Sentence examples for described by linear regression from inspiring English sources

The relation between the square of the differential between the initial base temperature and the temperature reached at the time of the reaction ΔAT2, and the slope α, was described by linear regression using the least squares method and yielded the following parameters (Fig. 5C): (ATW−T0)2 = 8.17+0.34α (r902 = 0.729; F1–90 = 242.1; p<0.001).

Associations between ampicillin resistance, presence of a novel putative E. faecium PAI, and genetic clustering in complex-17 were described by linear logistic regression models: log odds = b0 + b1x1 + b2x2 + b3x3 + … + bixi.

The relationship between body temperature, temperature excess and ambient temperature (Ta) was described by linear, exponential or polynomial regression functions and tested with ANOVA if possible (comparing linear regressions).

The relationship between body temperature, temperature excess, and Ta was described by linear, exponential or polynomial regression functions and tested with anova.

The standard calibration curves for known amounts of ATP, ranging from 0.025 to 10.0 µmol/L, were linear (R>0.999) and could be described by the linear regression equation: y = 0.4992*x−0.0463 (n = 4, P<0.0001, r = 0.9997), in which y is the ATP concentration in micromoles and x is the chromatogram peak area.

The slopes of the line by linear regression were determined.

For ideal perception this should be an identity 〈 L R 〉 = L T. The experiments show that averages of answers is very close to this prediction and may be correctly described by a linear regression 〈 L R 〉 = γ L T, with γ values different for each tester but in all cases close to 1.

The obtained dose-response curve of chromosome aberrations was described by a linear regression, which then became a plateau.

The relationship between VPW and PAOP is described by the linear regression equation: VPW = 57 + 0.9* PAOP) while the equation: VPW = 66.4 + 0.45* CVP) describes the correlation with CVP.

The relationship could be well described by a linear regression with MacNew HRQOL as dependent and HADS Total score as independent variable.

For each value of F, the individual relationships between ω and T could be described by a linear regression and the values of ω0 and T0 for each load were determined by extrapolation from these individual regressions.