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- Logistic_regression abstract "In statistics, logistic regression or logit regression is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features). That is, it is used in estimating empirical values of the parameters in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and subsequently in this article) "logistic regression" is used to refer specifically to the problem in which the dependent variable is binary—that is, the number of available categories is two—and problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.Logistic regression measures the relationship between a categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable. As such it treats the same set of problems as does probit regression using similar techniques.".
- Logistic_regression wikiPageExternalLink ref=sr_1_2?ie=UTF8&qid=1339171287&sr=8-2.
- Logistic_regression wikiPageExternalLink logistic-regression-analysis.html.
- Logistic_regression wikiPageExternalLink logisticregression.htm.
- Logistic_regression wikiPageExternalLink ?Tag=logistic+regression.
- Logistic_regression wikiPageID "226631".
- Logistic_regression wikiPageRevisionID "606443185".
- Logistic_regression hasPhotoCollection Logistic_regression.
- Logistic_regression id "48.0".
- Logistic_regression title "Econometrics Lecture".
- Logistic_regression subject Category:Categorical_data.
- Logistic_regression subject Category:Log-linear_models.
- Logistic_regression type Abstraction100002137.
- Logistic_regression type CategoricalData.
- Logistic_regression type Cognition100023271.
- Logistic_regression type Datum105816622.
- Logistic_regression type Information105816287.
- Logistic_regression type PsychologicalFeature100023100.
- Logistic_regression comment "In statistics, logistic regression or logit regression is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features). That is, it is used in estimating empirical values of the parameters in a qualitative response model.".
- Logistic_regression label "Logistic regression".
- Logistic_regression label "Logistische Regression".
- Logistic_regression label "Logit模型".
- Logistic_regression label "Regresión logística".
- Logistic_regression label "Regresja logistyczna".
- Logistic_regression label "Regressione logistica".
- Logistic_regression label "Regressão logística".
- Logistic_regression label "Régression logistique".
- Logistic_regression label "Логистическая регрессия".
- Logistic_regression label "انحدار لوجستي".
- Logistic_regression label "ロジスティック回帰".
- Logistic_regression sameAs Logistická_regrese.
- Logistic_regression sameAs Logistische_Regression.
- Logistic_regression sameAs Regresión_logística.
- Logistic_regression sameAs Erregresio_logistiko.
- Logistic_regression sameAs Régression_logistique.
- Logistic_regression sameAs Regresi_logistik.
- Logistic_regression sameAs Regressione_logistica.
- Logistic_regression sameAs ロジスティック回帰.
- Logistic_regression sameAs Regresja_logistyczna.
- Logistic_regression sameAs Regressão_logística.
- Logistic_regression sameAs m.01h161.
- Logistic_regression sameAs Q1132755.
- Logistic_regression sameAs Q1132755.
- Logistic_regression sameAs Logistic_regression.
- Logistic_regression wasDerivedFrom Logistic_regression?oldid=606443185.
- Logistic_regression isPrimaryTopicOf Logistic_regression.