A hospital emergency department (ED) studied factors that it thought had an effect on the number of patients who left without being seen (LWBS). It analyzed n = 164 24-hour observation periods using six binary predictors representing days of the week, daily occupancy rate, and number of patients presenting to the ED. The fitted regression equation was LWBS = 1.87 + 1.19Mon − 0.187Tue − 0.785Wed − 0.580Thu − 0.451Fri − 0.267Sat + 0.078Occ + 0.025#PatPres (SE = 6.18, R2 = .294, R2adj = .292). (a) How many binary must be omitted to prevent perfect multicollinearity? (b) Which day sees an increase in patients who leave without being seen? (c) Does the number of patients who leave without being seen increase or decrease as occupancy and number of total patients increase?
A hospital emergency department (ED) studied factors that it thought had an effect on the number of patients who left without being seen (LWBS). It analyzed n = 164 24-hour observation periods using six binary predictors representing days of the week, daily occupancy rate, and number of patients presenting to the ED. The fitted regression equation was LWBS = 1.87 + 1.19Mon − 0.187Tue − 0.785Wed − 0.580Thu − 0.451Fri − 0.267Sat + 0.078Occ + 0.025#PatPres (SE = 6.18, R2 = .294, R2adj = .292).
(a) How many binary must be omitted to prevent perfect multicollinearity?
(b) Which day sees an increase in patients who leave without being seen?
(c) Does the number of patients who leave without being seen increase or decrease as occupancy and number of total patients increase?
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