Examples of 'logistic regression models' in a sentence
Meaning of "logistic regression models"
Logistic regression models are statistical models used to analyze the relationship between a categorical dependent variable and one or more independent variables. It is commonly used in predictive modeling and determining the probability of occurrence of an event. This phrase is used in the context of data analysis and statistical modeling
How to use "logistic regression models" in a sentence
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logistic regression models
They are binary logistic regression models.
Logistic regression models were developed in each exposure group.
We applied multinomial logistic regression models separately by sex.
Logistic regression models were also used.
Univariate and multivariate logistic regression models were used.
Three logistic regression models were elaborated.
All adjustments were the same as logistic regression models.
Logistic regression models were run with all these variables.
Statistical analysis included multiple logistic regression models.
Multiple logistic regression models were built to investigate these associations.
The quantification of association was measured using logistic regression models.
Each of the logistic regression models were developed in three stages.
Linear regression models were estimated with logistic regression models.
Logistic regression models were used to assess the associations of interest.
The multivariate analysis was performed using logistic regression models.
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Logistic regression models.
The quantification of this association was measured by logistic regression models.
Logistic regression models were used considering mortality as the dependent variable.
Age was entered as a continuous variable in the logistic regression models.
Multivariable logistic regression models were also used to distinguish the groups.
These people were therefore not represented in the logistic regression models.
Different independent logistic regression models were developed for men and women.
These variables were subsequently entered stepwise into logistic regression models.
Logistic regression models were used to identify factors associated with the outcomes.
This was accomplished by estimating logistic regression models of the turndown process.
Logistic regression models were used to analyze the associations between these variables.
The multivariable analysis consisted of logistic regression models with calculations of adjusted ORs.
Logistic regression models are used to compare the odds of consulting alternative practitioners.
Conditional and unconditional logistic regression models were run and the results were similar.
Logistic regression models showed no significant relationships between pneumoconiosis and symptoms.
To investigate the risk factors were used logistic regression models in single and multiple approaches.
Logistic regression models were applied to identify potential factors influencing the groups in combination.
Five of the studies used multivariate logistic regression models to account for these confounders.
Three logistic regression models were performed to identify factors associated to adhesion to home program.
The second stage was based on bivariate logistic regression models with weighting and design effect.
The logistic regression models the probability of plans being defined contribution based on certain characteristics.
The association between chronic conditions and disability was analyzed through the logistic regression models.
Markers were fit with logistic regression models to generate classifiers to generate a model.
The correlates of DG and gambling types were examined with logistic regression models.
We used logistic regression models to explore the relationship between death and independent variables.
Statistical analyses were based on linear and logistic regression models adjusted for potential confounding factors.
Logistic regression models included only independent variables not collinear to the confounding variable.
Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models.
Another way to interpret logistic regression models is to convert the coefficients into odds ratios.
Table 4 presents the adjustment of two logistic regression models.
Linear and logistic regression models were used for continuous and categorical outcomes respectively.
Table 3 shows the multiple logistic regression models.
Ordinal logistic regression models have shown to be suitable for analyzing data with ordinal response.
For example, data can be generated based on logistic regression models.
Multinomial logistic regression models were used to predict the diagnostic group with these independent variables.
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