"Losowanie warstwowe \u2013 losowanie pr\u00F3by oddzielnie z ka\u017Cdej cz\u0119\u015Bci, kt\u00F3ra nazywa si\u0119 warstw\u0105 populacji generalnej, kt\u00F3re zosta\u0142y wydzielone przed losowaniem. Ten spos\u00F3b wyboru pr\u00F3bki losowej jest stosowany, gdy populacja jest bardzo niejednorodna. Wst\u0119pny podzia\u0142 populacji na bardziej jednorodne warstwy, a nast\u0119pnie losowanie pr\u00F3bek z ka\u017Cdej z tych warstw cz\u0119sto zwi\u0119kszaj\u0105 reprezentatywno\u015B\u0107 pr\u00F3by i zmniejszaj\u0105 . Warstwy powinny by\u0107 roz\u0142\u0105czne (tzn \u017Caden element populacji nie mo\u017Ce by\u0107 zaliczony do dw\u00F3ch r\u00F3\u017Cnych warstw) oraz wyczerpuj\u0105ce (czyli w sumie powinny dawa\u0107 ca\u0142\u0105 populacj\u0119; ka\u017Cdy element populacji powinien nale\u017Ce\u0107 do jednej z warstw). Populacj\u0119 mo\u017Cna podzieli\u0107 na nie wi\u0119cej jak sze\u015B\u0107 warstw."@pl . "Losowanie warstwowe"@pl . . "La mostra estratificada o mostreig estratificat \u00E9s una forma de representaci\u00F3 estad\u00EDstica que mostra com es comporta una caracter\u00EDstica o variable en una poblaci\u00F3 a trav\u00E9s de fer evident el canvi d'aquesta variable en sub-poblacions o estrats. \u00C9s un m\u00E8tode de mostreig d'una poblaci\u00F3. Consisteix en la divisi\u00F3 pr\u00E8via de la poblaci\u00F3 d'estudi en grups o classes que es suposen homogenis respecte a la caracter\u00EDstica a estudiar i que no s'ensolapin. Segons la quantitat d'elements de la mostra que s'han d'elegir de cadascun dels estrats, hi ha dues t\u00E8cniques de mostreig estratificat: 1. \n* Assignaci\u00F3 proporcional: la mida de cada estrat en la mostra \u00E9s proporcional a la seva mida en la poblaci\u00F3. 2. \n* Assignaci\u00F3 \u00F2ptima: la mostra recollir\u00E0 m\u00E9s individus d'aquells estrats que tinguin m\u00E9s variabilitat. Per aix\u00F2 cal un coneixement previ de la poblaci\u00F3. Per exemple, per a un estudi d'opini\u00F3, pot resultar interessant estudiar per separat les opinions d'homes i dones donat que s'estima que, dins de cadascun d'aquests grups, pot haver certa homogene\u00EFtat. Aix\u00ED, si la poblaci\u00F3 \u00E9s composta d'un 55% de dones i un 45% d'homes, es prendria una mostra que contingui tamb\u00E9 aquesta mateixa proporci\u00F3. En general, la mida de la mostra en cada estrat es pren en proporci\u00F3 amb la mida de l'estrat. Aix\u00F2 s'anomena assignaci\u00F3 proporcional."@ca . . . "\u5C64\u5316\u62BD\u51FA\u6CD5\uFF08\u305D\u3046\u304B\u3061\u3085\u3046\u3057\u3085\u3064\u307B\u3046\u3001\u82F1: stratified sampling\uFF09\u3068\u306F\u3001\u7D71\u8A08\u5B66\u306B\u304A\u3051\u308B\u6BCD\u96C6\u56E3\u304B\u3089\u306E\u6A19\u672C\u8ABF\u67FB\u306E\u624B\u6CD5\u306E\u3072\u3068\u3064\u3002"@ja . "\u0412 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043A\u0435, \u0440\u0430\u0439\u043E\u043D\u0438\u0440\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u0432\u044B\u0431\u043E\u0440\u043A\u0430 (\u0434\u0440\u0443\u0433\u043E\u0435 \u043D\u0430\u0437\u0432\u0430\u043D\u0438\u0435 \u2014 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0438\u0446\u0438\u0440\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u0432\u044B\u0431\u043E\u0440\u043A\u0430) \u2014 \u043C\u0435\u0442\u043E\u0434 \u0441\u0435\u043C\u043F\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438\u0437 \u0433\u0435\u043D\u0435\u0440\u0430\u043B\u044C\u043D\u043E\u0439 \u0441\u043E\u0432\u043E\u043A\u0443\u043F\u043D\u043E\u0441\u0442\u0438, \u043A\u043E\u0442\u043E\u0440\u044B\u0439 \u043F\u043E\u0437\u0432\u043E\u043B\u044F\u0435\u0442 \u0443\u043B\u0443\u0447\u0448\u0438\u0442\u044C \u0442\u043E\u0447\u043D\u043E\u0441\u0442\u044C \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0445 \u0440\u0435\u0437\u0443\u043B\u044C\u0442\u0430\u0442\u043E\u0432 \u043F\u0440\u0438 \u0440\u0430\u0437\u0431\u0438\u0435\u043D\u0438\u0438 \u0432\u0441\u0435\u0433\u043E \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u0430 \u0441\u043E\u0431\u044B\u0442\u0438\u0439 \u043D\u0430 \u043D\u0435\u0441\u043A\u043E\u043B\u044C\u043A\u043E \u043E\u0431\u043B\u0430\u0441\u0442\u0435\u0439-\u0441\u0442\u0440\u0430\u0442 \u0438 \u043D\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043C\u043E\u0439 \u0440\u0430\u0431\u043E\u0442\u0435 \u0441 \u044D\u0442\u0438\u043C\u0438 \u0441\u0442\u0440\u0430\u0442\u0430\u043C\u0438. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0432 \u043A\u0430\u0436\u0434\u043E\u0439 \u0441\u0442\u0440\u0430\u0442\u0435 \u043C\u043E\u0436\u043D\u043E \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0442\u044C \u0441\u0432\u043E\u044E \u0441\u043E\u0431\u0441\u0442\u0432\u0435\u043D\u043D\u0443\u044E \u0432\u044B\u0431\u043E\u0440\u043A\u0443 \u043F\u043E \u0437\u043D\u0430\u0447\u0438\u043C\u043E\u0441\u0442\u0438."