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An observational analytic study of 60 diabetic type 2. Systematized alcatory sampling, applying a multiple logistical regression analysis (JMP of the SAS Institute program).
A design of a multiparametric risk model of microvascular events in the patient diabetic type 2 with arterial hypertension by means of analysis of multiple logistical regression that allows to discriminate against the variables associated to a bigger risk and the most susceptible individuals to present this type of complications.
For the multiple logistical regression models, gender, age, mental health score and emotional role function were significant predictors of vitality in all four models.
Statistical analyses were performed using multiple logistical regression.
Finally, we performed multiple logistical regression analysis to calculate the influence of every single independent variable on mortality.
As behaviour is a dichotomous variable (yes/no), multiple logistical regression was applied in order to predict which factors influence patient behaviour.
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To assess the association between risk factors and COPD, linear regression and simple and multiple logistical regressions will be used according to the nature of the response variables.
We conducted multiple logistical regressions on dichotomous outcomes adjusted for maternal age (<20, 20-24, 30-34, 30-34, ≥35), calendar time (1996-8, 1999-2001, 2002-3), marital status (unmarried, married, divorced, widow), income (three categories), and smoking (no/yes).
T-test, X2 and multiple logistic regression.
Statistical analysis included multiple logistic regression models.
Multiple logistic regression analyses were performed.
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