. . . . . . "In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination). Formally, in binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled \"0\" and \"1\", while the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled \"1\" can vary between 0 (certainly the value \"0\") and 1 (certainly the value \"1\"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. See and for formal mathematics, and for a worked example. Binary variables are widely used in statistics to model the probability of a certain class or event taking place, such as the probability of a team winning, of a patient being healthy, etc. (see ), and the logistic model has been the most commonly used model for binary regression since about 1970. Binary variables can be generalized to categorical variables when there are more than two possible values (e.g. whether an image is of a cat, dog, lion, etc.), and the binary logistic regression generalized to multinomial logistic regression. If the multiple categories are ordered, one can use the ordinal logistic regression (for example the proportional odds ordinal logistic model). See for further extensions. The logistic regression model itself simply models probability of output in terms of input and does not perform statistical classification (it is not a classifier), though it can be used to make a classifier, for instance by choosing a cutoff value and classifying inputs with probability greater than the cutoff as one class, below the cutoff as the other; this is a common way to make a binary classifier. Analogous linear models for binary variables with a different sigmoid function instead of the logistic function (to convert the linear combination to a probability) can also be used, most notably the probit model; see . The defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio. More abstractly, the logistic function is the natural parameter for the Bernoulli distribution, and in this sense is the \"simplest\" way to convert a real number to a probability. In particular, it maximizes entropy (minimizes added information), and in this sense makes the fewest assumptions of the data being modeled; see . The parameters of a logistic regression are most commonly estimated by maximum-likelihood estimation (MLE). This does not have a closed-form expression, unlike linear least squares; see . Logistic regression by MLE plays a similarly basic role for binary or categorical responses as linear regression by ordinary least squares (OLS) plays for scalar responses: it is a simple, well-analyzed baseline model; see for discussion. The logistic regression as a general statistical model was originally developed and popularized primarily by Joseph Berkson, beginning in , where he coined \"logit\"; see ."@en . "Regressi\u00F3 log\u00EDstica"@ca . "\u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A (\u0628\u0627\u0644\u0625\u0646\u062C\u0644\u064A\u0632\u064A\u0629 Logistic regression) \u0647\u0648 \u0646\u0645\u0648\u0630\u062C \u0625\u062D\u0635\u0627\u0626\u064A \u064A\u0646\u062A\u0645\u064A \u0644\u0646\u0645\u0627\u0630\u062C \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u062E\u0637\u064A \u064A\u0645\u0643\u0646 \u0645\u0646 \u0646\u0645\u0630\u062C\u0629 \u0645\u062A\u063A\u064A\u0631 \u062B\u0646\u0627\u0626\u064A \u0627\u0644\u062D\u062F \u0628\u062F\u0644\u0627\u0644\u0629 \u0645\u062C\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0645\u062A\u063A\u064A\u0631\u0627\u062A \u0627\u0644\u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0627\u0644\u0645\u062A\u0648\u0642\u0639\u0629\u060C \u0631\u0642\u0645\u064A\u0629 \u0643\u0627\u0646\u062A \u0623\u0648 \u0641\u0626\u0648\u064A\u0629. \u064A\u0633\u062A\u062E\u062F\u0645 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0644\u0644\u062A\u0646\u0628\u0624 \u0648\u0642\u0648\u0639 \u062D\u062F\u062B \u0645\u0627 \u0628\u0645\u0639\u0631\u0641\u0629 \u0625\u0636\u0627\u0641\u064A\u0629 \u0644\u0642\u064A\u0645 \u0645\u062A\u063A\u064A\u0631\u0627\u062A \u064A\u0645\u0643\u0646 \u0623\u0646 \u062A\u0643\u0648\u0646 \u0645\u0641\u0633\u0631\u0629 \u0623\u0648 \u0645\u0631\u062A\u0628\u0637\u0629 \u0628\u0647\u0630\u0627 \u0627\u0644\u062D\u062F\u062B. \u064A\u0633\u062A\u062E\u062F\u0645 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631\u064F \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0639\u062F\u0629 \u0645\u062A\u063A\u064A\u0631\u0627\u062A \u0645\u064F\u062A\u0648\u0642\u064E\u0651\u0639\u0629 \u0648\u0627\u0644\u062A\u064A \u064A\u0645\u0643\u0646 \u0623\u0646 \u062A\u0643\u0648\u0646 \u0631\u0642\u0645\u064A\u0629 \u0623\u0648 \u0641\u0626\u0648\u064A\u0629. \u064A\u0634\u062A\u0647\u0631 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0623\u064A\u0636\u0627 \u0628\u062A\u0633\u0645\u064A\u0627\u062A \u0646\u0645\u0648\u0630\u062C \u0644\u0648\u062C\u064A\u062A (Logit) \u0623\u0648 \u0627\u0644\u0645\u0635\u0646\u0641 \u0627\u0644\u0639\u0627\u0645 \u0644\u0644\u0623\u0646\u062A\u0631\u0648\u0628\u064A\u0629. \u062A\u0633\u062A\u0639\u0645\u0644 \u0647\u0630\u0647 \u0627\u0644\u0646\u0645\u0630\u062C\u0629 \u0628\u0634\u0643\u0644 \u0648\u0627\u0633\u0639 \u0641\u064A \u0627\u0644\u0639\u062F\u064A\u062F \u0645\u0646 \u0627\u0644\u062A\u0637\u0628\u064A\u0642\u0627\u062A \u0627\u0644\u0639\u0644\u0645\u064A\u0629 \u0648\u0627\u0644\u062A\u062C\u0627\u0631\u064A\u0629 \u0648\u0647\u064A \u0645\u0646 \u0637\u0631\u0642 \u0627\u0644\u0646\u0645\u0630\u062C\u0629 \u0627\u0644\u0623\u0643\u062B\u0631 \u062A\u0637\u0628\u064A\u0642\u0627 \u0641\u064A \u0645\u062C\u0627\u0644 \u0627\u0644\u062A\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064A\u060C \u062D\u064A\u062B \u062A\u0635\u0646\u0641 \u0636\u0645\u0646 \u0637\u0631\u0642 \u0627\u0644\u062A\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064A \u0627\u0644\u0645\u0631\u0627\u0642\u0628."