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Statements

Subject Item
n2:RIV%2F60460709%3A41110%2F13%3A61432%21RIV14-MSM-41110___
rdf:type
skos:Concept n12:Vysledek
dcterms:description
The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the cluste The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the cluste
dcterms:title
Hierarchical Cluster Analysis – Various Approaches to Data Preparation Hierarchical Cluster Analysis – Various Approaches to Data Preparation
skos:prefLabel
Hierarchical Cluster Analysis – Various Approaches to Data Preparation Hierarchical Cluster Analysis – Various Approaches to Data Preparation
skos:notation
RIV/60460709:41110/13:61432!RIV14-MSM-41110___
n12:predkladatel
n13:orjk%3A41110
n3:aktivita
n18:S
n3:aktivity
S
n3:cisloPeriodika
3
n3:dodaniDat
n11:2014
n3:domaciTvurceVysledku
n16:4359879 n16:8677786
n3:druhVysledku
n4:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
77309
n3:idVysledku
RIV/60460709:41110/13:61432
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Hierarchical clustering, PCA, correlation, Pseudo t2, Pseudo F Statistic, e-communication, Internet satisfaction
n3:klicoveSlovo
n9:Internet%20satisfaction n9:e-communication n9:PCA n9:Pseudo%20F%20Statistic n9:Pseudo%20t2 n9:Hierarchical%20clustering n9:correlation
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[C5F657E997E1]
n3:nazevZdroje
AGRIS on-line Papers in Economics and Informatics
n3:obor
n5:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n11:2013
n3:svazekPeriodika
V
n3:tvurceVysledku
Poláčková, Julie Pacáková, Zuzana
n3:wos
0
s:issn
1804-1930
s:numberOfPages
11
n6:organizacniJednotka
41110