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Cumulative CO2 C emission from unamended and amended soils was related to soil properties by stepwise regression models.
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Potential associative co-factors were examined by multivariate stepwise regression models to predict the dependent variable (change in pain, function, and working capacity).
The association of these variables with climate, topography, landscape, human settlement, and geographic position was explored by means of stepwise regression models applied on the axes obtained from principal component analysis.
Interaction and setting effects on surimi alginate gels were investigated by d-optional mixture design and analyzed by linear and nonlinear manual or stepwise regression models.
Results from stepwise regression models stratified by sex and race/ethnicity are also shown in Table 3. Predictors in men and women varied slightly: Only the category of obesity (body mass index ≥30) was associated with significantly lower concentrations in men; a linear association with body mass index was observed among women.
Forward stepwise regression models were chosen by retaining terms whose statistical significance persisted in the presence of other significant effects.
Multivariate logistic stepwise regression models were used.
Six stepwise regression models were built.
Age, PCOS, waist-to-hip ratio, ferritin and free testosterone concentrations were excluded by the stepwise regression model.
Irrespective of selection by the stepwise regression model we forced sex, age, body-mass index, socioeconomic status, type of diabetes, glycated haemoglobin levels, statin use and blood leukocyte counts into the models.
Only amino acids that were retained by the stepwise regression model are shown in the last two columns.
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