Sentence examples for temperature regression from inspiring English sources

The gain of shivering was the slope of oxygen consumption versus core temperature regression.

The gain of shivering was determined by the slope of oxygen consumption vs core temperature regression.

The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t 0 and the final time t f) inside which makes temperature regression analysis possible.

In each temperature regression model, the variance inflation factor (VIF) was estimated for all temperature IMFs and a VIF value of 10 or greater is considered to be an indication of significant collinearity, and the R square was determined to estimate the variance in daily headache incidence that could be explained by the identified IMFs.

In the final model, remaining predictors that were statistically significant were: a) temperature (regression coefficient = −4.6, p < 0.001), and b) smoking (regression coefficient = 2.7, p = 0.010).

The final regression model, which omitted these statistically non-significant predictors revealed: a) both remaining predictors to be statistically significant: smoking regression coefficient = 1.1, p = 0.022; temperature regression coefficient = −3.4, (p = 0.002; and b) model R-squared = 0.315 (p < 0.0001).

The slopes of the high and low temperature regressions were nearly identical, with the relative proportions of total saturated FAs in TAGs decreasing by 0.1%/degree of latitude.

Mean temperature estimates depend on the slope (m) and intercept (b) of the δprecip-temperature regression line, and δpore ice values corrected for the first ice enrichment effect (εice-water; 5.8‰ and 0.8‰ for δDpore ice and δ18Opore ice, respectively; Supplementary Note 2).

e, Low response diversity was inferred because the biomass of most species decreased or was unaffected by temperature (linear regression between temperature and species biomass; n = 972 species biomass × temperature observations).

Regarding the direction of association between headache and temperature, the regression models showed that headache incidence had the inverse correlations with temperature IMFs, either for temperature sensitive patients during winter (IMF 5: r = −0.523, p < 0.001) and spring (IMF 6: r = −0.310, p = 0.003), and for temperature non-sensitive patients during summer (IMF 5: r = −0.384, p < 0.001).

This approach controls for confounding by month and season by design, and permits adjustment for potential confounding by temperature through regression modeling.