@ru . "\u0421\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043A\u043E\u0432\u0430\u043D\u0430 \u043F\u0440\u043E\u0431\u0430"@uk . . . . . . . . . . "Mostra estratificada"@ca . . . . "\u00C9chantillonnage stratifi\u00E9"@fr . . "\u0420\u0430\u0439\u043E\u043D\u0438\u0440\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u0432\u044B\u0431\u043E\u0440\u043A\u0430"@ru . "Losowanie warstwowe \u2013 losowanie pr\u00F3by oddzielnie z ka\u017Cdej cz\u0119\u015Bci, kt\u00F3ra nazywa si\u0119 warstw\u0105 populacji generalnej, kt\u00F3re zosta\u0142y wydzielone przed losowaniem. Ten spos\u00F3b wyboru pr\u00F3bki losowej jest stosowany, gdy populacja jest bardzo niejednorodna. Wst\u0119pny podzia\u0142 populacji na bardziej jednorodne warstwy, a nast\u0119pnie losowanie pr\u00F3bek z ka\u017Cdej z tych warstw cz\u0119sto zwi\u0119kszaj\u0105 reprezentatywno\u015B\u0107 pr\u00F3by i zmniejszaj\u0105 . Warstwy powinny by\u0107 roz\u0142\u0105czne (tzn \u017Caden element populacji nie mo\u017Ce by\u0107 zaliczony do dw\u00F3ch r\u00F3\u017Cnych warstw) oraz wyczerpuj\u0105ce (czyli w sumie powinny dawa\u0107 ca\u0142\u0105 populacj\u0119; ka\u017Cdy element populacji powinien nale\u017Ce\u0107 do jednej z warstw). Populacj\u0119 mo\u017Cna podzieli\u0107 na nie wi\u0119cej jak sze\u015B\u0107 warstw. Losowanie warstwowe stosuje si\u0119 w\u00F3wczas gdy badana populacja generalna charakteryzuje si\u0119 du\u017Cym zr\u00F3\u017Cnicowaniem, a zarazem wyst\u0119powaniem du\u017Cej liczby jednostek posiadaj\u0105cych t\u0119 sam\u0105 warto\u015B\u0107 zmiennej. Podczas losowania warstwowego mo\u017Ce nast\u0105pi\u0107 taka sytuacja, gdy zostan\u0105 wylosowane tylko takie elementy, kt\u00F3re posiadaj\u0105 tak\u0105 sam\u0105 warto\u015B\u0107 zmiennej. Aby tego unikn\u0105\u0107, nale\u017Cy podzieli\u0107 populacj\u0119 generaln\u0105 na mniejsze zbiory jednostek charakteryzuj\u0105cych si\u0119 tak\u0105 sam\u0105 warto\u015Bci\u0105 zmiennej, a nast\u0119pnie wylosowaniu z ka\u017Cdej z grup pr\u00F3b\u0119. Suma wszystkich pr\u00F3b stanowi pr\u00F3b\u0119 z populacji generalnej."@pl . . . . . "Amostragem estratificada"@pt . . . . "\u0420\u043E\u0437\u0448\u0430\u0440\u043E\u0432\u0430\u043D\u0430 \u0432\u0438\u0431\u0456\u0440\u043A\u0430, \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043A\u043E\u0432\u0430\u043D\u0430 \u043F\u0440\u043E\u0431\u0430 (\u0430\u043D\u0433\u043B. stratified sample) \u2014 \u043F\u0440\u043E\u0431\u0430, \u0449\u043E \u0441\u043A\u043B\u0430\u0434\u0430\u0454\u0442\u044C\u0441\u044F \u0437 \u043F\u043E\u0440\u0446\u0456\u0439, \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u0438\u0445 \u0437 \u0456\u0434\u0435\u043D\u0442\u0438\u0447\u043D\u0438\u0445 \u0441\u0443\u0431\u0447\u0430\u0441\u0442\u0438\u043D (\u0441\u0442\u0440\u0430\u0442) \u0440\u043E\u0434\u043E\u043D\u0430\u0447\u0430\u043B\u044C\u043D\u043E\u0457 \u0441\u0443\u043A\u0443\u043F\u043D\u043E\u0441\u0442\u0456. \u0417 \u043A\u043E\u0436\u043D\u043E\u0457 \u0441\u0443\u0431\u0447\u0430\u0441\u0442\u0438\u043D\u0438 \u043F\u0440\u043E\u0431\u0438 \u0432\u0456\u0434\u0431\u0438\u0440\u0430\u044E\u0442\u044C\u0441\u044F \u0434\u043E\u0432\u0456\u043B\u044C\u043D\u043E. \u0417\u0430\u0432\u0434\u0430\u043D\u043D\u044F\u043C \u0432\u0437\u044F\u0442\u0442\u044F \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043A\u043E\u0432\u0430\u043D\u0438\u0445 \u043F\u0440\u043E\u0431 \u0454 \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u043D\u044F \u0431\u0456\u043B\u044C\u0448 \u0440\u0435\u043F\u0440\u0435\u0437\u0435\u043D\u0442\u0430\u0442\u0438\u0432\u043D\u043E\u0433\u043E \u0437\u0440\u0430\u0437\u043A\u0430, \u043D\u0456\u0436 \u0442\u043E\u0439, \u0449\u043E \u0431\u0435\u0440\u0435\u0442\u044C\u0441\u044F \u0437\u0430 \u043C\u0435\u0442\u043E\u0434\u0438\u043A\u043E\u044E \u0432\u0438\u043F\u0430\u0434\u043A\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0431\u043E\u0440\u0443 \u043F\u0440\u043E\u0431. \u0420\u043E\u0437\u0448\u0430\u0440\u043E\u0432\u0430\u043D\u0430 \u0432\u0438\u0431\u0456\u0440\u043A\u0430 \u0441\u043A\u043B\u0430\u0434\u0430\u0454\u0442\u044C\u0441\u044F \u0437 \u0440\u0456\u0437\u043D\u0438\u0445 \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0456\u0432 \u043F\u043E\u043F\u0443\u043B\u044F\u0446\u0456\u0457, \u043D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, \u0440\u043E\u0431\u043B\u044F\u0447\u0438 \u0432\u0438\u0431\u0456\u0440\u043A\u0438 \u0443 \u0440\u0456\u0437\u043D\u0438\u0445 \u0432\u0456\u043A\u043E\u0432\u0438\u0445 \u0433\u0440\u0443\u043F\u0430\u0445. \u0420\u043E\u0437\u043C\u0456\u0440 \u0432\u0438\u0431\u0456\u0440\u043A\u0438 \u0434\u043B\u044F \u043A\u043E\u0436\u043D\u043E\u0433\u043E \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0443 \u043F\u0440\u043E\u043F\u043E\u0440\u0446\u0456\u0439\u043D\u0438\u0439 \u0440\u043E\u0437\u043C\u0456\u0440\u0443 \u0446\u044C\u043E\u0433\u043E \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0443. \u0412\u0430\u0436\u043B\u0438\u0432\u043E, \u0449\u043E\u0431 \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0438 \u043D\u0435 \u043F\u0435\u0440\u0435\u0442\u0438\u043D\u0430\u043B\u0438\u0441\u044C."