Examples of using Regression model in English and their translations into Dutch
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Colloquial
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Medicine
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Computer
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Ecclesiastic
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Official/political
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Programming
Logistic regression models.
Estimated based on a random coefficient regression model.
Build and evaluate regression models generated from big data.
fitting Logistic Regression models.
Logistic regression model adjusted for randomisation stratification variables.
Vælg‘lineær' under regression model.
A- Logistic regression model adjusted for randomisation stratification variables.
In that case these redundant X columns should be omitted from the regression model.
A multivariable Poisson regression model was derived, containing five determinants.
we will use a regression model.
How do you make a multiple regression model for extreme or strongly correlating data?
Tom Aldenberg(RIVM) has discussed predictive uncertainty of QSAR regression models.
Steyerberg has developed advanced regression models and other statistical prediction techniques.
P-value from Cox regression model, stratifying for site of disease
Data were analyzed using stepwise multiple linear regression models(significance, P<0.05).
You can define your own regression models, or choose from more than 350 predefined 2D/3D models. .
heteroskedasticity tests on) every possible regression model mixing a group of independent variables and transformations.
Dynamic Regression is a regression model that includes lagged values of explanatory variables
were processed in an encompassing multiple regression model, in which time lags of zero to four year were incorporated.
Also fits a multiple linear regression model for comparison purposes, and performs chi-square tests
you break your leg on Thursday afternoon, a regression model would predict that you would still go to the cinema that evening.
An exploratory multivariate Cox regression model using backward selection indicated that the following baseline prognostic factors were strongly associated with survival independent of treatment: gender,
but custom regression models may also be defined by the user.
Data can be modeled using a toolbox of linear regression models, nonlinear regression models, smoothing methods, or various kinds of splines.
including the use of copula models, regression models that are based on e.g. various kinds of dimension reduction approaches,
Cox proportional hazards regression model, principles of longitudinal data analysis.