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Confounding may occur when a variable is associated with both the predictor variable of interest and the dependent variable.
This may have restricted the variance in both the predictor variable (living arrangement) and in the outcome variable (antidepressant use), thus underestimating their effects.
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The overall multiple regression of the density of the butterflies on their range position and dispersal ability was highly significant (F 2,76 = 38.84, P < 0.001; r2 = 0.51), and both the predictor variables had independently negative effects on butterfly density (Table 4).
The overall multiple linear regression was highly significant (F 2,76 = 121.90, P < 0.001, r2 = 0.76), and both of the predictor variables had an independent positive effect on the distribution of the butterflies (Table 2).
The relationship between distribution of the butterflies and both of the predictor variables are shown in figures 2 and 3. To study the effect of niche breadth on the distribution of the butterflies, we analysed the effects of larval specificity and adult habitat breadth on butterfly distribution using analysis of variance (ANOVA) and Tukey's multiple comparisons test.
To calculate a decision curve in the absence of outcome data, the user specifies the predicted probability from the model as both the outcome and the predictor variable.
For both utilization and expenditures, the predictor variable of interest was the presence or absence of diagnosed minor depression.
We used logistic regression to statistically model student performance on these exam questions, with comparison or intervention group as the predictor variable for both the overall performance and question-by-question performance.
The VLPFC label was selected as the predictor variable after viewing the results of the preceding analyses.
The predictor variable having highest correlation coefficient has been chosen as 'super predictor variable'.
The predictor variable was the surface of the dental implant.
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