| Exam Board | OCR MEI |
|---|---|
| Module | S2 (Statistics 2) |
| Year | 2011 |
| Session | June |
| Marks | 16 |
| Paper | Download PDF ↗ |
| Mark scheme | Download PDF ↗ |
| Topic | Poisson distribution |
| Type | Explain or apply conditions in context |
| Difficulty | Moderate -0.3 This is a straightforward Poisson distribution question testing standard conditions and routine calculations. Part (i) requires explaining standard Poisson assumptions (textbook definitions), parts (ii)-(iv) involve direct application of Poisson probability formulas with simple parameter scaling, and part (v) uses the standard normal approximation technique. While it covers multiple concepts, each part is procedural with no novel problem-solving required, making it slightly easier than average. |
| Spec | 5.02i Poisson distribution: random events model5.02j Poisson formula: P(X=x) = e^(-lambda)*lambda^x/x!5.02k Calculate Poisson probabilities5.02l Poisson conditions: for modelling5.02m Poisson: mean = variance = lambda |
| Answer | Marks | Guidance |
|---|---|---|
| Answer | Marks | Guidance |
| Independently means arrival time of each car is unrelated to arrival time of any other car | E1 | Each answer must be 'in context' and 'clear'; allow sensible alternative wording |
| Randomly means arrival times of cars are not predictable | E1 | SC1 for ALL answers not in context but otherwise correct |
| At a uniform average rate means average rate of car arrivals does not vary over time | E1 | |
| Total: 3 |
| Answer | Marks | Guidance |
|---|---|---|
| Answer | Marks | Guidance |
| \(P(\text{at most 1}) = e^{-0.62}\frac{0.62^0}{0!} + e^{-0.62}\frac{0.62^1}{1!}\) | M1 | For either term |
| \(= 0.5379\ldots + 0.3335\ldots = 0.871\) | M1 | For sum of both |
| A1 CAO | Allow 0.8715; not 0.872 or 0.8714; allow 0.87 without wrong working | |
| Total: 3 |
| Answer | Marks | Guidance |
|---|---|---|
| Answer | Marks | Guidance |
| New \(\lambda = 10 \times 0.62 = 6.2\) | B1 | For mean (SOI) |
| \(P(\text{more than 5 in 10 mins}) = 1 - 0.4141 = 0.5859\) | M1 | Use of \(1 - P(X \leq 5)\) with any \(\lambda\) |
| A1 CAO | Allow 0.586 | |
| Total: 3 |
| Answer | Marks | Guidance |
|---|---|---|
| Answer | Marks | Guidance |
| Poisson with mean 37.2 | B1 | |
| B1 | Dependent on first B1; condone \(P(37.2, 37.2)\) | |
| Total: 2 |
| Answer | Marks | Guidance |
|---|---|---|
| Answer | Marks | Guidance |
| Use Normal approximation with \(\mu = \sigma^2 = \lambda = 37.2\) | B1 | For Normal (SOI) |
| B1 | For parameters | |
| \(P(X \geq 40) = P\!\left(Z > \frac{39.5 - 37.2}{\sqrt{37.2}}\right)\) | B1 | For 39.5 |
| \(= P(Z > 0.377) = 1 - \Phi(0.377) = 1 - 0.6469\) | M1 | Correct use of Normal approximation using correct tail |
| \(= 0.3531\) | A1 cao | Allow 0.353 |
| Total: 5 |
# Question 2:
## Part 2(i)
| Answer | Marks | Guidance |
|--------|-------|----------|
| Independently means arrival time of each car is unrelated to arrival time of any other car | E1 | Each answer must be 'in context' and 'clear'; allow sensible alternative wording |
| Randomly means arrival times of cars are not predictable | E1 | SC1 for ALL answers not in context but otherwise correct |
| At a uniform average rate means average rate of car arrivals does not vary over time | E1 | |
| **Total: 3** | | |
## Part 2(ii)
| Answer | Marks | Guidance |
|--------|-------|----------|
| $P(\text{at most 1}) = e^{-0.62}\frac{0.62^0}{0!} + e^{-0.62}\frac{0.62^1}{1!}$ | M1 | For either term |
| $= 0.5379\ldots + 0.3335\ldots = 0.871$ | M1 | For sum of both |
| | A1 CAO | Allow 0.8715; not 0.872 or 0.8714; allow 0.87 without wrong working |
| **Total: 3** | | |
## Part 2(iii)
| Answer | Marks | Guidance |
|--------|-------|----------|
| New $\lambda = 10 \times 0.62 = 6.2$ | B1 | For mean (SOI) |
| $P(\text{more than 5 in 10 mins}) = 1 - 0.4141 = 0.5859$ | M1 | Use of $1 - P(X \leq 5)$ with any $\lambda$ |
| | A1 CAO | Allow 0.586 |
| **Total: 3** | | |
## Part 2(iv)
| Answer | Marks | Guidance |
|--------|-------|----------|
| Poisson with mean 37.2 | B1 | |
| | B1 | Dependent on first B1; condone $P(37.2, 37.2)$ |
| **Total: 2** | | |
## Part 2(v)
| Answer | Marks | Guidance |
|--------|-------|----------|
| Use Normal approximation with $\mu = \sigma^2 = \lambda = 37.2$ | B1 | For Normal (SOI) |
| | B1 | For parameters |
| $P(X \geq 40) = P\!\left(Z > \frac{39.5 - 37.2}{\sqrt{37.2}}\right)$ | B1 | For 39.5 |
| $= P(Z > 0.377) = 1 - \Phi(0.377) = 1 - 0.6469$ | M1 | Correct use of Normal approximation using correct tail |
| $= 0.3531$ | A1 cao | Allow 0.353 |
| **Total: 5** | | |
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2 At a drive-through fast food takeaway, cars arrive independently, randomly and at a uniform average rate. The numbers of cars arriving per minute may be modelled by a Poisson distribution with mean 0.62.\\
(i) Briefly explain the meaning of each of the three terms 'independently', 'randomly' and 'at a uniform average rate', in the context of cars arriving at a fast food takeaway.\\
(ii) Find the probability of at most 1 car arriving in a period of 1 minute.\\
(iii) Find the probability of more than 5 cars arriving in a period of 10 minutes.\\
(iv) State the exact distribution of the number of cars arriving in a period of 1 hour.\\
(v) Use a suitable approximating distribution to find the probability that at least 40 cars arrive in a period of 1 hour.
\hfill \mbox{\textit{OCR MEI S2 2011 Q2 [16]}}