Optimal Statistical Analysis Involving Multiple Confounding Variables

Paul R. Yarnold

Optimal Data Analysis, LLC

This paper demonstrates a maximum-accuracy statistical approach that assesses the effect of two or more confounding variables on the estimated association of a class variable and attribute(s). An example involves modeling patient self-ratings of the likelihood that they will recommend an Emergency Department to others (class variable), based on five patient-rated dimensions of physician care behavior (attributes), and patient satisfaction ratings for amount of time waiting to see the physician, and for amount of time waiting in the registration room before going to the treatment area (confounders).

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