OCR MEI S2 2011 June — Question 2 16 marks

Exam BoardOCR MEI
ModuleS2 (Statistics 2)
Year2011
SessionJune
Marks16
PaperDownload PDF ↗
Mark schemeDownload PDF ↗
TopicPoisson distribution
TypeExplain or apply conditions in context
DifficultyModerate -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.
Spec5.02i 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

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.
  1. 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.
  2. Find the probability of at most 1 car arriving in a period of 1 minute.
  3. Find the probability of more than 5 cars arriving in a period of 10 minutes.
  4. State the exact distribution of the number of cars arriving in a period of 1 hour.
  5. Use a suitable approximating distribution to find the probability that at least 40 cars arrive in a period of 1 hour.

Question 2:
Part 2(i)
AnswerMarks Guidance
AnswerMarks Guidance
Independently means arrival time of each car is unrelated to arrival time of any other carE1 Each answer must be 'in context' and 'clear'; allow sensible alternative wording
Randomly means arrival times of cars are not predictableE1 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 timeE1
Total: 3
Part 2(ii)
AnswerMarks Guidance
AnswerMarks 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 CAOAllow 0.8715; not 0.872 or 0.8714; allow 0.87 without wrong working
Total: 3
Part 2(iii)
AnswerMarks Guidance
AnswerMarks 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 CAOAllow 0.586
Total: 3
Part 2(iv)
AnswerMarks Guidance
AnswerMarks Guidance
Poisson with mean 37.2B1
B1Dependent on first B1; condone \(P(37.2, 37.2)\)
Total: 2
Part 2(v)
AnswerMarks Guidance
AnswerMarks Guidance
Use Normal approximation with \(\mu = \sigma^2 = \lambda = 37.2\)B1 For Normal (SOI)
B1For 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]}}