@ar . . . . . . "Logistisk regression \u00E4r en matematisk metod med vilken man kan analysera . Metoden l\u00E4mpar sig b\u00E4st d\u00E5 man \u00E4r intresserad av att unders\u00F6ka om det finns ett samband mellan en responsvariabel (Y), som endast kan anta tv\u00E5 m\u00F6jliga v\u00E4rden, och en f\u00F6rklarande variabel (X). Exempel: Man \u00E4r intresserad av att studera om det finns ett samband mellan m\u00E4ngden tj\u00E4ra i lungorna (X) och huruvida lungcancer f\u00F6religger (Y). Responsvariabeln kan endast anta de tv\u00E5 v\u00E4rdena 'Ja' eller 'Nej', medan den f\u00F6rklarande variabeln (i princip) kan anta vilka positiva v\u00E4rden som helst. Det \u00E4r inte meningsfullt att f\u00F6rs\u00F6ka beskriva ett eventuellt samband mellan X och Y p\u00E5 en linj\u00E4r form, s\u00E5 som \u00E4r brukligt vid enkel linj\u00E4r regression: Anledningen till detta \u00E4r att uttrycket representerar ett reellt tal, medan v\u00E4nsterledet, Y, endast kan anta tv\u00E5 m\u00F6jliga v\u00E4rden. (Det finns fler reella tal \u00E4n vad som \u00E4r m\u00F6jliga att r\u00E4kna upp; man s\u00E4ger att det finns \u00F6veruppr\u00E4kneligt m\u00E5nga reella tal.) Vi \u00E4r intresserade av ett samband mellan sannolikheten att Y skall anta v\u00E4rdet 'Ja', och den f\u00F6rklarande variabeln X: Eftersom en sannolikhet \u00E4r ett tal som ligger mellan v\u00E4rdena noll och ett, m\u00E5ste funktionen f vara s\u00E5dan att d\u00E5 X \u00E4r ett reellt tal \u00E4r f(X) ett tal mellan noll och ett: I den enkla logistiska regressionsmodellen definieras funktionen f indirekt av f\u00F6ljande samband: Notera att om p \u00E4r ett tal mellan noll och ett, s\u00E5 \u00E4r ett reellt tal: D\u00E5 man j\u00E4mf\u00F6r denna matematiska modell \u00F6ver sambandet mellan X och Y med gjorda m\u00E4tningar p\u00E5 X och noteringar av f\u00F6rekomsten av lungcancer, f\u00E5r man inte en perfekt \u00F6verensst\u00E4mmelse. De avvikelser som noteras kan ha tv\u00E5 orsaker: \n* (1) Den matematiska modellen \u00E4r ol\u00E4mplig och det f\u00F6rekommer slumpeffekter, eller \n* (2) Den matematiska modellen \u00E4r l\u00E4mplig och det f\u00F6rekommer slumpeffekter. Som synes kan man inte bli kvitt slumpeffekterna. Vad man d\u00E4remot kan g\u00F6ra \u00E4r att f\u00F6rs\u00F6ka att beskriva dem genom att unders\u00F6ka deras frekvensfunktion. Den enkla logistiska regressionsmodellen utg\u00E5r fr\u00E5n att avvikelserna mellan uttrycket och \u00E4r best\u00E4mda av den s\u00E5 kallade normalf\u00F6rdelningen, vars f\u00F6rdelningsfunktion \u00E4r: Man s\u00E4ger att avvikelsen, , mellan modell-Y och m\u00E4tdata-Y \u00E4r -f\u00F6rdelad. Den enkla logistiska regressionsmodellen tar h\u00E4nsyn b\u00E5de till sambandet mellan X och Y och till slumpens p\u00E5verkan: Sambandet mellan och X f\u00E5r vi genom att invertera ovanst\u00E5ende ekvation: Det \u00E4r viktigt att notera att slumpeffekterna kommer in multiplikativt i denna modell (som exponenter), till skillnad fr\u00E5n additivt, som vid enkel- och multipel linj\u00E4r regression. Detta g\u00F6r det sv\u00E5rt att best\u00E4mma den frekvensfunktion som styr det slumpm\u00E4ssiga beteendet hos kvoten"@sv . . . "\u041B\u043E\u0433\u0456\u0441\u0442\u0438\u0447\u043D\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044F"@uk . . . . . . . . . . . . . . . . . "A regress\u00E3o log\u00EDstica \u00E9 uma t\u00E9cnica estat\u00EDstica que tem como objetivo produzir, a partir de um conjunto de observa\u00E7\u00F5es, um modelo que permita a predi\u00E7\u00E3o de valores tomados por uma vari\u00E1vel categ\u00F3rica, frequentemente bin\u00E1ria, a partir de uma s\u00E9rie de vari\u00E1veis explicativas cont\u00EDnuas e/ou bin\u00E1rias. A regress\u00E3o log\u00EDstica \u00E9 amplamente usada em ci\u00EAncias m\u00E9dicas e sociais, e tem outras denomina\u00E7\u00F5es, como modelo log\u00EDstico, modelo logit, e classificador de m\u00E1xima entropia. A regress\u00E3o log\u00EDstica \u00E9 utilizada em \u00E1reas como as seguintes: \n* Em medicina, permite por exemplo determinar os factores que caracterizam um grupo de indiv\u00EDduos doentes em rela\u00E7\u00E3o a indiv\u00EDduos s\u00E3os; \n* No dom\u00EDnio dos seguros, permite encontrar frac\u00E7\u00F5es da clientela que sejam sens\u00EDveis a determinada pol\u00EDtica securit\u00E1ria em rela\u00E7\u00E3o a um dado risco particular; \n* Em institui\u00E7\u00F5es financeiras, pode detectar os grupos de risco para a subscri\u00E7\u00E3o de um cr\u00E9dito; \n* Em econometria, permite explicar uma vari\u00E1vel discreta, como por exemplo as inten\u00E7\u00F5es de voto em actos eleitorais. O \u00EAxito da regress\u00E3o log\u00EDstica assenta sobretudo nas numerosas ferramentas que permitem interpretar de modo aprofundado os resultados obtidos. Em compara\u00E7\u00E3o com as t\u00E9cnicas conhecidas em regress\u00E3o, em especial a regress\u00E3o linear, a regress\u00E3o log\u00EDstica distingue-se essencialmente pelo facto de a vari\u00E1vel resposta ser categ\u00F3rica. Enquanto m\u00E9todo de predi\u00E7\u00E3o para vari\u00E1veis categ\u00F3ricas, a regress\u00E3o log\u00EDstica \u00E9 compar\u00E1vel \u00E0s t\u00E9cnicas supervisionadas propostas em aprendizagem autom\u00E1tica (\u00E1rvores de decis\u00E3o, redes neurais, etc.), ou ainda a an\u00E1lise discriminante preditiva em estat\u00EDstica explorat\u00F3ria. \u00C9 poss\u00EDvel de as colocar em concorr\u00EAncia para escolha do modelo mais adaptado para um certo problema preditivo a resolver. Trata-se de um modelo de regress\u00E3o para vari\u00E1veis dependentes ou de resposta binomialmente distribu\u00EDdas. \u00C9 \u00FAtil para modelar a probabilidade de um evento ocorrer como fun\u00E7\u00E3o de outros factores. \u00C9 um modelo linear generalizado que usa como fun\u00E7\u00E3o de liga\u00E7\u00E3o a fun\u00E7\u00E3o logit. Assun\u00E7\u00F5es: \n* Rela\u00E7\u00E3o linear entre o vetor das vari\u00E1veis explicativas X e o logit da vari\u00E1vel resposta Y \n* Aus\u00EAncia de multicolinearidade \n* Valor esperado dos res\u00EDduos igual a zero \n* Aus\u00EAncia de heterocedasticidade N\u00E3o pressup\u00F5e normalidade dos res\u00EDduos nem homogeneidade de vari\u00E2ncias. Por isso torna prefer\u00EDvel em situa\u00E7\u00F5es pr\u00E1ticas."