@uk . . . "Stratified sampling"@en . "Das Ziehen einer geschichteten Zufallsstichprobe (auch: stratifizierte Zufallsstichprobe) kann in der Statistik Vorteile bringen, wenn die Grundgesamtheit in sinnvolle Gruppen, die sogenannten Schichten, unterteilt werden kann. Sinnvoll bedeutet hier, dass die Schichten hinsichtlich eines oder mehrerer Merkmale, die auch die Auspr\u00E4gung des letztlich interessierenden Merkmals beeinflussen, in sich relativ homogen sind und sich voneinander m\u00F6glichst deutlich unterscheiden. Typische Schichten, die f\u00FCr Stichproben zur Beantwortung sozialwissenschaftlicher, medizinischer oder Marktforschungs-relevanter Fragestellungen eine Rolle spielen, w\u00E4ren etwa Altersgruppen oder Bev\u00F6lkerungsschichten nach Einkommen, Bildungsabschluss, Wohnort etc. Man schr\u00E4nkt nun die rein zuf\u00E4llige Auswahl der Stichprobenelemente insofern ein, als man die Stichprobenumf\u00E4nge pro Schicht vorgibt und danach in jeder Schicht eine reine Zufallsstichprobe zieht. (Die einzelnen Zufallsstichproben werden getrennt ausgewertet und die Ergebnisse im Anschluss zusammengefasst.) Man \u201Everbietet\u201C damit extreme Stichproben, die beispielsweise zuf\u00E4llig fast nur Elemente aus einer Schicht enthalten, und bekommt in der Konsequenz bessere Punktsch\u00E4tzer, d. h. Sch\u00E4tzer mit kleinerer Varianz. Durch geeignete Schichtung l\u00E4sst sich also bei gleicher Ergebnisgenauigkeit der Gesamtstichprobenumfang gegen\u00FCber einer einfachen Zufallsstichprobenziehung verringern, was die Kosten der Datenerhebung senkt. In Monte-Carlo-Simulationen kann man geschichtete Zufallsziehungen als Mittel der Varianzreduktion einsetzen. Die Schichtungsmerkmale (Paradaten) m\u00FCssen vorab bekannt sein."@de . . "El muestreo estratificado es una forma de representaci\u00F3n estad\u00EDstica que muestra c\u00F3mo se comporta una caracter\u00EDstica o variable en una poblaci\u00F3n a trav\u00E9s de hacer evidente el cambio de dicha variable en subpoblaciones o estratos en los que se ha dividido.Consiste en la divisi\u00F3n previa de la poblaci\u00F3n de estudio en grupos o clases que se suponen homog\u00E9neos respecto a caracter\u00EDstica a estudiar y que no se solapen. Seg\u00FAn la cantidad de elementos de la muestra que se han de elegir de cada uno de los estratos, existen dos t\u00E9cnicas de muestreo estratificado: 1. \n* Asignaci\u00F3n proporcional: el tama\u00F1o de cada estrato en la muestra es proporcional a su tama\u00F1o en la poblaci\u00F3n. 2. \n* Asignaci\u00F3n \u00F3ptima: la muestra recoger\u00E1 m\u00E1s individuos de aquellos estratos que tengan m\u00E1s variabilidad. Para ello es necesario un conocimiento previo de la poblaci\u00F3n. Por ejemplo, para un estudio de opini\u00F3n, puede resultar interesante estudiar por separado las opiniones de hombres y mujeres pues se estima que, dentro de cada uno de estos grupos, puede haber cierta homogeneidad. As\u00ED, si la poblaci\u00F3n est\u00E1 compuesta de un 55% de mujeres y un 45% de hombres, se tomar\u00EDa una muestra que contenga tambi\u00E9n esa misma proporci\u00F3n."@es . "Em estat\u00EDstica, a amostragem estratificada \u00E9 um m\u00E9todo de amostragem de uma popula\u00E7\u00E3o que pode ser dividida em subpopula\u00E7\u00F5es. Em pesquisas estat\u00EDsticas, quando as subpopula\u00E7\u00F5es dentro de uma popula\u00E7\u00E3o geral variam, pode ser vantajoso amostrar cada subpopula\u00E7\u00E3o (estrato) independentemente. A estratifica\u00E7\u00E3o \u00E9 o processo de dividir os membros da popula\u00E7\u00E3o em subgrupos homog\u00EAneos antes da amostragem. Os estratos devem definir uma parti\u00E7\u00E3o da popula\u00E7\u00E3o. Ou seja, deve ser coletivamente exaustivo e mutuamente exclusivo: cada elemento da popula\u00E7\u00E3o deve ser atribu\u00EDdo a um e apenas um estrato. Em seguida, a amostragem aleat\u00F3ria simples \u00E9 aplicada dentro de cada estrato. O objetivo \u00E9 melhorar a precis\u00E3o da amostra reduzindo o erro amostral. Pode produzir uma m\u00E9dia ponderada que tem menos variabilidade do que a m\u00E9dia aritm\u00E9tica de uma amostra aleat\u00F3ria simples da popula\u00E7\u00E3o. Em estat\u00EDstica computacional, a amostragem estratificada \u00E9 um m\u00E9todo de redu\u00E7\u00E3o de vari\u00E2ncia quando os m\u00E9todos de Monte Carlo s\u00E3o usados para estimar estat\u00EDsticas populacionais de uma popula\u00E7\u00E3o conhecida."@pt . . . "\u5206\u5C42\u62BD\u6837\uFF08stratified sampling\uFF09\uFF0C\u53C8\u540D\u5C64\u5316\u62BD\u51FA\u6CD5\uFF0C\u662F\u7D71\u8A08\u5B78\u7684\u4E00\u5F9E\u7D71\u8A08\u7E3D\u9AD4\uFF08\u53C8\u7A31\u70BA\u300C\u6BCD\u9AD4\u300D\uFF09\u62BD\u53D6\u6837\u672C\u65B9\u6CD5\u3002\u5C06\u62BD\u6837\u5355\u4F4D\u6309\u67D0\u79CD\u7279\u5F81\u6216\u67D0\u79CD\u89C4\u5219\u5212\u5206\u4E3A\u4E0D\u540C\u7684\u5C42\uFF0C\u7136\u540E\u4ECE\u4E0D\u540C\u7684\u5C42\u4E2D\u72EC\u7ACB\u3001\u968F\u673A\u5730\u62BD\u53D6\u6837\u672C\u3002\u4ECE\u800C\u4FDD\u8BC1\u6837\u672C\u7684\u7ED3\u6784\u4E0E\u603B\u4F53\u7684\u7ED3\u6784\u6BD4\u8F83\u76F8\u8FD1\uFF0C\u4ECE\u800C\u63D0\u9AD8\u4F30\u8BA1\u7684\u7CBE\u5EA6\u3002\u76F8\u5C0D\u65BC\u6C92\u6709\u7D93\u904E\u5206\u5C64\u7684\u62BD\u6A23\u8ABF\u67E5\uFF0C\u5176\u6578\u64DA\u6703\u88AB\u7A31\u70BA\u300C\u672A\u5206\u5C64\u62BD\u6A23\u300D\uFF08unstratified