A roller-coaster ride has two separate safety systems to detect faults on the track and on the roller-coaster train itself. Over a long period of time, it is found that the average number of faults detected per day by the systems are 0.25 for the track and 0.15 for the train. Assume that the faults detected on the track are independent of those detected on the train.

  1. (i)

    State, in this context, a condition that must be met for a Poisson distribution to be a suitable model for the number of faults occurring on a randomly chosen day.

  2. (ii)

    Find the probability that a total of at most 4 faults is detected by the two systems in a period of 10 days.

  3. (iii)

    Find the smallest number of days for which the probability that no fault is detected by the two systems is less than 0.05.

  4. (iv)

    Find the probability that, in a randomly chosen period of 10 days, there are at least 3 faults detected on the track, given that there are a total of at most 4 faults detected by the two systems.

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Worked solution

9(i)

1. State a necessary condition in the context of the question for a Poisson model to be valid.

For a Poisson distribution to accurately model the number of faults occurring, the events (faults) must occur singly, randomly, independently of each other, and at a constant average rate.

Stated in context, acceptable conditions include:

  • The average rate of faults occurring per day remains constant from day to day.
  • The occurrence of one fault does not affect the likelihood of another fault occurring (faults occur independently of one another).
Answer

The average number of faults per day remains constant over time. (Alternatively: The occurrence of any one fault is independent of the occurrence of any other fault.)

9(ii)

1. Determine the distribution of the total number of faults in a 10-day period.

Let XX be the total number of faults detected by the two systems in 10 days.
The average number of total faults per day is 0.25+0.15=0.400.25 + 0.15 = 0.40.
For a 10-day period, the mean number of faults is λ=0.40×10=4\lambda = 0.40 \times 10 = 4.
Given that the faults occur independently, the total number of faults follows a Poisson distribution, so X∼Po(4)X \sim \text{Po}(4).

2. Calculate the probability of detecting at most 4 faults.

We require P(X≤4)\text{P}(X \le 4).
Using the Poisson cumulative distribution function (e.g., on a graphing calculator):
P(X≤4)≈0.62884\text{P}(X \le 4) \approx 0.62884
P(X≤4)=0.629 (to 3 s.f.)\text{P}(X \le 4) = 0.629 \text{ (to 3 s.f.)}

Answer

0.629

9(iii)

1. Formulate an inequality for the probability of 0 faults occurring over $n$ days

Let nn be the number of days.
Since the total average number of faults per day is 0.25+0.15=0.40.25 + 0.15 = 0.4, the number of faults in nn days follows a Poisson distribution with mean 0.4n0.4n.
We require the probability of 00 faults to be less than 0.050.05:
P(Y=0)<0.05\mathrm{P}(Y = 0) < 0.05
e−0.4n<0.05\mathrm{e}^{-0.4n} < 0.05

2. Solve the exponential inequality to find the smallest integer $n$

Taking the natural logarithm on both sides:
−0.4n<ln⁡0.05-0.4n < \ln 0.05
n>ln⁡0.05−0.4n > \frac{\ln 0.05}{-0.4}
n>7.4893n > 7.4893
Since nn must be an integer, the smallest number of days is 88.

Answer

88

9(iv)

1. Identify the distributions for the track, train, and total faults in a 10-day period.

Let XX, YY and TT be the number of faults on the track, train, and in total respectively in 10 days.
λX=10×0.25=2.5  ⟹  X∼Po(2.5)\lambda_X = 10 \times 0.25 = 2.5 \implies X \sim \text{Po}(2.5)
λY=10×0.15=1.5  ⟹  Y∼Po(1.5)\lambda_Y = 10 \times 0.15 = 1.5 \implies Y \sim \text{Po}(1.5)
T=X+Y∼Po(4)T = X + Y \sim \text{Po}(4)

2. Set up the conditional probability.

We require P(X≥3∣T≤4)\mathrm{P}(X \ge 3 \mid T \le 4).
Using the formula for conditional probability:
P(X≥3∣T≤4)=P(X≥3 and T≤4)P(T≤4)\mathrm{P}(X \ge 3 \mid T \le 4) = \frac{\mathrm{P}(X \ge 3 \text{ and } T \le 4)}{\mathrm{P}(T \le 4)}

3. Enumerate the mutually exclusive cases satisfying both conditions.

Since T=X+YT = X + Y and Y≥0Y \ge 0, the event (X≥3 and T≤4)(X \ge 3 \text{ and } T \le 4) occurs under exactly three mutually exclusive scenarios:

  • X=3,Y=0X = 3, Y = 0
  • X=3,Y=1X = 3, Y = 1
  • X=4,Y=0X = 4, Y = 0

4. Calculate the probabilities and evaluate the final conditional probability.

Since XX and YY are independent:
P(X≥3 and T≤4)=P(X=3)P(Y=0)+P(X=3)P(Y=1)+P(X=4)P(Y=0)=(0.21376)(0.22313)+(0.21376)(0.33470)+(0.13360)(0.22313)≈0.14905\begin{aligned} \mathrm{P}(X \ge 3 \text{ and } T \le 4) &= \mathrm{P}(X=3)\mathrm{P}(Y=0) + \mathrm{P}(X=3)\mathrm{P}(Y=1) + \mathrm{P}(X=4)\mathrm{P}(Y=0) \\ &= (0.21376)(0.22313) + (0.21376)(0.33470) + (0.13360)(0.22313) \\ &\approx 0.14905 \end{aligned}
From the total distribution T∼Po(4)T \sim \text{Po}(4), we have P(T≤4)≈0.62884\mathrm{P}(T \le 4) \approx 0.62884 (often found in part ii).
P(X≥3∣T≤4)=0.149050.62884≈0.23702≈0.237 (to 3 s.f.)\mathrm{P}(X \ge 3 \mid T \le 4) = \frac{0.14905}{0.62884} \approx 0.23702 \approx 0.237 \text{ (to 3 s.f.)}

Answer

0.2370.237

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