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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).
Suppose that the noisy seismic data model is: Y = S + N (3)where Y indicates the noisy record, S is the pure seismic data, and N represents the noise model.
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).
Similar(47)
The model was y = a+b1x1+b2x2+b3 x1*x2+ε, where the dependent variable (y) is threshold cycle (Ct), the covariate (x1) is the logarithmic-transformed known pcna copy number in the standard DNA, the fix factor (x2) is the DNA type (circular or linear, coded as 0 or 1 by SPSS 15), and ε is the error.
The final model was: Y i = βo + β3 (lack of latrine use).
The used model was: y = mu + GENOTYPE + ERROR (fixed in low case, random in capitals).
The basic model was: Y ij was log(PdG) or log(E1C) of day j in cycle i.
The linear mixed model was y = μ + Xβ + Ζu = e where y is a vector of phenotypes (DTDs for bulls and TDs for cows.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com