samples\uFF09\u3002 \u5728\u793E\u4F1A\u7EDF\u8BA1\u8C03\u67E5\uFF08statistical survey\uFF09\uFF0C\u7576\u7E3D\u9AD4\u5167\u7684\u300C\u300D\uFF08subpopulations\uFF09\u4E4B\u9593\u7684\u5DEE\u7570\u8F03\u5927\uFF0C\u5C0D\u6BCF\u500B\u5B50\u7E3D\u9AD4\u5206\u5225\u9032\u884C\u5206\u5C64\u62BD\u6A23\u8ABF\u67E5\uFF0C\u6703\u4EE4\u7D71\u8A08\u8ABF\u67E5\u7D50\u679C\u66F4\u70BA\u6E96\u78BA\u3002\u5B50\u7E3D\u9AD4\u7684\u5206\u5C64\u5FC5\u9808\u70BA\u4E92\u65A5\uFF0C\u5373\u6BCF\u500B\u7E3D\u9AD4\u7684\u6210\u54E1\u5747\u53EA\u80FD\u5C6C\u65BC\u4E00\u500B\u5206\u5C64\u3002\u4E4B\u5F8C\uFF0C\u53EF\u5C0D\u6BCF\u500B\u5B50\u7E3D\u9AD4\u9032\u884C\u6216\u3002\u9019\u6A23\u53EF\u4EE4\u8ABF\u67E5\u7684\u4EE3\u8868\u6027\u6539\u5584\u3002\u76F8\u5C0D\u65BC\u7C21\u9AD4\u96A8\u6A5F\u62BD\u6A23\u63A1\u53D6\u7684\u7B97\u672F\u5E73\u5747\u503C\uFF0C\u5206\u5C64\u7684\u62BD\u6A23\u61C9\u63A1\u7528\u52A0\u6B0A\u5E73\u5747\u503C\u3002"@zh . . "\u0641\u064A \u0627\u0644\u0625\u062D\u0635\u0627\u0621 \u060C \u064A\u0639\u062A\u0628\u0631 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0627\u0644\u0637\u0628\u0642\u064A \u0637\u0631\u064A\u0642\u0629 \u0644\u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0645\u0646 \u0627\u0644\u0633\u0643\u0627\u0646 \u0648\u0627\u0644\u062A\u064A \u064A\u0645\u0643\u0646 \u062A\u0642\u0633\u064A\u0645\u0647\u0627 \u0625\u0644\u0649 \u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0633\u0643\u0627\u0646\u064A\u0629 \u0641\u0631\u0639\u064A\u0629 . \u0641\u064A \u0627\u0644\u0645\u0633\u0648\u062D\u0627\u062A \u0627\u0644\u0625\u062D\u0635\u0627\u0626\u064A\u0629 \u060C \u0639\u0646\u062F\u0645\u0627 \u062A\u062A\u0628\u0627\u064A\u0646 \u0627\u0644\u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0627\u0644\u0633\u0643\u0627\u0646\u064A\u0629 \u0627\u0644\u0641\u0631\u0639\u064A\u0629 \u0636\u0645\u0646 \u0625\u062C\u0645\u0627\u0644\u064A \u0639\u062F\u062F \u0627\u0644\u0633\u0643\u0627\u0646\u060C \u0642\u062F \u064A\u0643\u0648\u0646 \u0645\u0646 \u0627\u0644\u0645\u0641\u064A\u062F \u0623\u062E\u0630 \u0639\u064A\u0646\u0629 \u0645\u0646 \u0643\u0644 \u0645\u062C\u0645\u0648\u0639\u0629 \u0633\u0643\u0627\u0646\u064A\u0629 \u0641\u0631\u0639\u064A\u0629 ( \u0637\u0628\u0642\u0629 ) \u0628\u0634\u0643\u0644 \u0645\u0633\u062A\u0642\u0644. \u0627\u0644\u062A\u0642\u0633\u064A\u0645 \u0627\u0644\u0637\u0628\u0642\u064A \u0647\u0648 \u0639\u0645\u0644\u064A\u0629 \u062A\u0642\u0633\u064A\u0645 \u0623\u0641\u0631\u0627\u062F \u0627\u0644\u0633\u0643\u0627\u0646 \u0625\u0644\u0649 \u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0641\u0631\u0639\u064A\u0629 \u0645\u062A\u062C\u0627\u0646\u0633\u0629 \u0642\u0628\u0644 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A. \u064A\u062C\u0628 \u0623\u0646 \u062A\u062D\u062F\u062F \u0627\u0644\u0637\u0628\u0642\u0627\u062A \u062A\u0642\u0633\u064A\u0645\u064B\u0627 \u0644\u0644\u0633\u0643\u0627\u0646. \u0623\u064A \u0623\u0646\u0647 \u064A\u062C\u0628 \u0623\u0646 \u064A\u0643\u0648\u0646 \u0634\u0627\u0645\u0644\u0627\u064B \u0628\u0634\u0643\u0644 \u062C\u0645\u0627\u0639\u064A \u0648\u0645\u062A\u0628\u0627\u062F\u0644 : \u064A\u062C\u0628 \u062A\u062E\u0635\u064A\u0635 \u0643\u0644 \u0639\u0646\u0635\u0631 \u0641\u064A \u0627\u0644\u0645\u062C\u062A\u0645\u0639 \u0644\u0637\u0628\u0642\u0629 \u0648\u0627\u062D\u062F\u0629 \u0641\u0642\u0637. \u062B\u0645 \u064A\u062A\u0645 \u062A\u0637\u0628\u064A\u0642 \u0639\u064A\u0646\u0627\u062A \u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0628\u0633\u064A\u0637\u0629 \u062F\u0627\u062E\u0644 \u0643\u0644 \u0637\u0628\u0642\u0629. \u0627\u0644\u0647\u062F\u0641 \u0647\u0648 \u062A\u062D\u0633\u064A\u0646 \u062F\u0642\u0629 \u0627\u0644\u0639\u064A\u0646\u0629 \u0639\u0646 \u0637\u0631\u064A\u0642 \u062A\u0642\u0644\u064A\u0644 \u062E\u0637\u0623 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A . \u064A\u0645\u0643\u0646 \u0623\u0646 \u064A\u0646\u062A\u062C \u0645\u062A\u0648\u0633\u0637 \u0645\u0631\u062C\u062D \u0623\u0642\u0644 \u062A\u0646\u0648\u0639\u064B\u0627 \u0645\u0646 \u0627\u0644\u0645\u062A\u0648\u0633\u0637 \u0627\u0644\u062D\u0633\u0627\u0628\u064A \u0644\u0639\u064A\u0646\u0629 \u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0628\u0633\u064A\u0637\u0629 \u0645\u0646 \u0627\u0644\u0645\u062C\u062A\u0645\u0639. \u0641\u064A \u0627\u0644\u0625\u062D\u0635\u0627\u0621 \u0627\u0644\u062D\u0633\u0627\u0628\u064A\u060C \u064A\u0639\u062F \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0627\u0644\u0637\u0628\u0642\u064A \u0637\u0631\u064A\u0642\u0629 \u0644\u062A\u0642\u0644\u064A\u0644 \u0627\u0644\u062A\u0628\u0627\u064A\u0646 \u0639\u0646\u062F \u0627\u0633\u062A\u062E\u062F\u0627\u0645 \u0637\u0631\u0642 \u0645\u0648\u0646\u062A \u0643\u0627\u0631\u0644\u0648 \u0644\u062A\u0642\u062F\u064A\u0631 \u0625\u062D\u0635\u0627\u0621\u0627\u062A \u0627\u0644\u0633\u0643\u0627\u0646 \u0645\u0646 \u0645\u062C\u0645\u0648\u0639\u0629 \u0633\u0643\u0627\u0646\u064A\u0629 \u0645\u0639\u0631\u0648\u0641\u0629."