@pt . . "En estad\u00EDstica, la regresi\u00F3n log\u00EDstica es un tipo de an\u00E1lisis de regresi\u00F3n utilizado para predecir el resultado de una variable categ\u00F3rica (una variable que puede adoptar un n\u00FAmero limitado de categor\u00EDas) en funci\u00F3n de las variables independientes o predictoras. Es \u00FAtil para modelar la probabilidad de un evento ocurriendo en funci\u00F3n de otros factores. El an\u00E1lisis de regresi\u00F3n log\u00EDstica se enmarca en el conjunto de Modelos Lineales Generalizados (GLM por sus siglas en ingl\u00E9s) que usa como funci\u00F3n de enlace la funci\u00F3n logit. Las probabilidades que describen el posible resultado de un \u00FAnico ensayo se modelan como una funci\u00F3n de variables explicativas, utilizando una funci\u00F3n log\u00EDstica. La regresi\u00F3n log\u00EDstica es usada extensamente en las ciencias m\u00E9dicas y sociales. Otros nombres para regresi\u00F3n log\u00EDstica usados en varias \u00E1reas de aplicaci\u00F3n incluyen modelo log\u00EDstico, modelo logit, y clasificador de m\u00E1xima entrop\u00EDa."@es . "\u908F\u8F2F\u8FF4\u6B78"@zh . . . . "\uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0(\uC601\uC5B4: logistic regression)\uB294 \uC601\uAD6D\uC758 \uD1B5\uACC4\uD559\uC790\uC778 D. R. Cox\uAC00 1958\uB144\uC5D0 \uC81C\uC548\uD55C \uD655\uB960 \uBAA8\uB378\uB85C\uC11C \uB3C5\uB9BD \uBCC0\uC218\uC758 \uC120\uD615 \uACB0\uD569\uC744 \uC774\uC6A9\uD558\uC5EC \uC0AC\uAC74\uC758 \uBC1C\uC0DD \uAC00\uB2A5\uC131\uC744 \uC608\uCE21\uD558\uB294 \uB370 \uC0AC\uC6A9\uB418\uB294 \uD1B5\uACC4 \uAE30\uBC95\uC774\uB2E4. \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0\uC758 \uBAA9\uC801\uC740 \uC77C\uBC18\uC801\uC778 \uD68C\uADC0 \uBD84\uC11D\uC758 \uBAA9\uD45C\uC640 \uB3D9\uC77C\uD558\uAC8C \uC885\uC18D \uBCC0\uC218\uC640 \uB3C5\uB9BD \uBCC0\uC218\uAC04\uC758 \uAD00\uACC4\uB97C \uAD6C\uCCB4\uC801\uC778 \uD568\uC218\uB85C \uB098\uD0C0\uB0B4\uC5B4 \uD5A5\uD6C4 \uC608\uCE21 \uBAA8\uB378\uC5D0 \uC0AC\uC6A9\uD558\uB294 \uAC83\uC774\uB2E4. \uC774\uB294 \uB3C5\uB9BD \uBCC0\uC218\uC758 \uC120\uD615 \uACB0\uD569\uC73C\uB85C \uC885\uC18D \uBCC0\uC218\uB97C \uC124\uBA85\uD55C\uB2E4\uB294 \uAD00\uC810\uC5D0\uC11C\uB294 \uC120\uD615 \uD68C\uADC0 \uBD84\uC11D\uACFC \uC720\uC0AC\uD558\uB2E4. \uD558\uC9C0\uB9CC \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0\uB294 \uC120\uD615 \uD68C\uADC0 \uBD84\uC11D\uACFC\uB294 \uB2E4\uB974\uAC8C \uC885\uC18D \uBCC0\uC218\uAC00 \uBC94\uC8FC\uD615 \uB370\uC774\uD130\uB97C \uB300\uC0C1\uC73C\uB85C \uD558\uBA70 \uC785\uB825 \uB370\uC774\uD130\uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C \uD574\uB2F9 \uB370\uC774\uD130\uC758 \uACB0\uACFC\uAC00 \uD2B9\uC815 \uBD84\uB958\uB85C \uB098\uB258\uAE30 \uB54C\uBB38\uC5D0 \uC77C\uC885\uC758 \uBD84\uB958 (classification) \uAE30\uBC95\uC73C\uB85C\uB3C4 \uBCFC \uC218 \uC788\uB2E4. \uD754\uD788 \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0\uB294 \uC885\uC18D\uBCC0\uC218\uAC00 \uC774\uD56D\uD615 \uBB38\uC81C(\uC989, \uC720\uD6A8\uD55C \uBC94\uC8FC\uC758 \uAC1C\uC218\uAC00 \uB450\uAC1C\uC778 \uACBD\uC6B0)\uB97C \uC9C0\uCE6D\uD560 \uB54C \uC0AC\uC6A9\uB41C\uB2E4. \uC774\uC678\uC5D0, \uB450 \uAC1C \uC774\uC0C1\uC758 \uBC94\uC8FC\uB97C \uAC00\uC9C0\uB294 \uBB38\uC81C\uAC00 \uB300\uC0C1\uC778 \uACBD\uC6B0\uC5D4 \uB2E4\uD56D \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0 (multinomial logistic regression) \uB610\uB294 \uBD84\uD654 \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0 (polytomous logistic regression)\uB77C\uACE0 \uD558\uACE0 \uBCF5\uC218\uC758 \uBC94\uC8FC\uC774\uBA74\uC11C \uC21C\uC11C\uAC00 \uC874\uC7AC\uD558\uBA74 \uC11C\uC218 \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0 (ordinal logistic regression) \uB77C\uACE0 \uD55C\uB2E4. \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0 \uBD84\uC11D\uC740 \uC758\uB8CC, \uD1B5\uC2E0, \uB370\uC774\uD130\uB9C8\uC774\uB2DD\uACFC \uAC19\uC740 \uB2E4\uC591\uD55C \uBD84\uC57C\uC5D0\uC11C \uBD84\uB958 \uBC0F \uC608\uCE21\uC744 \uC704\uD55C \uBAA8\uB378\uB85C\uC11C \uD3ED\uB113\uAC8C \uC0AC\uC6A9\uB418\uACE0 \uC788\uB2E4."@ko . . . "\uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0(\uC601\uC5B4: logistic regression)\uB294 \uC601\uAD6D\uC758 \uD1B5\uACC4\uD559\uC790\uC778 D. R. Cox\uAC00 1958\uB144\uC5D0 \uC81C\uC548\uD55C \uD655\uB960 \uBAA8\uB378\uB85C\uC11C \uB3C5\uB9BD \uBCC0\uC218\uC758 \uC120\uD615 \uACB0\uD569\uC744 \uC774\uC6A9\uD558\uC5EC \uC0AC\uAC74\uC758 \uBC1C\uC0DD \uAC00\uB2A5\uC131\uC744 \uC608\uCE21\uD558\uB294 \uB370 \uC0AC\uC6A9\uB418\uB294 \uD1B5\uACC4 \uAE30\uBC95\uC774\uB2E4. \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0\uC758 \uBAA9\uC801\uC740 \uC77C\uBC18\uC801\uC778 \uD68C\uADC0 \uBD84\uC11D\uC758 \uBAA9\uD45C\uC640 \uB3D9\uC77C\uD558\uAC8C \uC885\uC18D \uBCC0\uC218\uC640 \uB3C5\uB9BD \uBCC0\uC218\uAC04\uC758 \uAD00\uACC4\uB97C \uAD6C\uCCB4\uC801\uC778 \uD568\uC218\uB85C \uB098\uD0C0\uB0B4\uC5B4 \uD5A5\uD6C4 \uC608\uCE21 \uBAA8\uB378\uC5D0 \uC0AC\uC6A9\uD558\uB294 \uAC83\uC774\uB2E4. \uC774\uB294 \uB3C5\uB9BD \uBCC0\uC218\uC758 \uC120\uD615 \uACB0\uD569\uC73C\uB85C \uC885\uC18D \uBCC0\uC218\uB97C \uC124\uBA85\uD55C\uB2E4\uB294 \uAD00\uC810\uC5D0\uC11C\uB294 \uC120\uD615 \uD68C\uADC0 \uBD84\uC11D\uACFC \uC720\uC0AC\uD558\uB2E4. \uD558\uC9C0\uB9CC \uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0\uB294 \uC120\uD615 \uD68C\uADC0 \uBD84\uC11D\uACFC\uB294 \uB2E4\uB974\uAC8C \uC885\uC18D \uBCC0\uC218\uAC00 \uBC94\uC8FC\uD615 \uB370\uC774\uD130\uB97C \uB300\uC0C1\uC73C\uB85C \uD558\uBA70 \uC785\uB825 \uB370\uC774\uD130\uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C \uD574\uB2F9 \uB370\uC774\uD130\uC758 \uACB0\uACFC\uAC00 \uD2B9\uC815 \uBD84\uB958\uB85C \uB098\uB258\uAE30 \uB54C\uBB38\uC5D0 \uC77C\uC885\uC758 \uBD84\uB958 (classification) \uAE30\uBC95\uC73C\uB85C\uB3C4 \uBCFC \uC218 \uC788\uB2E4."@ko . . . . . . . . . . . . . . . . . . . "Why is there likely some kind of error? How can this be remedied?"