Sentence examples for multiple regression see from inspiring English sources

In a multiple regression (see Table 2, column 2) only being aged over 64 has a significant (positive) effect on bunching.

Three of these variables remained significant in the final model of the multi-level multiple regression (see table 4).

Of the two variables, phytoestrogen content was the stronger predictor in the sense of being more significant statistically in the multiple regression (see Table 5 for more details).

Performance on the CSA was explored further using multiple regression (see Figure  1), with CSA performance as the dependent variable, and a series of predictors, including PACES performance, BME and CSA type (old vs new) and their interactions.

While both methods rely on combining simple interval methods with multiple regression (see table 1), their implementation in software packages is not identical, which can lead to slightly different results in practice (e.g. [ 15]).

On the other hand, CRP was prominent and present in most terms in the final model when RA/CA was interrogated by multiple regression (see online supplementary table S13) suggesting that innate immune processes may be implicated in the observed opioid-vasculopathy effect.

No multicollinearity was present and the standardised residuals showed a normal distribution (Range = [ 2.92; 3.96]), meeting the important assumptions of normality and multicollinearity underlying multiple linear regression (see Figure S5, in Additional file 1).

To control for all other variables and illustrate the relative influence of each variable on students' incoming acceptance of evolution, we conducted a multiple linear regression (see Table 4 for a summary of coefficients, standard errors, and statistical significance): R 2 = .50 (R = .71); F (6, 169) = 28.50, p < .001 (Adjusted R 2 = .49).49

To control for all other variables and illustrate the relative influence of each variable on student incoming understanding of natural selection, we conducted a multiple linear regression (see Table 5 for a summary of coefficients, standard errors, and statistical significance): R 2 = .24 (R = .49); F (6, 169) = 8.788, p < .001 (Adjusted R 2 = .21).21

That is, ionic strength indeed influences Ca isotope fractionation, supported by " p-values" for the slope of ionic strength (I) = 0 in a multiple linear regression (see Table 3) and calculated AIC values.

No multicollinearity was present and the standardised residuals showed a normal distribution (Range = [−2.92; 3.96]), meeting the important assumptions of normality and multicollinearity underlying multiple linear regression (see Figure S5, in Additional file 1).