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 = 181 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 = 1.76 + 1.37Mon - 0.180 Tue - 0.778Wed - 0.530 Thu - 0.455Fri - 0.458Sat + 0.0790cc + 0.030#PatPres (SE = 6.58, Re = .286, Rad; = .284). (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? 1 (d) What percentage of the change in LWBS can be explained by this model? (Round your answer to 1 decimal place.) R [ % |