@en . . . . . . . . "1124071703"^^ . . . "\u041B\u043E\u0433\u0456\u0441\u0442\u0438\u0447\u043D\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044F (\u0430\u043D\u0433\u043B. logistic regression) \u0430\u0431\u043E \u043B\u043E\u0491\u0456\u0442-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044F (\u0430\u043D\u0433\u043B. logit model) \u2014 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043D\u0438\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043D\u0438\u0439 \u043C\u0435\u0442\u043E\u0434, \u0449\u043E \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u044E\u0442\u044C \u0443 \u0432\u0438\u043F\u0430\u0434\u043A\u0443, \u043A\u043E\u043B\u0438 \u0437\u0430\u043B\u0435\u0436\u043D\u0430 \u0437\u043C\u0456\u043D\u043D\u0430 \u0454 , \u0442\u043E\u0431\u0442\u043E \u043C\u043E\u0436\u0435 \u043D\u0430\u0431\u0443\u0432\u0430\u0442\u0438 \u0442\u0456\u043B\u044C\u043A\u0438 \u0434\u0432\u043E\u0445 \u0437\u043D\u0430\u0447\u0435\u043D\u044C (0 \u0430\u0431\u043E 1). \u041F\u0440\u0438 \u0437\u0430\u043F\u0440\u043E\u0432\u0430\u0434\u0436\u0435\u043D\u043D\u0456 \u043F\u043E\u0440\u043E\u0433\u043E\u0432\u043E\u0433\u043E \u0437\u043D\u0430\u0447\u0435\u043D\u043D\u044F \u043C\u043E\u0436\u0435 \u0437\u043D\u0430\u0445\u043E\u0434\u0438\u0442\u0438 \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F \u0443 \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0443\u0432\u0430\u043D\u043D\u0456."@uk . . . . . . . . . . . . . . "In de statistiek wordt logistische regressie gebruikt om een dichotome uitkomstvariabele te relateren aan een of meer variabelen. Logistische regressieanalyse kan gezien worden als de techniek die het meest bij lineaire regressie aansluit, en is hierbij tevens het alternatief voor lineaire regressie in het geval de gemeten variabele niet continu van aard is (metrisch of ratiomeetniveau). De analysetechniek heeft vaak een voorspellend karakter en wordt voornamelijk toegepast binnen de vakgebieden gezondheidswetenschappen, biologie, macro-economie, financi\u00EBle economie, sociologie en de sociale psychologie."@nl . "May 2017"@en . . . . . . . . . . . . . . . . "In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination). Formally, in binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled \"0\" and \"1\", while the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled \"1\" can vary between 0 (certainly the value \"0\")"@en . . . . . . . . . . . "226631"^^ . . "En estad\u00EDstica, la regresi\u00F3n log\u00EDstica es un tipo de an\u00E1lisis de regresi\u00F3n utilizado para predecir el resultado de una variable categ\u00F3rica (una variable que puede adoptar un n\u00FAmero limitado de categor\u00EDas) en funci\u00F3n de las variables independientes o predictoras. Es \u00FAtil para modelar la probabilidad de un evento ocurriendo en funci\u00F3n de otros factores. El an\u00E1lisis de regresi\u00F3n log\u00EDstica se enmarca en el conjunto de Modelos Lineales Generalizados (GLM por sus siglas en ingl\u00E9s) que usa como funci\u00F3n de enlace la funci\u00F3n logit. Las probabilidades que describen el posible resultado de un \u00FAnico ensayo se modelan como una funci\u00F3n de variables explicativas, utilizando una funci\u00F3n log\u00EDstica."@es . "Regress\u00E3o log\u00EDstica"@pt . "In statistica, il modello logit, noto anche come modello logistico o regressione logistica, \u00E8 un modello di regressione nonlineare utilizzato quando la variabile dipendente \u00E8 di tipo dicotomico. L'obiettivo del modello \u00E8 di stabilire la probabilit\u00E0 con cui un'osservazione pu\u00F2 generare uno o l'altro valore della variabile dipendente; pu\u00F2 inoltre essere utilizzato per classificare le osservazioni, in base alla caratteristiche di queste, in due categorie."@it . . . . . . . . . "Estatistikan, erregresio logistikoa edo logit eredua gertakizun baten probabilitatea aurresateko erabiltzen den erregresio-teknika bat da, aldagai independente zenbaitetan oinarrituta kurba logistiko bat egokituz. Adibidez, erregresio logistikoa pertsona batek aldi batean bihotzekoak jota izateko probabilitatea zenbatesteko erabil daiteke, bere adina, pisua eta errentzen duen jakinda. Alderantziz, probabilitate zehatz baterako, beste aldagai batek hartu behar duen balioa zenbatesteko ere erabil daiteke. Adibidez, gaixotasun bat ez garatzeko probabilitatea %99 izan dadin, sendagai batetik hartu beharreko dosia zein izan behar den kalkula daiteke erregresio logistikoaren bitartez, dosi ezberdinetarako gaixotasun garatu duten pertsonen kopuruari buruzko datuak erabiliz."@eu . . . . . . . . . . . . . . . . . . . . . . "Unter logistischer Regression oder Logit-Modell versteht man in der Statistik Regressionsanalysen zur (meist multiplen) Modellierung der Verteilung abh\u00E4ngiger diskreter Variablen. Wenn logistische Regressionen nicht n\u00E4her als multinomiale oder geordnete logistische Regressionen gekennzeichnet sind, ist zumeist die binomiale logistische Regression f\u00FCr dichotome (bin\u00E4re) abh\u00E4ngige Variablen gemeint. Die unabh\u00E4ngigen Variablen k\u00F6nnen dabei ein beliebiges Skalenniveau aufweisen, wobei diskrete Variablen mit mehr als zwei Auspr\u00E4gungen in eine Serie bin\u00E4rer Dummy-Variablen zerlegt werden."@de . . . . . . "Regresi logistik"@in . . . . . . "Unter logistischer Regression oder Logit-Modell versteht man in der Statistik Regressionsanalysen zur (meist multiplen) Modellierung der Verteilung abh\u00E4ngiger diskreter Variablen. Wenn logistische Regressionen nicht n\u00E4her als multinomiale oder geordnete logistische Regressionen gekennzeichnet sind, ist zumeist die binomiale logistische Regression f\u00FCr dichotome (bin\u00E4re) abh\u00E4ngige Variablen gemeint. Die unabh\u00E4ngigen Variablen k\u00F6nnen dabei ein beliebiges Skalenniveau aufweisen, wobei diskrete Variablen mit mehr als zwei Auspr\u00E4gungen in eine Serie bin\u00E4rer Dummy-Variablen zerlegt werden. Im binomialen Fall liegen Beobachtungen der Art vor, wobei eine bin\u00E4re abh\u00E4ngige Variable (den so genannten Regressanden) bezeichnet, die mit , einem bekannten und festen Kovariablenvektor von Regressoren, auftritt. bezeichnet die Anzahl der Beobachtungen. Das Logit-Modell ergibt sich aus der Annahme, dass die Fehlerterme unabh\u00E4ngig und identisch Gumbel-verteilt sind. Eine Erweiterung der logistischen Regression stellt die dar; eine Variante dieser ist das ."