"1"^^ . "Z(MSM0021627502)" . . "CZ - \u010Cesk\u00E1 republika" . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza povodn\u00ED na \u0159ece S\u00E1zav\u011B v obdob\u00ED 1961 - 2000" . "RIV/00216275:25310/07:00006183" . . . . . . "Meloun, Milan" . "Vodn\u00ED hospod\u00E1\u0159stv\u00ED" . . "419-423" . "457885" . . "5"^^ . . . "Multivariate Statistical Analysis of Floods on the River S\u00E1zava within 1961 - 2000"@en . "57" . "RIV/00216275:25310/07:00006183!RIV08-MSM-25310___" . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza je zalo\u017Eena na latentn\u00EDch prom\u011Bnn\u00FDch, kter\u00E9 jsou line\u00E1rn\u00ED kombinac\u00ED p\u016Fvodn\u00EDch. Zdrojov\u00E1 matice dat obsahuje kvalitativn\u00ED znaky v 13 sloupc\u00EDch a sledovan\u00E9 povodn\u011B jako objekty v 25 \u0159\u00E1dc\u00EDch zdrojov\u00E9 matice dat. C\u00EDlem anal\u00FDzy dat je nal\u00E9zt shluk podobn\u00FDch si povodn\u00ED a podobn\u00FDch znak\u016F, je\u017E povodn\u011B popisuj\u00ED. Podobnost povodn\u00ED je posuzov\u00E1na na z\u00E1klad\u011B jist\u00FDch podobnost\u00ED \u010Di vzd\u00E1lenosti povodn\u00ED v 13-rozm\u011Brn\u00E9m prostoru v\u0161ech znak\u016F dle krit\u00E9ria, \u017Ee \u010D\u00EDm je Mahalonobisova vzd\u00E1lenost shluk\u016F povodn\u00ED v\u011Bt\u0161\u00ED, t\u00EDm men\u0161\u00ED je jejich vz\u00E1jemn\u00E1 podobnost. Strukturu a vazby mezi sledovan\u00FDmi znaky vystihuj\u00ED metody sn\u00ED\u017Een\u00ED dimensionality. Rozptylov\u00FD diagram sk\u00F3re zobrazuje objekty, rozpt\u00FDlen\u00E9 v rovin\u011B prvn\u00EDch dvou hlavn\u00EDch komponent (PCA) \u010Di faktor\u016F (FA). Graf komponentn\u00EDch vah porovn\u00E1v\u00E1 vzd\u00E1lenosti (podobnosti) mezi znaky, kde kr\u00E1tk\u00E1 vzd\u00E1lenost zna\u010D\u00ED silnou korelaci dvou znak\u016F. Znaky ale tak\u00E9 povodn\u011B lze seskupovat do shluk\u016F hierarchicky, a to dle p\u0159edem zvolen\u00E9ho zp\u016Fsobu metriky a v\u00FDsledkem je"@cs . "[8D043454C7A0]" . "1211-0760" . . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza je zalo\u017Eena na latentn\u00EDch prom\u011Bnn\u00FDch, kter\u00E9 jsou line\u00E1rn\u00ED kombinac\u00ED p\u016Fvodn\u00EDch. Zdrojov\u00E1 matice dat obsahuje kvalitativn\u00ED znaky v 13 sloupc\u00EDch a sledovan\u00E9 povodn\u011B jako objekty v 25 \u0159\u00E1dc\u00EDch zdrojov\u00E9 matice dat. C\u00EDlem anal\u00FDzy dat je nal\u00E9zt shluk podobn\u00FDch si povodn\u00ED a podobn\u00FDch znak\u016F, je\u017E povodn\u011B popisuj\u00ED. Podobnost povodn\u00ED je posuzov\u00E1na na z\u00E1klad\u011B jist\u00FDch podobnost\u00ED \u010Di vzd\u00E1lenosti povodn\u00ED v 13-rozm\u011Brn\u00E9m prostoru v\u0161ech znak\u016F dle krit\u00E9ria, \u017Ee \u010D\u00EDm je Mahalonobisova vzd\u00E1lenost shluk\u016F povodn\u00ED v\u011Bt\u0161\u00ED, t\u00EDm men\u0161\u00ED je jejich vz\u00E1jemn\u00E1 podobnost. Strukturu a vazby mezi sledovan\u00FDmi znaky vystihuj\u00ED metody sn\u00ED\u017Een\u00ED dimensionality. Rozptylov\u00FD diagram sk\u00F3re zobrazuje objekty, rozpt\u00FDlen\u00E9 v rovin\u011B prvn\u00EDch dvou hlavn\u00EDch komponent (PCA) \u010Di faktor\u016F (FA). Graf komponentn\u00EDch vah porovn\u00E1v\u00E1 vzd\u00E1lenosti (podobnosti) mezi znaky, kde kr\u00E1tk\u00E1 vzd\u00E1lenost zna\u010D\u00ED silnou korelaci dvou znak\u016F. Znaky ale tak\u00E9 povodn\u011B lze seskupovat do shluk\u016F hierarchicky, a to dle p\u0159edem zvolen\u00E9ho zp\u016Fsobu metriky a v\u00FDsledkem je" . . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza povodn\u00ED na \u0159ece S\u00E1zav\u011B v obdob\u00ED 1961 - 2000" . . . "12" . . . "1"^^ . "Multivariate statistical analysis is based on the latent variables which are formed as the linear combination of original variables. The source data matrix contains here objects in 25 rows (floods) and variables (properties of floods) in 13 columns. Before data treatment the data are scaled. Similarity of objects and variables is considered on base on Mahalanobis distance in the 13-dimensional space. The principal components analysis PCA reduces dimensionality and presents floods in two or three dimensions. The plot of components weight shows hidden structure among variables while the scatterplot shows the hidden structure of objects. The cluster analysis leads to clusters which may be plotted in dendrogram. There are two dendrograms available, the dendrogram of variables properties) and the dendrogram of objects (floods)."@en . . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza povodn\u00ED na \u0159ece S\u00E1zav\u011B v obdob\u00ED 1961 - 2000"@cs . "PCA; FA; CA; CM; Cluster Analysis; Dendrogram; Cattel\u00B4s index diagram"@en . "Multivariate Statistical Analysis of Floods on the River S\u00E1zava within 1961 - 2000"@en . . "V\u00EDcerozm\u011Brn\u00E1 statistick\u00E1 anal\u00FDza povodn\u00ED na \u0159ece S\u00E1zav\u011B v obdob\u00ED 1961 - 2000"@cs . "25310" . .