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Question 8.5: An advertising board is illuminated by several hundred bulbs......

An advertising board is illuminated by several hundred bulbs. Some of the bulbs are fused or smashed regularly. If there are more than 5 fused bulbs on a day, the owner of the board replaces them, otherwise not. Consider the following data collected over a month which captures the number of days (n_{i} ) on which i bulbs were broken:

(a) Suggest an appropriate distribution for X: “number of broken bulbs per day”.
(b) What is the average number of broken bulbs per day? What is the variance?
(c) Determine the probabilities P(X = x) using the distribution you chose in (a) and using the average number of broken bulbs you calculated in (b). Compare the probabilities with the proportions obtained from the data.
(d) Calculate the probability that at least 6 bulbs are fused, which means they need to be replaced.
(e) Consider the random variable Y : “time until next bulb breaks”. What is the distribution of Y ?
(f) Calculate and interpret E(Y ).

Fused bulbs 0 1 2 3 4 5
n_{i} 6 8 8 5 2 1
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(a) It seems appropriate to model the number of fused bulbs with a Poisson distribution. We assume, however, that the probabilities of fused bulbs on two consecutive days are independent of each other; i.e. they only depend on λ but not on the time t.

(b) The arithmetic mean is

\bar{x} =\frac{1}{30} \left(0 + 1 · 8 + 2 · 8+· · ·+5 · 1\right)=\frac{52}{30}=1.7333

which means that, on an average, 1.73 bulbs are fused per day. The variance is

s^{2}= \frac{1}{30} \left(0 + 1^{2} · 8 + 2^{2}· 8+· · ·+5^{2} · 1\right)− 1.7333^{2}

= \frac{142}{30} − 3.0044 = 1.72889.

We see that mean and variance are similar, which is an indication that the choice of a Poisson distribution is appropriate since we assume E(X) = λ and Var(X) = λ.

(c) The following table lists the proportions (i.e. relative frequencies f_{j} ) together with the probabilities P(X = x) from a Po(1.73)-distribution. As a reference, we also list the probabilities from a Po(2)-distribution since it is not practically possible that 1.73 bulbs stop working and it may hence be an option to round the mean.

One can see that observed proportions and expected probabilities are close together which indicates again that the choice of a Poisson distribution was appropriate. Chapter 9 gives more details on how to estimate parameters, such as λ, from data if it is unknown.

(d) Using λ = 1.73, we calculate

P(X > 5) = 1 − P(X ≤ 5) = 1 − \sum\limits_{i=0}^{5}{\frac{\lambda ^{i}}{i!} exp\left(-\lambda \right) }

=1-exp\left(−1.73\right)\left(\frac{1.73^{0}}{0!} +\frac{1.73^{1}}{1!}+ \ldots +\frac{1.73^{5}}{5!}\right)

= 1 − 0.99 = 0.01.

Thus, the bulbs are replaced on only 1 % of the days.

(e) If X follows a Poisson distribution then, given Theorem 8.2.1, Y follows an exponential distribution with λ = 1.73.

(f) The expectation of an exponentially distributed variable is

E(Y) = \frac{1}{\lambda}=\frac{1}{1.73}=0.578.

This means that, on average, it takes more than half a day until one of the bulbs gets fused.

f_{i} Po(1.73) Po(2)
P(X = 0) 0.2 0.177 0.135
P(X = 1) 0.267 0.307 0.27
P(X = 2) 0.267 0.265 0.27
P(X = 3) 0.167 0.153 0.18
P(X = 4) 0.067 0.067 0.09
P(X = 5) 0.033 0.023 0.036

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