@de . . . . . . . . "Modello logit"@it . . . . . "\uB85C\uC9C0\uC2A4\uD2F1 \uD68C\uADC0"@ko . . . . . . . . . . . . . . . . . . . . "\u041B\u043E\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0430\u044F \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044F \u0438\u043B\u0438 \u043B\u043E\u0433\u0438\u0442-\u043C\u043E\u0434\u0435\u043B\u044C (\u0430\u043D\u0433\u043B. logit model) \u2014 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0430\u044F \u043C\u043E\u0434\u0435\u043B\u044C, \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u0435\u043C\u0430\u044F \u0434\u043B\u044F \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438 \u0432\u043E\u0437\u043D\u0438\u043A\u043D\u043E\u0432\u0435\u043D\u0438\u044F \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0433\u043E \u0441\u043E\u0431\u044B\u0442\u0438\u044F \u043F\u0443\u0442\u0451\u043C \u0435\u0433\u043E \u0441\u0440\u0430\u0432\u043D\u0435\u043D\u0438\u044F \u0441 \u043B\u043E\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u043A\u0440\u0438\u0432\u043E\u0439. \u042D\u0442\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044F \u0432\u044B\u0434\u0430\u0451\u0442 \u043E\u0442\u0432\u0435\u0442 \u0432 \u0432\u0438\u0434\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438 \u0431\u0438\u043D\u0430\u0440\u043D\u043E\u0433\u043E \u0441\u043E\u0431\u044B\u0442\u0438\u044F (1 \u0438\u043B\u0438 0)."@ru . . . . . . . . . . . "\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u56DE\u5E30\uFF08\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u304B\u3044\u304D\u3001\u82F1: Logistic regression\uFF09\u306F\u3001\u30D9\u30EB\u30CC\u30FC\u30A4\u5206\u5E03\u306B\u5F93\u3046\u5909\u6570\u306E\u7D71\u8A08\u7684\u56DE\u5E30\u30E2\u30C7\u30EB\u306E\u4E00\u7A2E\u3067\u3042\u308B\u3002\u9023\u7D50\u95A2\u6570\u3068\u3057\u3066\u30ED\u30B8\u30C3\u30C8\u3092\u4F7F\u7528\u3059\u308B\u4E00\u822C\u5316\u7DDA\u5F62\u30E2\u30C7\u30EB (GLM) \u306E\u4E00\u7A2E\u3067\u3082\u3042\u308B\u30021958\u5E74\u306B\u304C\u767A\u8868\u3057\u305F\u3002\u78BA\u7387\u306E\u56DE\u5E30\u3067\u3042\u308A\u3001\u7D71\u8A08\u5B66\u306E\u5206\u985E\u306B\u4E3B\u306B\u4F7F\u308F\u308C\u308B\u3002\u533B\u5B66\u3084\u793E\u4F1A\u79D1\u5B66\u3067\u3082\u3088\u304F\u4F7F\u308F\u308C\u308B\u3002 \u30E2\u30C7\u30EB\u306F\u540C\u3058\u304F1958\u5E74\u306B\u767A\u8868\u3055\u308C\u305F\u5358\u7D14\u30D1\u30FC\u30BB\u30D7\u30C8\u30ED\u30F3\u3068\u7B49\u4FA1\u3067\u3042\u308B\u304C\u3001scikit-learn\u306A\u3069\u3067\u306F\u3001\u30D1\u30E9\u30E1\u30FC\u30BF\u3092\u6C7A\u3081\u308B\u6700\u9069\u5316\u554F\u984C\u3067\u78BA\u7387\u7684\u52FE\u914D\u964D\u4E0B\u6CD5\u3092\u4F7F\u7528\u3059\u308B\u7269\u3092\u30D1\u30FC\u30BB\u30D7\u30C8\u30ED\u30F3\u3068\u547C\u3073\u3001\u3084\u6E96\u30CB\u30E5\u30FC\u30C8\u30F3\u6CD5\u306A\u3069\u3092\u4F7F\u7528\u3059\u308B\u7269\u3092\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u56DE\u5E30\u3068\u547C\u3093\u3067\u3044\u308B\u3002"@ja . . . . . "\u041B\u043E\u0433\u0456\u0441\u0442\u0438\u0447\u043D\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044F (\u0430\u043D\u0433\u043B. logistic regression) \u0430\u0431\u043E \u043B\u043E\u0491\u0456\u0442-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044F (\u0430\u043D\u0433\u043B. logit model) \u2014 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043D\u0438\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043D\u0438\u0439 \u043C\u0435\u0442\u043E\u0434, \u0449\u043E \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u044E\u0442\u044C \u0443 \u0432\u0438\u043F\u0430\u0434\u043A\u0443, \u043A\u043E\u043B\u0438 \u0437\u0430\u043B\u0435\u0436\u043D\u0430 \u0437\u043C\u0456\u043D\u043D\u0430 \u0454 , \u0442\u043E\u0431\u0442\u043E \u043C\u043E\u0436\u0435 \u043D\u0430\u0431\u0443\u0432\u0430\u0442\u0438 \u0442\u0456\u043B\u044C\u043A\u0438 \u0434\u0432\u043E\u0445 \u0437\u043D\u0430\u0447\u0435\u043D\u044C (0 \u0430\u0431\u043E 1). \u041F\u0440\u0438 \u0437\u0430\u043F\u0440\u043E\u0432\u0430\u0434\u0436\u0435\u043D\u043D\u0456 \u043F\u043E\u0440\u043E\u0433\u043E\u0432\u043E\u0433\u043E \u0437\u043D\u0430\u0447\u0435\u043D\u043D\u044F \u043C\u043E\u0436\u0435 \u0437\u043D\u0430\u0445\u043E\u0434\u0438\u0442\u0438 \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F \u0443 \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0443\u0432\u0430\u043D\u043D\u0456."@uk . . . "Regresi logistik (kadang disebut model logistik atau model ), dalam statistika digunakan untuk prediksi probabilitas kejadian suatu peristiwa dengan mencocokkan data pada fungsi logit . Metode ini merupakan model linier umum yang digunakan untuk . Seperti analisis regresi pada umumnya, metode ini menggunakan beberapa variabel prediktor, baik numerik maupun kategori. Misalnya, probabilitas bahwa orang yang menderita serangan jantung pada waktu tertentu dapat diprediksi dari informasi usia, jenis kelamin, dan indeks massa tubuh. Regresi logistik juga digunakan secara luas pada bidang kedokteran dan ilmu sosial, maupun pemasaran seperti prediksi kecenderungan pelanggan untuk membeli suatu produk atau berhenti berlangganan."@in . . . . . "Logistische regressie"@nl . . . . "\u908F\u8F2F\u65AF\u8FF4\u6B78\uFF08\u82F1\u8A9E\uFF1ALogistic regression\uFF0C\u53C8\u8B6F\u4F5C\u908F\u8F2F\u8FF4\u6B78\u3001\u5BF9\u6570\u51E0\u7387\u8FF4\u5F52\u3001\u7F85\u5409\u65AF\u8FF4\u6B78\uFF09\u662F\u4E00\u7A2E\u5BF9\u6570\u51E0\u7387\u6A21\u578B\uFF08\u82F1\u8A9E\uFF1ALogit model\uFF0C\u53C8\u8BD1\u4F5C\u903B\u8F91\u6A21\u578B\u3001\u8BC4\u5B9A\u6A21\u578B\u3001\u5206\u7C7B\u8BC4\u5B9A\u6A21\u578B\uFF09\uFF0C\u662F\u79BB\u6563\u9009\u62E9\u6CD5\u6A21\u578B\u4E4B\u4E00\uFF0C\u5C5E\u4E8E\u591A\u5143\u53D8\u91CF\u5206\u6790\u8303\u7574\uFF0C\u662F\u793E\u4F1A\u5B66\u3001\u751F\u7269\u7EDF\u8BA1\u5B66\u3001\u4E34\u5E8A\u3001\u3001\u8BA1\u91CF\u7ECF\u6D4E\u5B66\u3001\u5E02\u573A\u8425\u9500\u7B49\u7EDF\u8BA1\u5B9E\u8BC1\u5206\u6790\u7684\u5E38\u7528\u65B9\u6CD5\u3002"@zh . . . . . . . . . . . . "\u041B\u043E\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0430\u044F \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044F"@ru . . . . . . "In statistica, il modello logit, noto anche come modello logistico o regressione logistica, \u00E8 un modello di regressione nonlineare utilizzato quando la variabile dipendente \u00E8 di tipo dicotomico. L'obiettivo del modello \u00E8 di stabilire la probabilit\u00E0 con cui un'osservazione pu\u00F2 generare uno o l'altro valore della variabile dipendente; pu\u00F2 inoltre essere utilizzato per classificare le osservazioni, in base alla caratteristiche di queste, in due categorie. Il modello logit fa parte della classe dei modelli lineari generalizzati, cos\u00EC come il modello probit ed il , dai quali differisce essenzialmente per la scelta della funzione ."