ABB PU516A 3BSE032402R1 controller
Technical data of product parameters
A confounding variable (or confounder) is a variable that completely or partially accounts for the
apparent association between an outcome and a predictor variable. Specifically, a confounder is a
variable that is a significant predictor for the outcome under study, and is associated with, but not a
consequence of, the predictor variable in question PU516A 3BSE032402R1For example, traffic volume is a
significant predictor of crash frequency and may also be associated with several roadway characteristics
such as lane width and shoulder width (i.e., roadways with higher traffic volumes are often designed to
higher standards). If the safety effects of a specific design characteristicPU516A 3BSE032402R1are
in question, then the effects of traffic volume must be separated before the true effects of the variable
of interest may be known (Persaud et al., 1999 and Hauer et al., 2004). This holds for many variables and
emphasizes the importance of controlling for potential confounding effects when estimating the effects
of the variable of interest. Reasons for confounding include lack of available data and variables that are
not practical to measure or cannot be measured.
Unobserved heterogeneity and omitted variable bias
Heterogeneity refers to the differences across observations. Unobserved heterogeneity refers to
differences among observations that are not accounted for in the analysis. Unobserved heterogeneity is
referred to as omitted variable bias when the unobserved characteristics are correlated with a variable
that is included in a model (Lord and Mannering, 2010; Elvik, 2011a). For example, if the intent is to
estimate the safety effects of chevrons on horizontal curves and the curves with chevrons also have the
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