When mutual associations between the significant predicting variables were analysed with regression analysis, depressive symptoms were predicted by a stronger Anger-in trait, lower level of perceived emotional/informational support, presence of adjuvant treatment, and lower education.
Next, stepwise multivariate linear regression analysis was applied to determine and model the association between the target and predicting variables using SPSS 15.0.
aWith partial diagrams the association between the target variable and a specific predicting variable is investigated without including the effect of the other predicting variables on the target variable.
Due to its cross-sectional nature of the study, it is difficult to establish causal relationship between the dependent and predicting variables.
The residual scatterplots were examined to check the assumptions of normality, linearity and homoscedasticity between the predicted dependent variable scores and errors of prediction, and the assumptions were deemed to be satisfied.
Correlation coefficients between predicting variables and naming latency are shown in Table 3.
Correlation was conducted to assess the strength of linear relationships between numerical variables The unadjusted odds ratio (OR) of the association between predict variables and the outcome (major depression) variable with 95% confidence interval (CI) was calculated.
Furthermore, the linear dependency between the C storage and all predicting variables were investigated visually by partial diagrams.
The coefficients of determination (R2) between the predicted and observed variables including stand density, mean DBH (diameter at breast height), the quadratic mean DBH, total volume and merchantable volume as well as belowground, aboveground, and total biomass ranged from 0.50 to 0.88 (n = 20, P < 0.001) with the exception of mean tree height (R2 = 0.25, n = 20, P < 0.05).