@it . . . . "R\u00E9gression logistique"@fr . . . . . . . . "\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u56DE\u5E30\uFF08\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u304B\u3044\u304D\u3001\u82F1: Logistic regression\uFF09\u306F\u3001\u30D9\u30EB\u30CC\u30FC\u30A4\u5206\u5E03\u306B\u5F93\u3046\u5909\u6570\u306E\u7D71\u8A08\u7684\u56DE\u5E30\u30E2\u30C7\u30EB\u306E\u4E00\u7A2E\u3067\u3042\u308B\u3002\u9023\u7D50\u95A2\u6570\u3068\u3057\u3066\u30ED\u30B8\u30C3\u30C8\u3092\u4F7F\u7528\u3059\u308B\u4E00\u822C\u5316\u7DDA\u5F62\u30E2\u30C7\u30EB (GLM) \u306E\u4E00\u7A2E\u3067\u3082\u3042\u308B\u30021958\u5E74\u306B\u304C\u767A\u8868\u3057\u305F\u3002\u78BA\u7387\u306E\u56DE\u5E30\u3067\u3042\u308A\u3001\u7D71\u8A08\u5B66\u306E\u5206\u985E\u306B\u4E3B\u306B\u4F7F\u308F\u308C\u308B\u3002\u533B\u5B66\u3084\u793E\u4F1A\u79D1\u5B66\u3067\u3082\u3088\u304F\u4F7F\u308F\u308C\u308B\u3002 \u30E2\u30C7\u30EB\u306F\u540C\u3058\u304F1958\u5E74\u306B\u767A\u8868\u3055\u308C\u305F\u5358\u7D14\u30D1\u30FC\u30BB\u30D7\u30C8\u30ED\u30F3\u3068\u7B49\u4FA1\u3067\u3042\u308B\u304C\u3001scikit-learn\u306A\u3069\u3067\u306F\u3001\u30D1\u30E9\u30E1\u30FC\u30BF\u3092\u6C7A\u3081\u308B\u6700\u9069\u5316\u554F\u984C\u3067\u78BA\u7387\u7684\u52FE\u914D\u964D\u4E0B\u6CD5\u3092\u4F7F\u7528\u3059\u308B\u7269\u3092\u30D1\u30FC\u30BB\u30D7\u30C8\u30ED\u30F3\u3068\u547C\u3073\u3001\u3084\u6E96\u30CB\u30E5\u30FC\u30C8\u30F3\u6CD5\u306A\u3069\u3092\u4F7F\u7528\u3059\u308B\u7269\u3092\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u56DE\u5E30\u3068\u547C\u3093\u3067\u3044\u308B\u3002"@ja . . . . . . . . . . . "Regresi\u00F3n log\u00EDstica"@es . . . "\u041B\u043E\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0430\u044F \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044F \u0438\u043B\u0438 \u043B\u043E\u0433\u0438\u0442-\u043C\u043E\u0434\u0435\u043B\u044C (\u0430\u043D\u0433\u043B. logit model) \u2014 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0430\u044F \u043C\u043E\u0434\u0435\u043B\u044C, \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u0435\u043C\u0430\u044F \u0434\u043B\u044F \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438 \u0432\u043E\u0437\u043D\u0438\u043A\u043D\u043E\u0432\u0435\u043D\u0438\u044F \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0433\u043E \u0441\u043E\u0431\u044B\u0442\u0438\u044F \u043F\u0443\u0442\u0451\u043C \u0435\u0433\u043E \u0441\u0440\u0430\u0432\u043D\u0435\u043D\u0438\u044F \u0441 \u043B\u043E\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u043A\u0440\u0438\u0432\u043E\u0439. \u042D\u0442\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044F \u0432\u044B\u0434\u0430\u0451\u0442 \u043E\u0442\u0432\u0435\u0442 \u0432 \u0432\u0438\u0434\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438 \u0431\u0438\u043D\u0430\u0440\u043D\u043E\u0433\u043E \u0441\u043E\u0431\u044B\u0442\u0438\u044F (1 \u0438\u043B\u0438 0)."@ru . . "In de statistiek wordt logistische regressie gebruikt om een dichotome uitkomstvariabele te relateren aan een of meer variabelen. Logistische regressieanalyse kan gezien worden als de techniek die het meest bij lineaire regressie aansluit, en is hierbij tevens het alternatief voor lineaire regressie in het geval de gemeten variabele niet continu van aard is (metrisch of ratiomeetniveau)."@nl . . . . . . . . . . . . . . . "Erregresio logistiko"@eu . . . . . . . . . . . . . . . . "En statistiques, la r\u00E9gression logistique ou mod\u00E8le logit est un mod\u00E8le de r\u00E9gression binomiale. Comme pour tous les mod\u00E8les de r\u00E9gression binomiale, il s'agit d'expliquer au mieux une variable binaire (la pr\u00E9sence ou l'absence d'une caract\u00E9ristique donn\u00E9e) par des observations r\u00E9elles nombreuses, gr\u00E2ce \u00E0 un mod\u00E8le math\u00E9matique. En d'autres termes d'associer une variable al\u00E9atoire de Bernoulli (g\u00E9n\u00E9riquement not\u00E9e ) \u00E0 un vecteur de variables al\u00E9atoires . La r\u00E9gression logistique constitue un cas particulier de mod\u00E8le lin\u00E9aire g\u00E9n\u00E9ralis\u00E9. Elle est largement utilis\u00E9e en apprentissage automatique."@fr . . . . "Logistick\u00E1 regrese je ozna\u010Den\u00ED metody matematick\u00E9 statistikyzab\u00FDvaj\u00EDc\u00ED se problematikou odhadu pravd\u011Bpodobnosti n\u011Bjak\u00E9ho jevu (z\u00E1visle prom\u011Bnn\u00E9) na z\u00E1klad\u011B ur\u010Dit\u00FDch zn\u00E1m\u00FDch skute\u010Dnost\u00ED (nez\u00E1visle prom\u011Bnn\u00FDch), kter\u00E9 mohou ovlivnit v\u00FDskyt jevu.Ud\u00E1lost, zda zkouman\u00FD jev nastal, se modeluje pomoc\u00ED n\u00E1hodn\u00E9 veli\u010Diny, kter\u00E1 nab\u00FDv\u00E1 hodnoty 0, pokud jev nenastal, nebo 1, pokud jev nastal (viz t\u00E9\u017E charakteristick\u00E1 funkce). O n\u00E1hodn\u00E9 veli\u010Din\u011B, kter\u00E1 nab\u00FDv\u00E1 dvou hodnot 0 a 1 se \u0159\u00EDk\u00E1, \u017Ee m\u00E1 alternativn\u00ED rozd\u011Blen\u00ED. Metoda logistick\u00E9 regrese p\u0159edpokl\u00E1d\u00E1, \u017Ee za podm\u00EDnek, kter\u00E9 ur\u010Duje vektor , bude n\u00E1hodn\u00E1 veli\u010Dina rovna 1 s pravd\u011Bpodobnost\u00ED, jej\u00ED\u017E z\u00E1vislost na m\u016F\u017Eeme vyj\u00E1d\u0159it pomoc\u00ED tzv. logistick\u00E9 funkce, co\u017E zapisujeme jako Vektor je vektorem nezn\u00E1m\u00FDch parametr\u016F. Odhadem vektoru se tedy odhaduje i hledan\u00E1 pravd\u011Bpodobnost v\u00FDskytu zkouman\u00E9ho jevu (za p\u0159edpokladu parametrizace logistickou funkc\u00ED). Vektor se obvykle bere ve tvaru . Slo\u017Eka pak ur\u010Duje vliv tzv. absolutn\u00EDho \u010Dlenu. Skute\u010Dnost, \u017Ee pravd\u011Bpodobnost v\u00FDskytu jevu nez\u00E1vis\u00ED na n\u00E1mi zkouman\u00FDch nez\u00E1visl\u00FDch prom\u011Bnn\u00FDch (tj. ) znamen\u00E1, \u017Ee se d\u00E1 vyj\u00E1d\u0159it ve tvaru nez\u00E1visle na ."