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Each collection element is a vector of characteristics which is related to the cost, power consumption, and QoS.
The non-linear decomposition model assumes that the conditional expectation of the probability of the immunization status of a child is a non-linear function of a vector of characteristics.
I use the parameterization {rho}_{gmt}={beta}_3LS{I}_{mt}+{boldsymbol{upomega}}_3^{mathbf{prime}}{mathbf{v}}_{gmt}+{e}_{gmt}, (3)where v gmt is a vector of characteristics of women in group g in metro m in year t, and e gmt represents the un-modeled determinants of ρ.
The selection equation specifies that HIV status is only observed if Zγ + u2 > 0. In this equation Z stands for a vector of characteristics that affect consent for HIV testing.
Based on the linear model: where Yi refers to the outcome (mean VAS) for each group (1994 and 2006), X'i is a vector of characteristics (sociodemographic and chronic conditions), βi is the associated parameter vector, and εi is an error term.
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(iii)"Find a vector of characteristic values, one for each frame".
where score is t is the PISA reading score for student i in year t (t =2000, 2009) and school s, X is a vector for individual characteristics, S is a vector of school characteristics, and u is t is an error term with mean zero and variance σt 2. Among the school characteristics, we include the proportion of foreign language speakers in the school.
Finally, I is a vector of individual characteristics, S is a vector of school-level characteristics, and D is a vector of district-level characteristics, μ s are state dummy variables, υ icdst are dummy variables for years of teaching experience, and ε icdst is an idiosyncratic error term.
Furthermore, x it denotes a vector of worker characteristics, z jt a vector of plant characteristics, ε ijt the idiosyncratic error component, and α, β, γ, and δ are the corresponding coefficient vectors.
For the technically minded Citi kindly explains it is using a logit regression model, expressed as: p i,t) = 1/1 + e(to the power of)- βx i,t) where: x i,t) is a vector of fundamental characteristics of the firm; β is a vector of factor sensitivities; p i,t) is the probability that a firm i will be conduct a rights issue in period t.
X i,2006 is a vector of individual characteristics.
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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