Sentence examples for variance of y from inspiring English sources

The deficiency of the approximation relative to the full process is given by taking the limit of the mean squared error (MSE) of Y ˜ − Y (equivalent to the stationary variance of Y ˜ − Y ), which, as shown in the proof of Lemma 1, is an expression of the sum over all neglected reactions.

Let μ X and μ Y be the expectations of X and Y, (sigma _{Y}^{2}) be the variance of Y, and σ XY be their covariance.

Q is a Bernoulli distributed random variable, being independent of E and X and only accounts for a small proportion of variance of Y. (5) Determine the status (occur or not occur) of Y. (6) Determine the known (not necessary the fact) causal factors for Y through a random process.

It expresses the total contribution to the variance of Y due to non- X i, i.e. to the k−1 remaining factors, hence V(Y)− V X − i { E X − i (Y| X− i)} includes all terms, i.e. a first-order term as well as interactions in Eq. (13), which involve factor X i.

Under such models, the variance of Y consists of two additive components, one representing the variance part due to the variability of θ and one due to the inherent variability of Y if θ did not vary, i.e., V(Y) = V(E(Y|θ)) + E(V(Y|θ)).

We test the approach for two-dimensional computational scenarios with moderate and high levels of heterogeneity, the latter being expressed in terms of the variance of Y.

mseclim is the variance of ys, estimated based on cross-validation, and thus not exactly one, but slightly larger (i.e., involving the difference of the independent observation ys,v to the mean of the observations used in the model training ys,c).

In the above equations, μ y 1 and μ y 2 are the means of y 1 and y 2, σ y 1 2 and σ y 2 2 are the variances of y 1 and y 2, and σ y 1, y 2 is the cross covariance between y 1 and y 2. The constants C 1,C 2, and C 3 are necessary to avoid instability when the denominators are very close to zero.

The approximate formula EBV = (REL dau ) y j + (1- REL dau ) PA can be rearranged to solve for y j as: Variance of vector y is partitioned into additive relationship matrix A and diagonal matrix D-1 containing variance of residuals: Diagonals of D-1 for each bull are or equivalently.

The degree-of-freedom parameters of the scaled inverted chi-squared distributions were 5, and the scale parameters were chosen to match a heritability of 0.5, that is: S ε = 0.5 × (d f − 2 ) V y and S k = 0.5 × (d f − 2 ) V y N k, where V y is the sample variance of { y i, i = 1, …, n }.

Practitioners can in principle describe the sensitivity pattern of a model Y= f X1,X2,…,Xk) with k uncertain input factors via a full decomposition of the variance V of Y into terms depending on the factors and their interactions.