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The soil temperature univariate model explained approximately 60% of the variations in soil respiration (Table 2).
Also, the other model explained approximately 43 % of the variability in the 85th-percentile outside lane operating speeds.
Krueger and Brazeal (1994) confirms that three components of this model explained approximately 50percentt of the variance in entrepreneurial intentions.
This model presented a residual standard error equal to 0.0392 on 104 degrees of freedom, the coefficient standard error was 0.00347, and the R2 was 0.975, which means the factors and interactions in the model explained approximately 98% of the variation in y ∗ ̂.
Our model explained approximately 20% of the variance (adjusted R2 = 0.20) in annual citation rates of our cohort.
The first component of the PLS-DA model explained approximately 64% of the variation, and the second component explained an additional 35% of the variation.
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When we combine our variables in one model to explain the frequency of Skattefunn projects in a particular region (OLS regression analysis), our best model explains approximately 29% of the observed variations (Table 4).
The value of adjusted R2 (coefficient of determination) reveals that the estimated model explains approximately 99% of the variance of the dependent variable (OHIP Total Score).
The squared multiple correlations of the dependent variable indicate that the model explains approximately 50% of the variation (r =.504) in 1-year EWL.
In our analysis of the impact of cost on gene expression, multivariate regression models explained approximately 40% of variation in transcript and protein levels (Table 2).
The results of the multivariate analyses of the main effects are depicted in Table 3. Overall, the final logistic models explained approximately 18 %, 14, and 11% of the vandance in mood disorders, suicide attempts and anxiety disorders, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com