Single feature model
In this case, the single feature that Cynthia cares about is price. The single feature model works well for simple decisions, but most purchases require more thought. For example, buying a house based solely on price would be a terrible idea because it might be in a bad neighborhood or terrible condition.
Answer:
I would say safekeeping of employees and guests, as well as eliminating probable threats.
Explanation:
Answer:
For April, revenue was $90,000 and labor hours were 4x[(40x6)+(25x4)]. This is 90,000/1,360 = 66.18 dollars per hour of labor. For May, revenue was $80,000 and labor hours were 4x[(40x6)+(10x2)] This is 80,000/1,040 = 77 dollars per hour of labor a difference of $ 10.82per hour. The percentage change in productivity between April and May, then, is 3.95/44.12 = 0.1634935026x 100 = 16.35%
good luck ❤