@cs . . . . . . . . . "\u908F\u8F2F\u65AF\u8FF4\u6B78\uFF08\u82F1\u8A9E\uFF1ALogistic regression\uFF0C\u53C8\u8B6F\u4F5C\u908F\u8F2F\u8FF4\u6B78\u3001\u5BF9\u6570\u51E0\u7387\u8FF4\u5F52\u3001\u7F85\u5409\u65AF\u8FF4\u6B78\uFF09\u662F\u4E00\u7A2E\u5BF9\u6570\u51E0\u7387\u6A21\u578B\uFF08\u82F1\u8A9E\uFF1ALogit model\uFF0C\u53C8\u8BD1\u4F5C\u903B\u8F91\u6A21\u578B\u3001\u8BC4\u5B9A\u6A21\u578B\u3001\u5206\u7C7B\u8BC4\u5B9A\u6A21\u578B\uFF09\uFF0C\u662F\u79BB\u6563\u9009\u62E9\u6CD5\u6A21\u578B\u4E4B\u4E00\uFF0C\u5C5E\u4E8E\u591A\u5143\u53D8\u91CF\u5206\u6790\u8303\u7574\uFF0C\u662F\u793E\u4F1A\u5B66\u3001\u751F\u7269\u7EDF\u8BA1\u5B66\u3001\u4E34\u5E8A\u3001\u3001\u8BA1\u91CF\u7ECF\u6D4E\u5B66\u3001\u5E02\u573A\u8425\u9500\u7B49\u7EDF\u8BA1\u5B9E\u8BC1\u5206\u6790\u7684\u5E38\u7528\u65B9\u6CD5\u3002"@zh . . . . . "Regresja logistyczna \u2013 jedna z metod regresji u\u017Cywanych w statystyce w przypadku, gdy zmienna zale\u017Cna jest na skali dychotomicznej (przyjmuje tylko dwie warto\u015Bci). Zmienne niezale\u017Cne w analizie regresji logistycznej mog\u0105 przyjmowa\u0107 charakter nominalny, porz\u0105dkowy, przedzia\u0142owy lub ilorazowy. W przypadku zmiennych nominalnych oraz porz\u0105dkowych nast\u0119puje ich przekodowanie w liczb\u0119 zmiennych zero-jedynkowych tak\u0105 sam\u0105 lub o 1 mniejsz\u0105 ni\u017C liczba kategorii w jej definicji. Formalnie model regresji logistycznej jest uog\u00F3lnionym modelem liniowym (GLM), w kt\u00F3rym u\u017Cyto logitu jako funkcji wi\u0105\u017C\u0105cej."@pl . "Econometrics Lecture"@en . . . . . . . "Estatistikan, erregresio logistikoa edo logit eredua gertakizun baten probabilitatea aurresateko erabiltzen den erregresio-teknika bat da, aldagai independente zenbaitetan oinarrituta kurba logistiko bat egokituz. Adibidez, erregresio logistikoa pertsona batek aldi batean bihotzekoak jota izateko probabilitatea zenbatesteko erabil daiteke, bere adina, pisua eta errentzen duen jakinda. Alderantziz, probabilitate zehatz baterako, beste aldagai batek hartu behar duen balioa zenbatesteko ere erabil daiteke. Adibidez, gaixotasun bat ez garatzeko probabilitatea %99 izan dadin, sendagai batetik hartu beharreko dosia zein izan behar den kalkula daiteke erregresio logistikoaren bitartez, dosi ezberdinetarako gaixotasun garatu duten pertsonen kopuruari buruzko datuak erabiliz."@eu . . . . "\u30ED\u30B8\u30B9\u30C6\u30A3\u30C3\u30AF\u56DE\u5E30"@ja . . "Logistisk regression \u00E4r en matematisk metod med vilken man kan analysera . Metoden l\u00E4mpar sig b\u00E4st d\u00E5 man \u00E4r intresserad av att unders\u00F6ka om det finns ett samband mellan en responsvariabel (Y), som endast kan anta tv\u00E5 m\u00F6jliga v\u00E4rden, och en f\u00F6rklarande variabel (X). Exempel: Man \u00E4r intresserad av att studera om det finns ett samband mellan m\u00E4ngden tj\u00E4ra i lungorna (X) och huruvida lungcancer f\u00F6religger (Y). Responsvariabeln kan endast anta de tv\u00E5 v\u00E4rdena 'Ja' eller 'Nej', medan den f\u00F6rklarande variabeln (i princip) kan anta vilka positiva v\u00E4rden som helst."@sv . . . . . "48.0"^^ . . . . . . . . . . "Logistische Regression"@de . . . . . . . . . "En statistiques, la r\u00E9gression logistique ou mod\u00E8le logit est un mod\u00E8le de r\u00E9gression binomiale. Comme pour tous les mod\u00E8les de r\u00E9gression binomiale, il s'agit d'expliquer au mieux une variable binaire (la pr\u00E9sence ou l'absence d'une caract\u00E9ristique donn\u00E9e) par des observations r\u00E9elles nombreuses, gr\u00E2ce \u00E0 un mod\u00E8le math\u00E9matique. En d'autres termes d'associer une variable al\u00E9atoire de Bernoulli (g\u00E9n\u00E9riquement not\u00E9e ) \u00E0 un vecteur de variables al\u00E9atoires . La r\u00E9gression logistique constitue un cas particulier de mod\u00E8le lin\u00E9aire g\u00E9n\u00E9ralis\u00E9. Elle est largement utilis\u00E9e en apprentissage automatique."@fr . . . . . . . "Logistic regression"@en . "Enkel logistisk regression"@sv . . . . . "En estad\u00EDstica, la regressi\u00F3 log\u00EDstica \u00E9s un model de regressi\u00F3 per a variables dependents o de resposta binomials distribu\u00EFdes. \u00C9s \u00FAtil per a modelar la probabilitat d'un esdeveniment passant com a funci\u00F3 d'altres factors. \u00C9s un que s'utilitza com a funci\u00F3 d'enlla\u00E7 la funci\u00F3 logit. La regressi\u00F3 log\u00EDstica \u00E9s utilitzada extensament en les ci\u00E8ncies m\u00E8diques i socials. Altres noms per regressi\u00F3 log\u00EDstica usats en diverses \u00E0rees d'aplicaci\u00F3 inclouen model log\u00EDstic , model logit , i classificador de m\u00E0xima entropia ."@ca . . . "En estad\u00EDstica, la regressi\u00F3 log\u00EDstica \u00E9s un model de regressi\u00F3 per a variables dependents o de resposta binomials distribu\u00EFdes. \u00C9s \u00FAtil per a modelar la probabilitat d'un esdeveniment passant com a funci\u00F3 d'altres factors. \u00C9s un que s'utilitza com a funci\u00F3 d'enlla\u00E7 la funci\u00F3 logit. La regressi\u00F3 log\u00EDstica \u00E9s utilitzada extensament en les ci\u00E8ncies m\u00E8diques i socials. Altres noms per regressi\u00F3 log\u00EDstica usats en diverses \u00E0rees d'aplicaci\u00F3 inclouen model log\u00EDstic , model logit , i classificador de m\u00E0xima entropia ."@ca . "Regresja logistyczna \u2013 jedna z metod regresji u\u017Cywanych w statystyce w przypadku, gdy zmienna zale\u017Cna jest na skali dychotomicznej (przyjmuje tylko dwie warto\u015Bci). Zmienne niezale\u017Cne w analizie regresji logistycznej mog\u0105 przyjmowa\u0107 charakter nominalny, porz\u0105dkowy, przedzia\u0142owy lub ilorazowy. W przypadku zmiennych nominalnych oraz porz\u0105dkowych nast\u0119puje ich przekodowanie w liczb\u0119 zmiennych zero-jedynkowych tak\u0105 sam\u0105 lub o 1 mniejsz\u0105 ni\u017C liczba kategorii w jej definicji. Zwykle warto\u015Bci zmiennej obja\u015Bnianej wskazuj\u0105 na wyst\u0105pienie, lub brak wyst\u0105pienia pewnego zdarzenia, kt\u00F3re chcemy prognozowa\u0107. Regresja logistyczna pozwala w\u00F3wczas na obliczanie prawdopodobie\u0144stwa tego zdarzenia (tzw. prawdopodobie\u0144stwo sukcesu). Formalnie model regresji logistycznej jest uog\u00F3lnionym modelem liniowym (GLM), w kt\u00F3rym u\u017Cyto logitu jako funkcji wi\u0105\u017C\u0105cej."