445
n3:druhVysledku
n19:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
444585
n3:idVysledku
RIV/00216275:25410/07:00005480
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Linear regression model; parameters estimates; bootstrap; resampling
n3:klicoveSlovo
n12:resampling n12:bootstrap n12:Linear%20regression%20model n12:parameters%20estimates
n3:kontrolniKodProRIV
[5D7B22EBEF47]
n3:mistoKonaniAkce
Ponta Delgada
n3:mistoVydani
Azores, Portugal
n3:nazevZdroje
ISBIS 2007
n3:obor
n16:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n4:2007
n3:tvurceVysledku
Kubanová, Jana Linda, Bohdan
n3:typAkce
n5:WRD
n3:zahajeniAkce
2007-08-18+02:00
s:numberOfPages
1
n6:hasPublisher
University of Azores
n20:isbn
978-989-95489-0-9
n14:organizacniJednotka
25410