In: Math
A manager at a company that manufactures cell phones has noticed that the number of faulty cell phones in a production run of cell phones is usually small and that the quality of one day's run seems to have no bearing on the next day.
Question:
a) What model might you use to model the number of faulty cell phones produced in one day?
b) If the mean number of faulty cell phones is 3.4 per day, what is the probability that no faulty cell phones will be produced tomorrow?
c) If the mean number of faulty cell phones is 3.4 per day, what is the probability that 3 or more faulty cell phones were produced in today's run?
Answer:
Given that:
a) What model might you use to model the number of faulty cell phones produced in one day?
Since this is discrete random variable, with fixed rate, and independent of previous time-spans, this can be modelled using Poison distribution.
b) If the mean number of faulty cell phones is 3.4 per day, what is the probability that no faulty cell phones will be produced tomorrow?
Mean of faulty cell phones = 3.4 per day
i.e = 3.4
X Poisson(3.4)
P(no faulty cell will be produced tomorrow) = P(X = 0)
c) f the mean number of faulty cell phones is 3.4 per day,
what is the probability that 3 or more faulty cell phones were
produced in today's run?
Probability (3 or more faulty cell phones were produced in today's run) =