Examples of using Logistic regression in English and their translations into Hungarian
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In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.
In this case, Blumenstock used logistic regression with 10-fold cross-validation, but he could have used a
Odds ratio and p-value were obtained from a logistic regression model adjusted for baseline ECOG Performance Score(0 versus 1).
In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical
Odds ratio and p-value were obtained from a logistic regression model adjusted for baseline ECOG Performance Score(0 versus 1).
A values presented for GIOTRIF vs. chemotherapy, p-value based on logistic regression b p-value for time to deterioration based on stratified log-rank test.
IMP24011: p-values compared efalizumab with placebo using logistic regression including baseline PASI score,
Using linear and logistic regression analyses, they showed that there was a positive association between meat consumption and obesity.15.
Wald p-values are quoted for the comparison of treatments using logistic regression with factors for treatment and region.
on stratified log-rank test; p-value for Objective Response Rate based on logistic regression.
p-value for Objective Response Rate based on logistic regression.
The researchers used logistic regression models to compare the risks of mental
InStat also does not perform logistic regression, stepwise multiple regression,
In addition, results from logistic regression analyses of data from patients in the phase 1 trial,
Logistic regression revealed a statistically significant association of the anti-angiogenic effect to nintedanib exposure.
In fact, the odds ratio has much more common use in statistics, since logistic regression, often associated with clinical trials,
I already ran a multivariable logistic regression analysis, and we can go a long way towards closing the gap on the risk level if Melissa loses 10% to 15% of her weight.
such as linear regression or logistic regression.
Commonly used models in the GLiM family include binary logistic regression[6] for binary
Health and multivariate logistic regression and longitudinal analysis strategies.