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Equation (2) can be easily estimated by using the regression given in Eq. (3).
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These regressions and simple linear regressions were performed using the regression wizard in SigmaPlot 13.0 (Systat).
Correlation between DCs and activity markers were assessed using linear regression, given with the r2 correlation coefficient.
Still, one should be cautious when using multi-task regression given tasks with considerably differing average target values.
The association of density with risk of breast cancer was assessed using logistic regression giving odds ratios per 10% increase in density, and by comparison of continuous mean densities between particular groups of cancers and those who did not develop breast cancer.
As with crop remain age and heterozygosity, bias reduction in genetic distances could be achieved using quantile regression (giving emphasis to long genetic distances by setting τ to a low value).
The air flow upstream from a running spider declines smoothly with distance (Figure 4); a constrained regression, using the function given in (Eq. 2) and the independently measured mean spider's velocity as a fixed parameter, lead to a good fit over the whole range of distances (R2 = 0.80).
Our test is performed using a simple linear regression, given the assumption of the strong relation between mechanisms (i) and (ii).
To help with the interpretation of this overall pooled correlation, we converted it back to a regression coefficient using the standard deviations given in the paper by DeLany et al [21], i.e. 5.6% for the change in percent body fat and 0.98 MJ/d for PA energy expenditure.
We used conditional logistic regression giving adjusted ORs and 95% CIs scaled to the IQR increase of the 24 h concentration level of each pollutant.
LOESS uses locally weighted polynomials to fit subsets of data using ordinary least squares regression, giving more weight to points closer to the point being estimated, and less weight to points farther away (Cleveland 1979).
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