@pl . . . . . "\u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A (\u0628\u0627\u0644\u0625\u0646\u062C\u0644\u064A\u0632\u064A\u0629 Logistic regression) \u0647\u0648 \u0646\u0645\u0648\u0630\u062C \u0625\u062D\u0635\u0627\u0626\u064A \u064A\u0646\u062A\u0645\u064A \u0644\u0646\u0645\u0627\u0630\u062C \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u062E\u0637\u064A \u064A\u0645\u0643\u0646 \u0645\u0646 \u0646\u0645\u0630\u062C\u0629 \u0645\u062A\u063A\u064A\u0631 \u062B\u0646\u0627\u0626\u064A \u0627\u0644\u062D\u062F \u0628\u062F\u0644\u0627\u0644\u0629 \u0645\u062C\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0645\u062A\u063A\u064A\u0631\u0627\u062A \u0627\u0644\u0639\u0634\u0648\u0627\u0626\u064A\u0629 \u0627\u0644\u0645\u062A\u0648\u0642\u0639\u0629\u060C \u0631\u0642\u0645\u064A\u0629 \u0643\u0627\u0646\u062A \u0623\u0648 \u0641\u0626\u0648\u064A\u0629. \u064A\u0633\u062A\u062E\u062F\u0645 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0644\u0644\u062A\u0646\u0628\u0624 \u0648\u0642\u0648\u0639 \u062D\u062F\u062B \u0645\u0627 \u0628\u0645\u0639\u0631\u0641\u0629 \u0625\u0636\u0627\u0641\u064A\u0629 \u0644\u0642\u064A\u0645 \u0645\u062A\u063A\u064A\u0631\u0627\u062A \u064A\u0645\u0643\u0646 \u0623\u0646 \u062A\u0643\u0648\u0646 \u0645\u0641\u0633\u0631\u0629 \u0623\u0648 \u0645\u0631\u062A\u0628\u0637\u0629 \u0628\u0647\u0630\u0627 \u0627\u0644\u062D\u062F\u062B. \u064A\u0633\u062A\u062E\u062F\u0645 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631\u064F \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0639\u062F\u0629 \u0645\u062A\u063A\u064A\u0631\u0627\u062A \u0645\u064F\u062A\u0648\u0642\u064E\u0651\u0639\u0629 \u0648\u0627\u0644\u062A\u064A \u064A\u0645\u0643\u0646 \u0623\u0646 \u062A\u0643\u0648\u0646 \u0631\u0642\u0645\u064A\u0629 \u0623\u0648 \u0641\u0626\u0648\u064A\u0629. \u064A\u0634\u062A\u0647\u0631 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0623\u064A\u0636\u0627 \u0628\u062A\u0633\u0645\u064A\u0627\u062A \u0646\u0645\u0648\u0630\u062C \u0644\u0648\u062C\u064A\u062A (Logit) \u0623\u0648 \u0627\u0644\u0645\u0635\u0646\u0641 \u0627\u0644\u0639\u0627\u0645 \u0644\u0644\u0623\u0646\u062A\u0631\u0648\u0628\u064A\u0629. \u062A\u0633\u062A\u0639\u0645\u0644 \u0647\u0630\u0647 \u0627\u0644\u0646\u0645\u0630\u062C\u0629 \u0628\u0634\u0643\u0644 \u0648\u0627\u0633\u0639 \u0641\u064A \u0627\u0644\u0639\u062F\u064A\u062F \u0645\u0646 \u0627\u0644\u062A\u0637\u0628\u064A\u0642\u0627\u062A \u0627\u0644\u0639\u0644\u0645\u064A\u0629 \u0648\u0627\u0644\u062A\u062C\u0627\u0631\u064A\u0629 \u0648\u0647\u064A \u0645\u0646 \u0637\u0631\u0642 \u0627\u0644\u0646\u0645\u0630\u062C\u0629 \u0627\u0644\u0623\u0643\u062B\u0631 \u062A\u0637\u0628\u064A\u0642\u0627 \u0641\u064A \u0645\u062C\u0627\u0644 \u0627\u0644\u062A\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064A\u060C \u062D\u064A\u062B \u062A\u0635\u0646\u0641 \u0636\u0645\u0646 \u0637\u0631\u0642 \u0627\u0644\u062A\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064A \u0627\u0644\u0645\u0631\u0627\u0642\u0628. \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A \u0647\u0648 \u062D\u0627\u0644\u0629 \u062E\u0627\u0635\u0629 \u0644\u0645\u062C\u0645\u0648\u0639\u0629 \u0627\u0644\u0646\u0645\u0627\u0630\u062C \u0627\u0644\u062E\u0637\u064A\u0629 \u0627\u0644\u0639\u0627\u0645\u0629\u060C \u0631\u063A\u0645 \u0623\u0646\u0647 \u062A\u0627\u0631\u064A\u062E\u064A\u0627\u060C \u062A\u0639\u062A\u0628\u0631 \u0627\u0644\u0623\u062E\u064A\u0631\u0629 \u062A\u0639\u0645\u064A\u0645\u0627 \u0644\u062A\u0642\u0646\u064A\u0629 \u0627\u0644\u0627\u0646\u062D\u062F\u0627\u0631 \u0627\u0644\u0644\u0648\u062C\u0633\u062A\u064A."@ar . . . . . "Logistick\u00E1 regrese"@cs . "A regress\u00E3o log\u00EDstica \u00E9 uma t\u00E9cnica estat\u00EDstica que tem como objetivo produzir, a partir de um conjunto de observa\u00E7\u00F5es, um modelo que permita a predi\u00E7\u00E3o de valores tomados por uma vari\u00E1vel categ\u00F3rica, frequentemente bin\u00E1ria, a partir de uma s\u00E9rie de vari\u00E1veis explicativas cont\u00EDnuas e/ou bin\u00E1rias. A regress\u00E3o log\u00EDstica \u00E9 amplamente usada em ci\u00EAncias m\u00E9dicas e sociais, e tem outras denomina\u00E7\u00F5es, como modelo log\u00EDstico, modelo logit, e classificador de m\u00E1xima entropia. A regress\u00E3o log\u00EDstica \u00E9 utilizada em \u00E1reas como as seguintes: Assun\u00E7\u00F5es: Por isso torna prefer\u00EDvel em situa\u00E7\u00F5es pr\u00E1ticas."@pt . "Regresja logistyczna"@pl . . . "Logistick\u00E1 regrese je ozna\u010Den\u00ED metody matematick\u00E9 statistikyzab\u00FDvaj\u00EDc\u00ED se problematikou odhadu pravd\u011Bpodobnosti n\u011Bjak\u00E9ho jevu (z\u00E1visle prom\u011Bnn\u00E9) na z\u00E1klad\u011B ur\u010Dit\u00FDch zn\u00E1m\u00FDch skute\u010Dnost\u00ED (nez\u00E1visle prom\u011Bnn\u00FDch), kter\u00E9 mohou ovlivnit v\u00FDskyt jevu.Ud\u00E1lost, zda zkouman\u00FD jev nastal, se modeluje pomoc\u00ED n\u00E1hodn\u00E9 veli\u010Diny, kter\u00E1 nab\u00FDv\u00E1 hodnoty 0, pokud jev nenastal, nebo 1, pokud jev nastal (viz t\u00E9\u017E charakteristick\u00E1 funkce). O n\u00E1hodn\u00E9 veli\u010Din\u011B, kter\u00E1 nab\u00FDv\u00E1 dvou hodnot 0 a 1 se \u0159\u00EDk\u00E1, \u017Ee m\u00E1 alternativn\u00ED rozd\u011Blen\u00ED. Metoda logistick\u00E9 regrese p\u0159edpokl\u00E1d\u00E1, \u017Ee za podm\u00EDnek, kter\u00E9 ur\u010Duje vektor , bude n\u00E1hodn\u00E1 veli\u010Dina rovna 1 s pravd\u011Bpodobnost\u00ED, jej\u00ED\u017E z\u00E1vislost na m\u016F\u017Eeme vyj\u00E1d\u0159it pomoc\u00ED tzv. logistick\u00E9 funkce, co\u017E zapisujeme jako Vektor je vektorem nezn\u00E1m\u00FDch parametr\u016F. Odhadem vektoru se tedy odhaduje i "@cs . . . . . . . . . . "\u0627\u0646\u062D\u062F\u0627\u0631 \u0644\u0648\u062C\u0633\u062A\u064A"@ar . . . . . . . "Regresi logistik (kadang disebut model logistik atau model ), dalam statistika digunakan untuk prediksi probabilitas kejadian suatu peristiwa dengan mencocokkan data pada fungsi logit . Metode ini merupakan model linier umum yang digunakan untuk . Seperti analisis regresi pada umumnya, metode ini menggunakan beberapa variabel prediktor, baik numerik maupun kategori. Misalnya, probabilitas bahwa orang yang menderita serangan jantung pada waktu tertentu dapat diprediksi dari informasi usia, jenis kelamin, dan indeks massa tubuh. Regresi logistik juga digunakan secara luas pada bidang kedokteran dan ilmu sosial, maupun pemasaran seperti prediksi kecenderungan pelanggan untuk membeli suatu produk atau berhenti berlangganan."@in . . . . . . "128172"^^ . . . . . . .