Po = 0.5385, Lq = 0.0593 boats, Wq = 0.5930 minutes, W = 6.5930 minutes.
<u>Explanation:</u>
The problem is that of Multiple-server Queuing Model.
Number of servers, M = 2.
Arrival rate,
= 6 boats per hour.
Service rate,
= 10 boats per hour.
Probability of zero boats in the system,
= 0.5385
<u>Average number of boats waiting in line for service:</u>
Lq =![[\lambda.\mu.( \lambda / \mu )M / {(M – 1)! (M. \mu – \lambda )2}] x P0](https://tex.z-dn.net/?f=%5B%5Clambda.%5Cmu.%28%20%5Clambda%20%2F%20%5Cmu%20%29M%20%2F%20%7B%28M%20%E2%80%93%201%29%21%20%28M.%20%5Cmu%20%E2%80%93%20%5Clambda%20%292%7D%5D%20x%20P0)
=
= 0.0593 boats.
The average time a boat will spend waiting for service, Wq = 0.0593 divide by 6 = 0.009883 hours = 0.5930 minutes.
The average time a boat will spend at the dock, W = 0.009883 plus (1 divide 10) = 0.109883 hours = 6.5930 minutes.
Answer:
The correct answer is option d.
Explanation:
The most effective model to understand the effect of change of a variable on other variable is by assuming other factors to be constant. This simplifies the model and helps in easily understanding the relationship between the two variables.
Though the assumption of other things being constant does not apply in the real world, it is still used as otherwise change in other factors would complicate the model. If several factors change it would be difficult to understand the relationship between variables.
Here, to study the effect of change in the price of grapes on the market for wine, it is necessary to assume other factors such as income, consumer preferences, etc to be constant.
I believe the answer is “a” or “paying cash dividends.”