égale – Traduction – Dictionnaire Keybot

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  Puissance statistique p...  
H0 : L'augmentation du R² est égale à 0 / Ha : L'augmentation du R² est différente de 0.
H0: Increase in R² is equal to 0 / Ha: Increase in R² is different from 0.
  Puissance statistique p...  
P1 (probabilité alternative) : La probabilité que X1 soit égale à une fois son écart-type au-dessus de sa moyenne, sachant que les autres variables explicatives sont à leur moyenne.
P1 (alternative probability): The probability that X1 be equal to one standard error above its mean value, all other explanatory variables being at their mean value.
P1 (alternative probability): The probability that X1 be equal to one standard error above its mean value, all other explanatory variables being at their mean value.
  Approche PLS | Logiciel...  
La première étape de l'algorithme PLS consiste en le choix arbitraire d'un vecteur de poids externes initiaux. Ces poids sont standardisés de façon à obtenir une variable latente de variance égale à 1.
The starting step of the PLS algorithm consists in beginning with an arbitrary vector of weights wjh. These weights are then standardized in order to obtain latent variables with unitary variance.
重みの初期値の良い選択は,wjh = sign(cor(xjh, xh)) ,または,より単純に,wjh = sign(cor(xjh, xh)) をとることであり,ただし h = 1 および 0 ,さもなければ,各ブロックのPCAからの第1固有ベクトルの要素であり得る.
  Régression logistique p...  
a1=0 : le paramètre correspondant à la première modalité est nul. Ce choix permet d'imposer que l'effet de la première modalité correspond à un standard. Dans ce cas, la constante du modèle est égale à la moyenne de la variable dépendante pour le groupe 1.
Predictions and residuals table: The predictions and residuals table shows, for each observation, its weight, the value of the qualitative explanatory variable, if there is only one, the observed value of the dependent variable, the model's prediction, the same values divided by the weights, the standardized residuals and a confidence interval.
  Régression logistique p...  
an=0 : le paramètre correspondant à la dernière modalité est nul. Ce choix permet d'imposer que l'effet de la dernière modalité correspond à un standard. Dans ce cas, la constante du modèle est égale à la moyenne de la variable dépendante pour le groupe g.
Classification table: Activate this option to display the table showing the percentage of well-classified observations for both categories. If a validation sample has been extracted, this table is also displayed for the validation data.
  Temps-Intensité | Logic...  
Surface : l’aire totale sous la courbe, égale à la somme de la surface avant et après le pic.
Area: the total area under the curve, equal to the sum of Area before and Area after.
Area: the total area under the curve, equal to the sum of Area before and Area after.
Area: the total area under the curve, equal to the sum of Area before and Area after.
Area: the total area under the curve, equal to the sum of Area before and Area after.
  Régression logistique p...  
Somme(ai)=0 : la somme des paramètres est nulle. Ce choix permet d'imposer que la constante du modèle est égale à la moyenne de la variable dépendante lorsque l'ANOVA est équilibrée.
ROC curve: The ROC curve is used to evaluate the performance of the model by means of the area under the curve (AUC) and to compare several models together (see the description section for more details).
  Tables actuarielles de ...  
De même, on fait l'hypothèse que la censure est indépendante : soient deux individus pris au hasard, inclus dans l'étude au temps t-1 ; si l'un deux est censuré au temps t, alors leur chance de survie est égale au temps t.
The life table method requires that the observations are independent. Second, the censoring must be independent: if you consider two random individuals in the study at time t-1, if one of the individuals is censored at time t, and if the other survives, then both must have equal chances to survive at time t. There are four different types of independent censoring:
  Modèles de régression s...  
Spécifiquement, pour chaque variable, la statistique de Wald teste les hypothèses que chaque estimation de paramètre dans cet ensemble est égale à zéro (pour les variables nominales, l’ensemble inclut un paramètre par catégorie).
Wald: Wald statistics are provided in the output to assess the statistical significance of the set of parameter estimates associated with a given variable. Specifically, for each variable, the Wald statistic tests the restriction that each of the parameter estimates in that set equals zero (for variables specified as Nominal, the set includes parameters for each category of the variable). For Regression models, by default, two Wald statistics (Wald, Wald(=)) are provided in the table when more than 1 class has been estimated. For each set of parameter estimates, the Wald(=) statistic considers the subset associated with each class and tests the restriction that each parameter in that subset equals the corresponding parameter in the subsets associated with each of the other classes. That is, the Wald(=) statistic tests the equality of each set of regression effects across classes.
