Sentence examples for log rate from inspiring English sources

One of the main numerical results of studies of reaction and diffusion in pore-fractal geometries is the existence of an intermediate low-slope asymptote, in the plot of log (rate) vs. log k, which separates the known asymptotes of kinetics- and diffusion-controlled rates.

The regression of log rate versus log C gave a linear regression equation, Log rate = log k ' + 0.01018 log C Table 1 Statistical and regression data of proposed method   Initial Rate Rate Constant Fixed Time Intercept 0.0021 ?0.22284 ?0.0028 Slop 0.01018 0.014626 0.0129 r2 0.9994 0.9990 0.9999 Correlation coefficient (r) 0.9997 0.9997 0.9997.

The above equation may be written in the logarithmic form as, Log rate = log k ' + n log C. Linear regression analysis was used to calculate slope, intercept and correlation coefficient (Table?1).

Taking logarithms of Equation (1), Log rate = log K ′ + n log drug Log Δ A / Δ t = log K ′ + n log drug Log Δ A / Δ t = - 3.5664 + 0.98 log drug, r 2 = 0.999.

The models were further reduced, if possible, by removing adjustment variables for which the squared change in the log rate ratio estimate (less adjusted minus more adjusted estimate) was smaller than the change in its estimated variance.

The null hypothesis to be tested was whether the rate ratio for treatment (ρ = λPlacebo/λVerum) is smaller or equal to 1 (i.e. log rate ratio is smaller or equal to 0).

Nevertheless, for certain ODE models such as those using lin-log rate equations, the IFPE can converge to the optimal parameter solution much faster than the IPE method.

To address these issues, in this paper we introduce a doubly stochastic Poisson process for count data regression, the failure log-rate of which is driven by a novel latent space stochastic feedforward neural network.

From the best-fitting log-rate model, the parameter estimates and their statistical significance are determined.

In order to model the variation in the under-reporting coefficients, an appropriate log-rate analysis technique was adopted.

Thirdly, the log-rate modeling results are presented and statistically significant effects on under-reporting are compared in detail.