From 0 to 17, there are 18 numbers, hence we defined that, the numbers of calls received from 0 to 5 calls as a Spare time, from 6 to 11 it is Modest, and from 12 to 17 it is a Busy hour.
Summing up the frequencies from 0 to 5, and further being divided by 7 days, staff answering very few phone calls are:
(60 + 0 + 0 + 3 + 3 + 5) / 7 ≈ 10.14 employees
This results indicates that in each day between Monday and Sunday, there are approximately 10.14 staff receives very few incoming calls in a sample hour, most of them receive 0 call. We therefore define this a Spare time.
Under the same formula of calculation, approximately …show more content…
We transfer the frequency table into a percentage indicator graph. As we generalized the three different degree of busyness, there are 3 groups in the above graph as well, which are also equivalent to the previous calculation results. Now, we use these three percentages as probabilities of events in a binomial distribution. Statistically speaking, the probability of a staff undergoing a spare hour is 33.81%, the probability of taking a normal work pace is about 43.33%, and the probability of working overload in a sample hour is around 22.83%.
According to the formula of binomial distribution, we can easily find out the probability (P) of how many times an event will happen within a certain number of times of experiments, long as the probability, times of experiments repeated, and numbers of the wanted successes are given. In this case, we can predict the Spare rate in next week, or the probability of in next week, 3 or 4 days it is happen out of the entire