@ar . "En statistique, un \u00E9chantillonnage stratifi\u00E9 est une m\u00E9thode d'\u00E9chantillonnage \u00E0 partir d'une population. Dans un sondage, lorsque des sous-populations varient au sein d'une population g\u00E9n\u00E9rale, il peut \u00EAtre avantageux de s\u00E9lectionner un \u00E9chantillon au sein de chaque sous-population (ou strates). La stratification est le processus consistant \u00E0 diviser la population g\u00E9n\u00E9rale en sous-groupes homog\u00E8nes avant l'\u00E9chantillonnage. Les strates doivent \u00EAtre mutuellement exclusives : chaque \u00E9l\u00E9ment de la population est assign\u00E9 \u00E0 une strate unique. Par ailleurs, aucun \u00E9l\u00E9ment de la population g\u00E9n\u00E9rale ne peut \u00EAtre omis. L'\u00E9chantillonnage est alors appliqu\u00E9 au sein des strates. Cette m\u00E9thode permet parfois de r\u00E9duire l'erreur d'\u00E9chantillonnage."@fr . "In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population. That is, it should be collectively exhaustive and mutually exclusive: every element in the population must be assigned to one and only one stratum. Then simple random sampling is applied within each stratum. The objective is to improve the precision of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population. In computational statistics, stratified sampling is a method of variance reduction when Monte Carlo methods are used to estimate population statistics from a known population."@en . "\u5206\u5C42\u62BD\u6837\uFF08stratified sampling\uFF09\uFF0C\u53C8\u540D\u5C64\u5316\u62BD\u51FA\u6CD5\uFF0C\u662F\u7D71\u8A08\u5B78\u7684\u4E00\u5F9E\u7D71\u8A08\u7E3D\u9AD4\uFF08\u53C8\u7A31\u70BA\u300C\u6BCD\u9AD4\u300D\uFF09\u62BD\u53D6\u6837\u672C\u65B9\u6CD5\u3002\u5C06\u62BD\u6837\u5355\u4F4D\u6309\u67D0\u79CD\u7279\u5F81\u6216\u67D0\u79CD\u89C4\u5219\u5212\u5206\u4E3A\u4E0D\u540C\u7684\u5C42\uFF0C\u7136\u540E\u4ECE\u4E0D\u540C\u7684\u5C42\u4E2D\u72EC\u7ACB\u3001\u968F\u673A\u5730\u62BD\u53D6\u6837\u672C\u3002\u4ECE\u800C\u4FDD\u8BC1\u6837\u672C\u7684\u7ED3\u6784\u4E0E\u603B\u4F53\u7684\u7ED3\u6784\u6BD4\u8F83\u76F8\u8FD1\uFF0C\u4ECE\u800C\u63D0\u9AD8\u4F30\u8BA1\u7684\u7CBE\u5EA6\u3002\u76F8\u5C0D\u65BC\u6C92\u6709\u7D93\u904E\u5206\u5C64\u7684\u62BD\u6A23\u8ABF\u67E5\uFF0C\u5176\u6578\u64DA\u6703\u88AB\u7A31\u70BA\u300C\u672A\u5206\u5C64\u62BD\u6A23\u300D\uFF08unstratified samples\uFF09\u3002 \u5728\u793E\u4F1A\u7EDF\u8BA1\u8C03\u67E5\uFF08statistical survey\uFF09\uFF0C\u7576\u7E3D\u9AD4\u5167\u7684\u300C\u300D\uFF08subpopulations\uFF09\u4E4B\u9593\u7684\u5DEE\u7570\u8F03\u5927\uFF0C\u5C0D\u6BCF\u500B\u5B50\u7E3D\u9AD4\u5206\u5225\u9032\u884C\u5206\u5C64\u62BD\u6A23\u8ABF\u67E5\uFF0C\u6703\u4EE4\u7D71\u8A08\u8ABF\u67E5\u7D50\u679C\u66F4\u70BA\u6E96\u78BA\u3002\u5B50\u7E3D\u9AD4\u7684\u5206\u5C64\u5FC5\u9808\u70BA\u4E92\u65A5\uFF0C\u5373\u6BCF\u500B\u7E3D\u9AD4\u7684\u6210\u54E1\u5747\u53EA\u80FD\u5C6C\u65BC\u4E00\u500B\u5206\u5C64\u3002\u4E4B\u5F8C\uFF0C\u53EF\u5C0D\u6BCF\u500B\u5B50\u7E3D\u9AD4\u9032\u884C\u6216\u3002\u9019\u6A23\u53EF\u4EE4\u8ABF\u67E5\u7684\u4EE3\u8868\u6027\u6539\u5584\u3002\u76F8\u5C0D\u65BC\u7C21\u9AD4\u96A8\u6A5F\u62BD\u6A23\u63A1\u53D6\u7684\u7B97\u672F\u5E73\u5747\u503C\uFF0C\u5206\u5C64\u7684\u62BD\u6A23\u61C9\u63A1\u7528\u52A0\u6B0A\u5E73\u5747\u503C\u3002"@zh . . . . "\uD1B5\uACC4\uD559\uC5D0\uC11C, \uCE35\uD654\uD45C\uC9D1(\u5C64\u5316\u6A19\u96C6, Stratified sampling)\uC740 \uBAA8\uC9D1\uB2E8\uC744 \uBA3C\uC800 \uC911\uBCF5\uB418\uC9C0 \uC54A\uB3C4\uB85D \uCE35\uC73C\uB85C \uB098\uB208 \uB2E4\uC74C \uAC01 \uCE35\uC5D0\uC11C \uD45C\uBCF8\uC744 \uCD94\uCD9C\uD558\uB294 \uBC29\uBC95\uC774\uB2E4. \uCE35\uC744 \uB098\uB20C \uB54C \uCE35\uB0B4\uB294 \uB3D9\uC9C8\uC801(homogeneous), \uCE35\uAC04\uC740 \uC774\uC9C8\uC801(heterogeneous) \uD2B9\uC131\uC744 \uAC00\uC9C0\uB3C4\uB85D \uD558\uBA74 \uC801\uC740 \uBE44\uC6A9\uC73C\uB85C \uB354 \uC815\uD655\uD55C \uCD94\uC815\uC744 \uD560 \uC218 \uC788\uC73C\uBA70, \uC804\uCCB4 \uBAA8\uC9D1\uB2E8\uBFD0\uB9CC \uC544\uB2C8\uB77C \uAC01 \uCE35\uC758 \uD2B9\uC131\uC5D0 \uB300\uD55C \uCD94\uC815\uB3C4 \uD560 \uC218 \uC788\uB2E4\uB294 \uC7A5\uC810\uC774 \uC788\uB2E4. \uAC01 \uCE35\uC73C\uB85C\uBD80\uD130 \uD45C\uBCF8\uC744 \uCD94\uCD9C\uD560 \uB54C \uB2E8\uC21C\uC784\uC758 \uCD94\uCD9C\uBC29\uBC95\uC744 \uC4F8 \uC218\uB3C4 \uC788\uACE0 \uACC4\uD1B5\uD45C\uC9D1(systematic sampling) \uB4F1 \uB2E4\uB978 \uCD94\uCD9C\uBC29\uBC95\uC744 \uC4F8 \uC218\uB3C4 \uC788\uB2E4. \uB610 \uD544\uC694\uC5D0 \uB530\uB77C \uAC01 \uCE35\uC744 \uB2E4\uC2DC \uD558\uC704\uCE35\uC73C\uB85C \uB098\uB204\uC5B4 \uCD94\uCD9C\uD558\uB294 \uB2E4\uB2E8\uACC4 \uCE35\uD654 \uCD94\uCD9C\uC744 \uD558\uAE30\uB3C4 \uD55C\uB2E4."