Examples of using Logistic regression in English and their translations into Slovak
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For basic logistic regression.
On discriminative Bayesian Network Classifiers and Logistic Regression.
Based on a logistic regression.
A values presented for GIOTRIF vs. erlotinib, p-value based on logistic regression.
By bivariant logistic regression.
All the factors that were significant in univariate analysis were further analyzed by multiple logistic regression.
A binary logistic regression.
Logistic regression revealed a statistically significant association between nintedanib exposure and DCE-MRI response.
Szitás: Predictions in neural networks and logistic regression(in Slovak).
Linear and Logistic regression are the most basic form of regression which are commonly used.
We constructed propensity scores using a logistic regression model.
Logistic regression revealed a statistically significant association of the anti-angiogenic effect to nintedanib exposure.
Univariate and multivariate logistic regression analyses were applied.
But if I can have the next slide, hereinstead of the proportional hazard model, we did a logistic regression model.
The analysis was performed using a logistic regression model with treatment as the only factor.
Of the respondents, 166(74 men and 92 women)went out by themselves and their responses were subjected to logistic regression analysis.
The essential difference between these two is that Logistic regression is used when the dependent variable is binary in nature.
Verstraeten: Page 142:“But if I can have the next slide,here instead of the proportional hazard model, we did a logistic regression model.
In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.
It supports L2-regularized classifiers L2-loss linear SVM, L1-loss linear SVM, and logistic regression(LR) L1-regularized classifiers after version 1.
In this case, Blumenstock used logistic regression with 10-fold cross-validation, but he could have used a variety of other statistical or machine learning approaches.
Build predictive models using techniques such as linear regression, generalized linear modeling, logistic regression and classification trees.
Odds ratio and p-value were obtained from a logistic regression model adjusted for baseline ECOG Performance Score(0 versus 1).
Build predictive models using a variety of techniques- linear regression, generalized linear modeling, logistic regression, classification trees, etc.
The essential difference between linear and logistic regression is that Logistic regression is used when the dependent variable is binary in nature.
Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates.
A p-values compared efalizumab with placebo using logistic regression including baseline PASI score, prior treatment for psoriasis and geographical region as covariates.
In this case, Blumenstock used logistic regression with 10-fold cross-validation, but he could have used a variety of other statistical or machine learning approaches.
IMP24011: p-values compared efalizumab with placebo using logistic regression including baseline PASI score, prior treatment for psoriasis and geographical region as covariates.
In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.