Examples of using Logistic regression models in English and their translations into Spanish
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Descriptive analyses were conducted and logistic regression models were adjusted.
We used logistic regression models to predict the presence of a BCG scar.
With this, it was determined to carry out three logistic regression models.
Multivariate logistic regression models were applied to calculate the odds ratios for the delay.
Sample application for creating and fitting Logistic Regression models.
Multiple logistic regression models were used to determine the variables associated with mortality.
The data was analyzed using Fisher's exact test and logistic regression models.
Logistic regression models were constructed to assess the risk for mineral deficiencies.
GLMs with this setup are logistic regression models(or logit models). .
The data were analysed by use of 2x2 contingency tables and logistic regression models.
Logistic regression models were constructed, adjusted for gender, age, educational level and employment status.
The association between vaccination andcovariates was evaluate by logistic regression models.
It has been estimated using binary logistic regression models and ordered logistic regression models. .
Prevalence was calculated and associations were established by binary logistic regression models.
Logistic regression models were adjusted, obtaining adjusted odds ratio and their confidence intervals(95%).
Each animal is scored as protected or not andtables of correlation based on logistic regression models are established between antibody titre and clinical protection.
Logistic regression models, they can be used to estimate the probabilities of things like lightning striking, and I can apply them to these circumstances.
We estimate the association of respiratory mortality and air pollution levels using conditional logistic regression models with further adjustment for the year of the study and time.
The 2004 LAPOP study applied logistic regression models to study the characteristics of the people who attend municipal meetings and petition local authorities.
The most typical link function is the canonical logit link: g( p) ln( p 1- p).{\displaystyle g(p)=\ln\left({p\over 1-p}\right).}GLMs with this setup are logistic regression models or logit models.
In order to investigate the characteristics of the victims of the conflict,we constructed three logistic regression models, one for each of the forms of victimization loss, displacement, and exile of a family member.
Analyses using logistic regression models show this risk increases for ageing mothers, women delivering in private centres, those who attended no delivery preparation course, residents of Southern Italy, and, needless to say, those experiencing a troubled pregnancy.
Mendoza(2015) offered insights into emigration patterns from Mexico City by comparing households with and without emigrants; her logistic regression models reveal the importance of social networks in motivating departures.
Coefficients of the logistic regression models were exponentiated to estimate the percent increase in risk of death related to a 20 ppb increase in ozone ambient levels(1-hr maximum and maximum of 8-hr moving average) and 20 µg/m3 in PM10 ambient levels 24-hr average.
In 2006, introducing tolerance of corruption as a binary variable(none/some tolerance), the logistic regression models gave a similar result Basic model: Cox and Snell's r2= .064; Political model, Cox and Snell's r2= .068.
Y' is the output of the logistic regression model for a particular example.
The logistic regression model explained 50.3% of the variation in the data.
Since it is a dichotomous variable,we used a logistic regression model.
A binary logistic regression model was made for the multivariate analysis.
Table VI.3 Logistic Regression Model with the Perception of Public Insecurity(Neighborhood Unsafe; Country Unsafe) as the Dependent Variable B Sig.