@ko . . . "Geschichtete Zufallsstichprobe"@de . "Das Ziehen einer geschichteten Zufallsstichprobe (auch: stratifizierte Zufallsstichprobe) kann in der Statistik Vorteile bringen, wenn die Grundgesamtheit in sinnvolle Gruppen, die sogenannten Schichten, unterteilt werden kann. Sinnvoll bedeutet hier, dass die Schichten hinsichtlich eines oder mehrerer Merkmale, die auch die Auspr\u00E4gung des letztlich interessierenden Merkmals beeinflussen, in sich relativ homogen sind und sich voneinander m\u00F6glichst deutlich unterscheiden. Typische Schichten, die f\u00FCr Stichproben zur Beantwortung sozialwissenschaftlicher, medizinischer oder Marktforschungs-relevanter Fragestellungen eine Rolle spielen, w\u00E4ren etwa Altersgruppen oder Bev\u00F6lkerungsschichten nach Einkommen, Bildungsabschluss, Wohnort etc."@de . . . "\uCE35\uD654\uD45C\uC9D1"@ko . . . . "\u0412 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043A\u0435, \u0440\u0430\u0439\u043E\u043D\u0438\u0440\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u0432\u044B\u0431\u043E\u0440\u043A\u0430 (\u0434\u0440\u0443\u0433\u043E\u0435 \u043D\u0430\u0437\u0432\u0430\u043D\u0438\u0435 \u2014 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0438\u0446\u0438\u0440\u043E\u0432\u0430\u043D\u043D\u0430\u044F \u0432\u044B\u0431\u043E\u0440\u043A\u0430) \u2014 \u043C\u0435\u0442\u043E\u0434 \u0441\u0435\u043C\u043F\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438\u0437 \u0433\u0435\u043D\u0435\u0440\u0430\u043B\u044C\u043D\u043E\u0439 \u0441\u043E\u0432\u043E\u043A\u0443\u043F\u043D\u043E\u0441\u0442\u0438, \u043A\u043E\u0442\u043E\u0440\u044B\u0439 \u043F\u043E\u0437\u0432\u043E\u043B\u044F\u0435\u0442 \u0443\u043B\u0443\u0447\u0448\u0438\u0442\u044C \u0442\u043E\u0447\u043D\u043E\u0441\u0442\u044C \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0445 \u0440\u0435\u0437\u0443\u043B\u044C\u0442\u0430\u0442\u043E\u0432 \u043F\u0440\u0438 \u0440\u0430\u0437\u0431\u0438\u0435\u043D\u0438\u0438 \u0432\u0441\u0435\u0433\u043E \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u0430 \u0441\u043E\u0431\u044B\u0442\u0438\u0439 \u043D\u0430 \u043D\u0435\u0441\u043A\u043E\u043B\u044C\u043A\u043E \u043E\u0431\u043B\u0430\u0441\u0442\u0435\u0439-\u0441\u0442\u0440\u0430\u0442 \u0438 \u043D\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043C\u043E\u0439 \u0440\u0430\u0431\u043E\u0442\u0435 \u0441 \u044D\u0442\u0438\u043C\u0438 \u0441\u0442\u0440\u0430\u0442\u0430\u043C\u0438. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0432 \u043A\u0430\u0436\u0434\u043E\u0439 \u0441\u0442\u0440\u0430\u0442\u0435 \u043C\u043E\u0436\u043D\u043E \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0442\u044C \u0441\u0432\u043E\u044E \u0441\u043E\u0431\u0441\u0442\u0432\u0435\u043D\u043D\u0443\u044E \u0432\u044B\u0431\u043E\u0440\u043A\u0443 \u043F\u043E \u0437\u043D\u0430\u0447\u0438\u043C\u043E\u0441\u0442\u0438."@ru . . . . . . "\u0641\u064A \u0627\u0644\u0625\u062D\u0635\u0627\u0621 \u060C \u064A\u0639\u062A\u0628\u0631 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0627\u0644\u0637\u0628\u0642\u064A \u0637\u0631\u064A\u0642\u0629 \u0644\u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0645\u0646 \u0627\u0644\u0633\u0643\u0627\u0646 \u0648\u0627\u0644\u062A\u064A \u064A\u0645\u0643\u0646 \u062A\u0642\u0633\u064A\u0645\u0647\u0627 \u0625\u0644\u0649 \u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0633\u0643\u0627\u0646\u064A\u0629 \u0641\u0631\u0639\u064A\u0629 . \u0641\u064A \u0627\u0644\u0645\u0633\u0648\u062D\u0627\u062A \u0627\u0644\u0625\u062D\u0635\u0627\u0626\u064A\u0629 \u060C \u0639\u0646\u062F\u0645\u0627 \u062A\u062A\u0628\u0627\u064A\u0646 \u0627\u0644\u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0627\u0644\u0633\u0643\u0627\u0646\u064A\u0629 \u0627\u0644\u0641\u0631\u0639\u064A\u0629 \u0636\u0645\u0646 \u0625\u062C\u0645\u0627\u0644\u064A \u0639\u062F\u062F \u0627\u0644\u0633\u0643\u0627\u0646\u060C \u0642\u062F \u064A\u0643\u0648\u0646 \u0645\u0646 \u0627\u0644\u0645\u0641\u064A\u062F \u0623\u062E\u0630 \u0639\u064A\u0646\u0629 \u0645\u0646 \u0643\u0644 \u0645\u062C\u0645\u0648\u0639\u0629 \u0633\u0643\u0627\u0646\u064A\u0629 \u0641\u0631\u0639\u064A\u0629 ( \u0637\u0628\u0642\u0629 ) \u0628\u0634\u0643\u0644 \u0645\u0633\u062A\u0642\u0644. \u0627\u0644\u062A\u0642\u0633\u064A\u0645 \u0627\u0644\u0637\u0628\u0642\u064A \u0647\u0648 \u0639\u0645\u0644\u064A\u0629 \u062A\u0642\u0633\u064A\u0645 \u0623\u0641\u0631\u0627\u062F \u0627\u0644\u0633\u0643\u0627\u0646 \u0625\u0644\u0649 \u0645\u062C\u0645\u0648\u0639\u0627\u062A \u0641\u0631\u0639\u064A\u0629 \u0645\u062A\u062C\u0627\u0646\u0633\u0629 \u0642\u0628\u0644 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A. \u064A\u062C\u0628 \u0623\u0646 \u062A\u062D\u062F\u062F \u0627\u0644\u0637\u0628\u0642\u0627\u062A \u062A\u0642\u0633\u064A\u0645\u064B\u0627 \u0644\u0644\u0633\u0643\u0627\u0646. \u0623\u064A \u0623\u0646\u0647 \u064A\u062C\u0628 \u0623\u0646 \u064A\u0643\u0648\u0646 \u0634\u0627\u0645\u0644\u0627\u064B \u0628\u0634\u0643\u0644 \u062C\u0645\u0627\u0639\u064A \u0648\u0645\u062A\u0628\u0627\u062F\u0644 : \u064A\u062C\u0628 \u062A\u062E\u0635\u064A\u0635 \u0643\u0644 \u0639\u0646\u0635\u0631 \u0641\u064A \u0627\u0644\u0645\u062C\u062A\u0645\u0639 \u0644\u0637\u0628\u0642\u0629 \u0648\u0627\u062D\u062F\u0629 \u0641\u0642\u0637. \u062B\u0645 \u064A\u062A\u0645 \u062A\u0637\u0628\u064A\u0642 \u0639\u064A\u0646\u0627\u062A \u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0628\u0633\u064A\u0637\u0629 \u062F\u0627\u062E\u0644 \u0643\u0644 \u0637\u0628\u0642\u0629. \u0627\u0644\u0647\u062F\u0641 \u0647\u0648 \u062A\u062D\u0633\u064A\u0646 \u062F\u0642\u0629 \u0627\u0644\u0639\u064A\u0646\u0629 \u0639\u0646 \u0637\u0631\u064A\u0642 \u062A\u0642\u0644\u064A\u0644 \u062E\u0637\u0623 \u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A . \u064A\u0645\u0643\u0646 \u0623\u0646 \u064A\u0646\u062A\u062C \u0645\u062A\u0648\u0633\u0637 \u0645\u0631\u062C\u062D \u0623\u0642\u0644 \u062A\u0646\u0648\u0639\u064B\u0627 \u0645\u0646 \u0627\u0644\u0645\u062A\u0648\u0633\u0637 \u0627\u0644\u062D\u0633\u0627\u0628\u064A \u0644\u0639\u064A\u0646\u0629 \u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0628\u0633\u064A\u0637\u0629 \u0645\u0646 \u0627\u0644\u0645\u062C\u062A\u0645\u0639."@ar . . "\u0623\u062E\u0630 \u0627\u0644\u0639\u064A\u0646\u0627\u062A \u0627\u0644\u0637\u0628\u0642\u064A\u0629"@ar . . . "L'allocazione ottima di Neyman, usata in statistica nell'ambito del campionamento statistico, \u00E8 un'allocazione delle unit\u00E0 in un disegno stratificato sviluppata indipendentemente da Aleksandr \u010Cuprov nel 1920 e Neyman nel 1934. La ripartizione degli elementi campionari negli strati considera sia la numerosit\u00E0 che la variabilit\u00E0 di ogni strato, a differenza dell'allocazione proporzionale che tiene conto della sola numerosit\u00E0 di strato. Se si indica con Nh la numerosit\u00E0 dello strato h e con Sh la deviazione standard nello strato stesso e con n il totale degli elementi campionati si ha:"@it . "\u5C64\u5316\u62BD\u51FA\u6CD5\uFF08\u305D\u3046\u304B\u3061\u3085\u3046\u3057\u3085\u3064\u307B\u3046\u3001\u82F1: stratified sampling\uFF09\u3068\u306F\u3001\u7D71\u8A08\u5B66\u306B\u304A\u3051\u308B\u6BCD\u96C6\u56E3\u304B\u3089\u306E\u6A19\u672C\u8ABF\u67FB\u306E\u624B\u6CD5\u306E\u3072\u3068\u3064\u3002"@ja . "Allocazione di Neyman"@it . . "10727"^^ . . "1124611692"^^ . . . . . . "La mostra estratificada o mostreig estratificat \u00E9s una forma de representaci\u00F3 estad\u00EDstica que mostra com es comporta una caracter\u00EDstica o variable en una poblaci\u00F3 a trav\u00E9s de fer evident el canvi d'aquesta variable en sub-poblacions o estrats. \u00C9s un m\u00E8tode de mostreig d'una poblaci\u00F3. Consisteix en la divisi\u00F3 pr\u00E8via de la poblaci\u00F3 d'estudi en grups o classes que es suposen homogenis respecte a la caracter\u00EDstica a estudiar i que no s'ensolapin. Segons la quantitat d'elements de la mostra que s'han d'elegir de cadascun dels estrats, hi ha dues t\u00E8cniques de mostreig estratificat:"@ca . . . . . . . "El muestreo estratificado es una forma de representaci\u00F3n estad\u00EDstica que muestra c\u00F3mo se comporta una caracter\u00EDstica o variable en una poblaci\u00F3n a trav\u00E9s de hacer evidente el cambio de dicha variable en subpoblaciones o estratos en los que se ha dividido.Consiste en la divisi\u00F3n previa de la poblaci\u00F3n de estudio en grupos o clases que se suponen homog\u00E9neos respecto a caracter\u00EDstica a estudiar y que no se solapen. Seg\u00FAn la cantidad de elementos de la muestra que se han de elegir de cada uno de los estratos, existen dos t\u00E9cnicas de muestreo estratificado:"@es . . "Muestreo estratificado"@es . . . . . "27596"^^ . "\uD1B5\uACC4\uD559\uC5D0\uC11C, \uCE35\uD654\uD45C\uC9D1(\u5C64\u5316\u6A19\u96C6, Stratified sampling)\uC740 \uBAA8\uC9D1\uB2E8\uC744 \uBA3C\uC800 \uC911\uBCF5\uB418\uC9C0 \uC54A\uB3C4\uB85D \uCE35\uC73C\uB85C \uB098\uB208 \uB2E4\uC74C \uAC01 \uCE35\uC5D0\uC11C \uD45C\uBCF8\uC744 \uCD94\uCD9C\uD558\uB294 \uBC29\uBC95\uC774\uB2E4. \uCE35\uC744 \uB098\uB20C \uB54C \uCE35\uB0B4\uB294 \uB3D9\uC9C8\uC801(homogeneous), \uCE35\uAC04\uC740 \uC774\uC9C8\uC801(heterogeneous) \uD2B9\uC131\uC744 \uAC00\uC9C0\uB3C4\uB85D \uD558\uBA74 \uC801\uC740 \uBE44\uC6A9\uC73C\uB85C \uB354 \uC815\uD655\uD55C \uCD94\uC815\uC744 \uD560 \uC218 \uC788\uC73C\uBA70, \uC804\uCCB4 \uBAA8\uC9D1\uB2E8\uBFD0\uB9CC \uC544\uB2C8\uB77C \uAC01 \uCE35\uC758 \uD2B9\uC131\uC5D0 \uB300\uD55C \uCD94\uC815\uB3C4 \uD560 \uC218 \uC788\uB2E4\uB294 \uC7A5\uC810\uC774 \uC788\uB2E4. \uAC01 \uCE35\uC73C\uB85C\uBD80\uD130 \uD45C\uBCF8\uC744 \uCD94\uCD9C\uD560 \uB54C \uB2E8\uC21C\uC784\uC758 \uCD94\uCD9C\uBC29\uBC95\uC744 \uC4F8 \uC218\uB3C4 \uC788\uACE0 \uACC4\uD1B5\uD45C\uC9D1(systematic sampling) \uB4F1 \uB2E4\uB978 \uCD94\uCD9C\uBC29\uBC95\uC744 \uC4F8 \uC218\uB3C4 \uC788\uB2E4. \uB610 \uD544\uC694\uC5D0 \uB530\uB77C \uAC01 \uCE35\uC744 \uB2E4\uC2DC \uD558\uC704\uCE35\uC73C\uB85C \uB098\uB204\uC5B4 \uCD94\uCD9C\uD558\uB294 \uB2E4\uB2E8\uACC4 \uCE35\uD654 \uCD94\uCD9C\uC744 \uD558\uAE30\uB3C4 \uD55C\uB2E4."@ko . . . "\u5C64\u5316\u62BD\u51FA\u6CD5"@ja . "In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. In computational statistics, stratified sampling is a method of variance reduction when Monte Carlo methods are used to estimate population statistics from a known population."