Examples of 'logistic regression model' in a sentence
Meaning of "logistic regression model"
logistic regression model: This phrase is utilized in statistics to describe a mathematical model used for predicting the probability of a categorical outcome based on one or more predictor variables
How to use "logistic regression model" in a sentence
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logistic regression model
The multiple logistic regression model was used.
Case of polytomous covariates in a logistic regression model.
The logistic regression model is build as follows.
You have seen the logistic regression model.
Logistic regression model with these variables.
Expected goals is a logistic regression model.
A logistic regression model was developed to estimate postfire survival.
All variables were tested by simple logistic regression model.
Next a logistic regression model was constructed.
A multivariate analysis was performed using the logistic regression model.
Logistic regression model used for the analysis.
Variables that were statistically significant were included in a logistic regression model.
Logistic regression model was used to determine independent predictors.
Multivariate analysis with a logistic regression model was conducted to adjust ORs.
Logistic regression model adjusted for randomisation stratification variables.
See also
These situations are not captured by the binomial logistic regression model.
The logistic regression model controlled for these variables.
Data were analyzed by the software stata using a logistic regression model.
Ordinal logistic regression model.
The joint effect of all variables was identified by a multivariate logistic regression model.
We used a logistic regression model to evaluate independent risk factors.
This association remained even when analyzed in a multivariate logistic regression model.
Logistic regression model controlled for age and gender.
Below we use the nomreg command to estimate a multinomial logistic regression model.
We performed a logistic regression model to identify independent predictors of inappropriate use.
The individual estimation of the risk of thrombosis is based on a logistic regression model.
A logistic regression model was used for the multivariate association analyses.
Multivariate analysis was performed using a logistic regression model and the stepwise technique.
The logistic regression model was adjusted for age and sex.
Statistically significant variables in univariate analysis were used to adjust the logistic regression model.
A logistic regression model was fitted with year as an independent categorical variable.
Descriptive statistics and binary logistic regression model were employed to analyze the data.
A logistic regression model was also build using the three markers.
The associated factors were computed by the prevalence ratio estimated by logistic regression model.
A binary logistic regression model was employed to analyse the collected data.
Variables significantly related to nutritional status were used in the multiple logistic regression model.
The logistic regression model was used for adjusting potential confounding factors.
The stepwise forward method was used to construct the multiple logistic regression model.
A logistic regression model for positive iln was developed based on clinicopathological features.
The study is quantitative in nature by using descriptive statistics and logistic regression model.
The logistic regression model is estimated with and without bootstrap aggregation.
We also assessed the interactions between some variables in another logistic regression model.
A logistic regression model was developed to validate the performance of lswi indistinguishing flooded areas.
Significant parameters were then entered into the multivariate logistic regression model.
A logistic regression model is used to model the response probability for sampled units.
The analysis was performed using a logistic regression model with treatment as the only factor.
A logistic regression model was used to verify the factors associated with the variable answer.
These were therefore included in the multiple logistic regression model with retrograde variable elimination.
Logistic regression model was developed to study multiple effects which might be involved with pain.
This was intended to select potential risk factors for STDs in the logistic regression model.
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