Barely Significant

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marginally significantp < 0.1002.0× alphagold
After constructing the full model containing all the predictors listed above and their interactions, we simplified the model utilizing a stepwise backwards elimination procedure based on the significance level ( p < 0.050) of the predictors, in each step dropping the predictor with the highest p value, until reaching the minimal adequate model containing only significant or marginally significant ( p < 0.100) effects.
showed a trendp = 0.12.0× alpha
In contrast, the MF females could be discriminated using Total Odour and Richness (pDFA, correctly cross-classified: 75%, expected to be correctly cross-classified: 51%, p = 0.036) and showed a trend towards being distinguishable using MDS1 and MDS2 (pDFA, correctly cross-classified: 48%, expected to be correctly cross-classified: 32%, p = 0.1).
likely to be significantP < 0.12.0× alphagold
For multiple logistic analysis, we incorporated several important factors that were likely to be significant (at P < 0.1) and that avoided multicollinearity, as follows: elderly patients (> 65 years old), visit to the health care facility after 24 h, presence of fever, absence of abdominal tenderness, elevated CRP (above 10 mg/L), night shift, first visit to a teaching hospital, unavailability of urgent radiology reports, first visit to the clinic, and examination by a non-generalist.
near-significantp < 0.102.0× alphagold
Only three bivariate linear models yielded near-significant ( p < 0.10) relationships with number of trees, and these models still yielded significance with these predictors: mean distance to nearest town ( p = 0.006; R 2 = 0.523), distance to nearest trail ( p = 0.014; R 2 = 0.476), and mean wind speed ( p = 0.006; R 2 = 0.517).