@en . "L'allocazione ottima di Neyman, usata in statistica nell'ambito del campionamento statistico, \u00E8 un'allocazione delle unit\u00E0 in un disegno stratificato sviluppata indipendentemente da Aleksandr \u010Cuprov nel 1920 e Neyman nel 1934. La ripartizione degli elementi campionari negli strati considera sia la numerosit\u00E0 che la variabilit\u00E0 di ogni strato, a differenza dell'allocazione proporzionale che tiene conto della sola numerosit\u00E0 di strato. Se si indica con Nh la numerosit\u00E0 dello strato h e con Sh la deviazione standard nello strato stesso e con n il totale degli elementi campionati si ha: con nh la numerosit\u00E0 campionaria dello strato h-esimo. Naturalmente la somma degli nh sar\u00E0 pari a ."@it . . "\u5206\u5C42\u62BD\u6837"@zh . "En statistique, un \u00E9chantillonnage stratifi\u00E9 est une m\u00E9thode d'\u00E9chantillonnage \u00E0 partir d'une population. Dans un sondage, lorsque des sous-populations varient au sein d'une population g\u00E9n\u00E9rale, il peut \u00EAtre avantageux de s\u00E9lectionner un \u00E9chantillon au sein de chaque sous-population (ou strates)."@fr . . . . . "\u0420\u043E\u0437\u0448\u0430\u0440\u043E\u0432\u0430\u043D\u0430 \u0432\u0438\u0431\u0456\u0440\u043A\u0430, \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043A\u043E\u0432\u0430\u043D\u0430 \u043F\u0440\u043E\u0431\u0430 (\u0430\u043D\u0433\u043B. stratified sample) \u2014 \u043F\u0440\u043E\u0431\u0430, \u0449\u043E \u0441\u043A\u043B\u0430\u0434\u0430\u0454\u0442\u044C\u0441\u044F \u0437 \u043F\u043E\u0440\u0446\u0456\u0439, \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u0438\u0445 \u0437 \u0456\u0434\u0435\u043D\u0442\u0438\u0447\u043D\u0438\u0445 \u0441\u0443\u0431\u0447\u0430\u0441\u0442\u0438\u043D (\u0441\u0442\u0440\u0430\u0442) \u0440\u043E\u0434\u043E\u043D\u0430\u0447\u0430\u043B\u044C\u043D\u043E\u0457 \u0441\u0443\u043A\u0443\u043F\u043D\u043E\u0441\u0442\u0456. \u0417 \u043A\u043E\u0436\u043D\u043E\u0457 \u0441\u0443\u0431\u0447\u0430\u0441\u0442\u0438\u043D\u0438 \u043F\u0440\u043E\u0431\u0438 \u0432\u0456\u0434\u0431\u0438\u0440\u0430\u044E\u0442\u044C\u0441\u044F \u0434\u043E\u0432\u0456\u043B\u044C\u043D\u043E. \u0417\u0430\u0432\u0434\u0430\u043D\u043D\u044F\u043C \u0432\u0437\u044F\u0442\u0442\u044F \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043A\u043E\u0432\u0430\u043D\u0438\u0445 \u043F\u0440\u043E\u0431 \u0454 \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u043D\u044F \u0431\u0456\u043B\u044C\u0448 \u0440\u0435\u043F\u0440\u0435\u0437\u0435\u043D\u0442\u0430\u0442\u0438\u0432\u043D\u043E\u0433\u043E \u0437\u0440\u0430\u0437\u043A\u0430, \u043D\u0456\u0436 \u0442\u043E\u0439, \u0449\u043E \u0431\u0435\u0440\u0435\u0442\u044C\u0441\u044F \u0437\u0430 \u043C\u0435\u0442\u043E\u0434\u0438\u043A\u043E\u044E \u0432\u0438\u043F\u0430\u0434\u043A\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0431\u043E\u0440\u0443 \u043F\u0440\u043E\u0431. \u0420\u043E\u0437\u0448\u0430\u0440\u043E\u0432\u0430\u043D\u0430 \u0432\u0438\u0431\u0456\u0440\u043A\u0430 \u0441\u043A\u043B\u0430\u0434\u0430\u0454\u0442\u044C\u0441\u044F \u0437 \u0440\u0456\u0437\u043D\u0438\u0445 \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0456\u0432 \u043F\u043E\u043F\u0443\u043B\u044F\u0446\u0456\u0457, \u043D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, \u0440\u043E\u0431\u043B\u044F\u0447\u0438 \u0432\u0438\u0431\u0456\u0440\u043A\u0438 \u0443 \u0440\u0456\u0437\u043D\u0438\u0445 \u0432\u0456\u043A\u043E\u0432\u0438\u0445 \u0433\u0440\u0443\u043F\u0430\u0445. \u0420\u043E\u0437\u043C\u0456\u0440 \u0432\u0438\u0431\u0456\u0440\u043A\u0438 \u0434\u043B\u044F \u043A\u043E\u0436\u043D\u043E\u0433\u043E \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0443 \u043F\u0440\u043E\u043F\u043E\u0440\u0446\u0456\u0439\u043D\u0438\u0439 \u0440\u043E\u0437\u043C\u0456\u0440\u0443 \u0446\u044C\u043E\u0433\u043E \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0443. \u0412\u0430\u0436\u043B\u0438\u0432\u043E, \u0449\u043E\u0431 \u043F\u0440\u043E\u0448\u0430\u0440\u043A\u0438 \u043D\u0435 \u043F\u0435\u0440\u0435\u0442\u0438\u043D\u0430\u043B\u0438\u0441\u044C."@uk . "Em estat\u00EDstica, a amostragem estratificada \u00E9 um m\u00E9todo de amostragem de uma popula\u00E7\u00E3o que pode ser dividida em subpopula\u00E7\u00F5es. Em pesquisas estat\u00EDsticas, quando as subpopula\u00E7\u00F5es dentro de uma popula\u00E7\u00E3o geral variam, pode ser vantajoso amostrar cada subpopula\u00E7\u00E3o (estrato) independentemente. A estratifica\u00E7\u00E3o \u00E9 o processo de dividir os membros da popula\u00E7\u00E3o em subgrupos homog\u00EAneos antes da amostragem. Os estratos devem definir uma parti\u00E7\u00E3o da popula\u00E7\u00E3o. Ou seja, deve ser coletivamente exaustivo e mutuamente exclusivo: cada elemento da popula\u00E7\u00E3o deve ser atribu\u00EDdo a um e apenas um estrato. Em seguida, a amostragem aleat\u00F3ria simples \u00E9 aplicada dentro de cada estrato. O objetivo \u00E9 melhorar a precis\u00E3o da amostra reduzindo o erro amostral. Pode produzir uma m\u00E9dia ponderada que tem menos variabilidad"@pt .