Sentence examples for mean and constant from inspiring English sources

The concrete's response to variable THM boundaries is also compared to the one where mean and constant ones are considered in the case of NCBs.

Second, the performance of linear regression models depends on various assumptions such as absence of multicollinearity, and normally distributed residuals with zero mean and constant variance.

Since ( varepsilon ) xt and ( varepsilon ) yt are white-noise processes, it follows that ( e ) xt and ( e ) yt have zero mean and constant variance, and are individually serially uncorrelated.

Where: N is the number of observations; y i is the observed inefficiency score, i.e. dependent variable; y* is the latent dependent variable; βi is the kx1 vector of unknown parameters; x 1 is the kx1 vector of explanatory/independent variables; and ε i is an independently distributed error term assumed to be normal with zero mean and constant variance σ2.

A moving average process of order 1 can be written as: Open image in new window (2 while an autoregressive process of order 1, can be expressed as: Open image in new window (3)where ε n, n = 1, 2, …, N are white noise terms (i.e. independent normal error terms with zero mean and constant unit variance).

We assumed P0 = 1 to be a mean and constant concentration value of the limiting cellular factor.

Disturbances (u_{i}) and (v_{i}) are assumed to have zero means and constant (but differentiated among both types of workers) variances (sigma _{u_{i}}^{2}) and (sigma _{v_{i}}^{2}).

Bland Altman plots [20] showed little mean shift and constant variation of differences over the range of values (Figure 1), demonstrating reasonable agreement between the two assays.

We assume that these models have normally distributed residual with mean zero and constant variance.

After ovulation, we modeled the log(PdG) with a normal distribution with a quadratic mean function and constant variance.

Under the standard Brownian motion model of evolution, the continuous character evolves by accumulating random independent increments drawn from a probability distribution with constant mean zero and constant finite variance σ.