Sentence examples for models is y from inspiring English sources

One of the simpler forms of such models is y = beta_{0} + ;sumlimits_{i = 1}^{m} {beta_{i} x_{i} + varepsilon } (13 where y is the dependent or response variable, {xi| 1 ≤ i ≤ m} are the independent or regressor variables and ε is the residual - the error due to lack of fit.

One of the simplest models is: y i = μ + ∑ j = 1 p X i j m j + e i, (1 where i = 1… n individual, j = 1… p marker position/segment, y i is the phenotypic value for individual i, μ is the overall mean, X ij is an element of the incidence matrix corresponding to marker j, individual i, m j is a random effect associated with marker j, and e i is a random residual.

As appropriate, the models were y = μ + M + R(M) + ε (where M = +/+, +/-, etc and G = 6-7, 2-6, etc was treated as an "inner" grouping) and y = μ + G + R(G) + ε, where ε indicates the within vial error variance.

The full models were Y = μ + S + G + O + S × G + S × O + G × O + S × G × O + ε, where S is the effect of S, G is genotype, O is odorant, and ε is the residual.

Generally, these models were: Y = L + N + H + V + L * N * H * V + E r r o r where Y is the phenotypic value, L and N are as described before, and H and V represent the latitude and longitude at an accession's reported collection site.

In general, the full models were: Y = L + N + A + L * N + L * A + N * A + B + E r r o r, where L (light quality: Full-Light or Dark) and N (nutrient level: High or Low) were fixed effects and A (accession) and B (block) were random effects.

The general form of the multiple regression model is y = β0 + β1x1 + β2x2 +... + βpxp + ε.

Therefore, the matrix version of our model is: y = α l n + ρWy + τEC + βX + ε (7).

The transmission model is y k [ n ] = H k [ n ] ∑ i = 1 K a T i [ n ] s i [ n ] + n k [ n ]. (9).

According to Equations (1) and (3), the disturbed data model is, y ̃ t = A θ + Δ A s t + n t (4).

Assume the channel model is Y = D X + N, by multiplying both sides of the equation by, k ( P - 1 ⊗ I m ) we obtain, Y ′ = k ( P - 1 ⊗ I m ) Y = k ( I k ⊗ C ) X + N ′, (11).