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In particular, control frameworks for the user data driven requirements elicitation process are experimented, and potential control variables are discussed.
All characteristics in table 1 were tested as potential control variables.
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Because just 7% (n = 20) of mothers reported working outside the home, only paternal occupation was considered as a potential control variable.
The analysis effectively reduces the number of potential controlled variables, by considering the sensitivity of the measurements towards changes in the manipulated variables.
Potential control and confounding variables, such as developmental level of the child at T1 and T2, premature birth, household income and maternal education level were included in the second model and fitted in the equation.
In this work, the potential of directly including control variables with time varying values and variable batch sizes in the scheduling of batch plants is explored.
This provides a useful general design tool, as opposed to the analysis of a particular simulated case, to assess the potential thermal effects of control variables in different building configurations.
We collected data on demographic, socioeconomic, and other factors that might constitute potential confounders or important control variables modifying the relationship between lead and child development.
In short, our control variables are potential mediators as well as potential confounders.
The control variables are potential variables that are capable of influencing firm performance, rather than the identified variables.
α (t − y),g is a coefficient of potential experience dummy, u ys is the unemployment rate of the year and the state of entry into the labor market, ( {u}_{ttilde{s}} ) is the current unemployment rate in the state of current residence, and X it represents other control variables including potential experience, the highest grade completed, and the age-adjusted AFQT score.
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