  Aide Multicritère à  la...  
Tableaux de classement après analyse de sensibilité sur le seuil de discordance : dans ces tableaux sont affichés les classements finaux des actions obtenus avec la méthode Electre 1 pour un seuil de concordance donné par l’utilisateur (sinon égale à sa valeur par défaut 1) et le seuil de discordance modifié.
Ranking tables of the discordance threshold sensitivity analysis: This result displays 2 tables with the final rank of actions obtained with Electre 1 using a modified concordance threshold and a discordance threshold fixed by the user (or set to the default value 0). The left table is the result with a 10% increase of the user value and the right table is the result with a 10% decrease.
  Modèle logit ordinal po...  
a1=0 : le paramètre correspondant à la première modalité est nul. Ce choix permet d'imposer que l'effet de la première modalité correspond à un standard. Dans ce cas, la constante du modèle est égale à la moyenne de la variable dépendante pour le groupe 1.
Classification table: Activate this option to display the table showing the percentage of well-classified observations for both categories. If a validation sample has been extracted, this table is also displayed for the validation data.
Classification table: Activate this option to display the table showing the percentage of well-classified observations for both categories. If a validation sample has been extracted, this table is also displayed for the validation data.
  Aide Multicritère à  la...  
Tableaux de classement après analyse de sensibilité sur le seuil de concordance : dans ces tableaux sont affichés les classements finaux des actions obtenus avec la méthode Electre 1 pour un seuil de concordance modifié et le seuil de discordance donné par l’utilisateur (sinon égale à sa valeur par défaut 0).
Ranking tables of the concordance threshold sensitivity analysis: This result displays 2 tables with the final rank of actions obtained with Electre 1 using a modified concordance threshold and a discordance threshold fixed to the user value (or set to the default value 0). The left table is the result with a 10% increase of the user value and the right table is the result with a 10% decrease.
  Modèle logit ordinal po...  
an=0 : le paramètre correspondant à la dernière modalité est nul. Ce choix permet d'imposer que l'effet de la dernière modalité correspond à un standard. Dans ce cas, la constante du modèle est égale à la moyenne de la variable dépendante pour le groupe g.
Comparison of the categories of the qualitative variables: If one or more explanatory qualitative variables have been selected, the results of the equality tests for the parameters taken in pairs from the different qualitative variable categories are displayed.
Comparison of the categories of the qualitative variables: If one or more explanatory qualitative variables have been selected, the results of the equality tests for the parameters taken in pairs from the different qualitative variable categories are displayed.
  Régression des doubles ...  
Dans le cas où la constante du modèle n’est pas fixée à une valeur donnée, le pouvoir explicatif est évalué en comparant l’ajustement (au sens des moindres carrés) du modèle final avec l’ajustement du modèle rudimentaire composé d’une constante égale à la moyenne de la variable dépendante.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.
  Analyse Kaplan-Meier | ...  
De même, on fait l'hypothèse que la censure est indépendante : soient deux individus pris au hasard, inclus dans l'étude au temps t-1 ; si l'un deux est censuré au temps t, alors leur chance de survie est égale au temps t.
The Kaplan-Meier method requires that the observations are independent. Second, the censoring must be independent: if you consider two random individuals in the study at time t-1, if one of the individuals is censored at time t, and if the other survives, then both must have equal chances to survive at time t. There are four different types of independent censoring:
  Régression des doubles ...  
Dans le cas où la constante du modèle n’est pas fixée à une valeur donnée, le pouvoir explicatif est évalué en comparant l’ajustement (au sens des moindres carrés) du modèle final avec l’ajustement du modèle rudimentaire composé d’une constante égale à la moyenne de la variable dépendante.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.
Analysis of variance table: It is used to evaluate the explanatory power of the explanatory variables. Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards least squares) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable. Where the constant of the model is set, the comparison is made with respect to the model for which the dependent variable is equal to the constant which has been set.