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The values of Y TP obtained experimentally were analysed by multiple regression and after removal of the non-significant factors (p > 0.05), and the theoretical model was as follows: Y_{text{TP}} left( {{text{mg GAE g}}^{ - 1} {text{dw}}} right) = 3.08 + 0.09t + 0.061T 100.061T 10
The influence of prognostic factors was analysed by multiple regression and logistic regression.
The data will be analysed by multiple regression modelling, fitting baseline measures of outcomes as covariates.
The data for staff turnover and sick leave were analysed by multiple regression analyses with the independent variables of time, professions, units and interactions with time.
Mean score values were compared, and response variables were analysed by multiple regression to explore the relation between the ratings and seniority, type of hospital, type of specialty, and effect of attendance to courses in learning and teaching among respondents.
C-peptide, proinsulin, blood glucose change, insulin dose, HbA1c and IDAA1c were analysed by multiple regression with age, sex, and antibody status (negative/positive) as explanatory variables in a compound symmetric repeated measurement model.
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Patterns of inflammatory markers per clinical diagnosis and factors associated with eosinophilia were analyzed by multiple regression analyses, the differences expressed in odds ratios (OR) with 95% confidence intervals.
The data were analysed by multiple linear regression and partial least squares regression analysis.
The correlations of parameters of carotid lesions with each other were analysed by multiple linear regression and by binary logistic regression.
The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models.
Associations between responses and optimism were analysed by multiple linear regression.
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