Exemplos de uso de Simple logistic em Inglês e suas traduções para o Português
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Colloquial
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Medicine
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
All variables were tested by simple logistic regression model.
In the simple logistic regression analyses, the older age at menarche was a protection factor against PCOS.
In the second analysis, the multiple and simple logistic regression models were constructed.
Simple logistic regression was performed for each of the socioeconomic, work-related, and stress-related variables independent variables.
For analysis it was used a statistical program, which has simple logistic regression.
Table 2 describes the results of the simple logistic regression of the associations between obesity and selected variables.
Table 1 summarizes the RF identified in the soccer practitioners anddescribes the results of the simple logistic regression between RF and the groups G1/G2.
For the descriptive analysis, a simple logistic regression model was used for variables previously selected as predictors of the combined event.
For the statistical analysis, univariate models of conditional simple logistic regression11 were first adjusted, with a 1.
Simple logistic regression was used to evaluate the association between PCOS dependent variable and the risk factors evaluated in this study independent variables.
A descriptive analysis was performed,and complemented by simple logistic regression of variables previously selected as independent variables.
To assess the strength of the relationship between the variables considered andthe probability of rejection pm1, simple logistic regression was performed.
To calculate the odds ratio we conducted simple logistic regression models(variables with p-value¿0.25 were elected) and multiple p.
The adjusted odds ratio was obtained in the multiple regression model that included other variables associated with readmission,chosen based on simple logistic regression analysis.
The discriminant markers selected with the help of k-means and simple logistic algorithms were age, weight, body mass index, systolic blood pressure.
The odds ratio OR of each variable of interest with the use of alcohol and tobacco was estimated with therespective 95% confidence intervals, using simple logistic regression.
This was evidenced through the adjustment of a simple logistic regression model, in which the log-transformed tIgE value was the explanatory variable.
Simple logistic regression analysis indicated that women, individuals above 60 years of age, those with health insurance, those having higher education, and those with higher income were more likely p.
The relationship betweenthe menopausal status and MS was observed in a simple logistic regression model, with the odds ratio and its confidence interval being estimated at 95.
In a simple logistic regression for adherence rate, with the professional category as factor, we found that there was no significant difference between physician and nursing assistant. However, they differed significantly p.
In this work,the toolbox, open source,"weka" was employed, using the simple k-means and simple logistic algorithms, for non-glycemic variables rating.
The chi-squared test and simple logistic regression was used to confirm the results for construction to the model, ajusted by multiple logistic regression.
Analysis of association between explanatory variables and outcome was performed with simple logistic regression and multiple logistic regression with hierarchical model.
In the quantitative phase, simple logistic regressions were estimated in order to identify the main socioeconomic and cultural factors affecting the propensity to evasion.
In inferential statistics information two bivaried and multivariate hypothesis testing was applied,with the use of simple logistic regression(oddis unadjusted ratio) and multiple logistic regression(oddis ratio adjusted).
The data in Table 2 refer to the simple logistic regression analysis of variables with a p value below 10% and which were selected for the multiple logistic regression analysis.
To evaluate the variation in the proportion of patients with positive sIgE andSPT with respect to tIgE, a simple logistic regression model was used, in which the log-transformed tIgE value was the explanatory variable.
Univariate analysis using simple logistic regression was used to check the association of self-rated health with each CVD, highlighting the p values for each category of self-perception.
In order to verify associations between obesity and sociodemographic variables, eating habits and nutritional knowledge, odds ratio andtheir respective confidence intervals were calculated by means of simple logistic regression.
The quantification of these associations was measured using simple logistic regression models, calculating the crude Odds ratio OR with respective confidence intervals of 95.