CAIE S2 2010 November — Question 7 11 marks

Exam BoardCAIE
ModuleS2 (Statistics 2)
Year2010
SessionNovember
Marks11
PaperDownload PDF ↗
Mark schemeDownload PDF ↗
TopicHypothesis test of a Poisson distribution
TypeOne-tailed test (increase or decrease)
DifficultyStandard +0.3 This is a straightforward application of Poisson hypothesis testing with standard parts: conducting a one-tailed test with given data, identifying Type I error probability (which equals the significance level), explaining Type I error, and stating what's needed for Type II error. All parts follow textbook procedures with no novel problem-solving required, though it does test understanding of hypothesis testing concepts beyond mere calculation.
Spec2.05a Hypothesis testing language: null, alternative, p-value, significance2.05b Hypothesis test for binomial proportion2.05c Significance levels: one-tail and two-tail5.02i Poisson distribution: random events model5.02k Calculate Poisson probabilities

7 In the past, the number of house sales completed per week by a building company has been modelled by a random variable which has the distribution \(\mathrm { Po } ( 0.8 )\). Following a publicity campaign, the builders hope that the mean number of sales per week will increase. In order to test at the \(5 \%\) significance level whether this is the case, the total number of sales during the first 3 weeks after the campaign is noted. It is assumed that a Poisson model is still appropriate.
  1. Given that the total number of sales during the 3 weeks is 5 , carry out the test.
  2. During the following 3 weeks the same test is carried out again, using the same significance level. Find the probability of a Type I error.
  3. Explain what is meant by a Type I error in this context.
  4. State what further information would be required in order to find the probability of a Type II error.

Question 7:
Part (i):
AnswerMarks Guidance
Answer/WorkingMark Guidance
\(H_0\): mean no. sales \(= 2.4\); \(H_1\): mean no. sales \(> 2.4\)B1 Or "\(= 0.8\) per week". Accept \(\lambda\), not \(\mu\)
\(P(X \geq 5) = 1 - e^{-2.4}\left(1 + 2.4 + \frac{2.4^2}{2!} + \frac{2.4^3}{3!} + \frac{2.4^4}{4!}\right)\)M1* Attempted with or without "\(1-\)". Allow one end error
\(= 1 - 0.9041\)A1 Allow incorrect \(\lambda\) in otherwise correct expression
\(= 0.0959\)A1
Compare with \(0.05\)M1* Indep M. (Allow recovery of above 3 marks at this point if comparison with \(0.95\) done.)
No evidence to believe mean sales increasedA1ft dep [6] Conclusion, no contradictions. SC: \(e^{-2.4} \times \frac{2.4^5}{5!} = 0.0602 > 0.05\): max B1M0A0A0M1A0
Part (ii):
AnswerMarks Guidance
Answer/WorkingMark Guidance
Need \(1^{\text{st}}\ x\) such that \(P(X \geq x) < 0.05\)M1* Attempt sum of at least 3 relevant Poisson terms, with comparison with \(0.05\) (can be implied). Can be implied, e.g. by \(P(X \leq 5) = 0.9643\) identified
\(P(X \geq 6) = 1 - e^{-2.4}\left(1 + 2.4 + \ldots + \frac{2.4^5}{5!}\right)\)M1*dep
\(= 1 - 0.9643 = 0.0357\)A1 [3]
Part (iii):
AnswerMarks Guidance
Answer/WorkingMark Guidance
Mean sales still \(0.8\) per week, but \(\geq 6\) sales in 3 weeks, so reject \(0.8\)B1 [1] Conclude mean sales have increased when not true
Part (iv):
AnswerMarks Guidance
Answer/WorkingMark Guidance
Value of true (new, changed) meanB1 [1]
## Question 7:

**Part (i):**

| Answer/Working | Mark | Guidance |
|---|---|---|
| $H_0$: mean no. sales $= 2.4$; $H_1$: mean no. sales $> 2.4$ | B1 | Or "$= 0.8$ per week". Accept $\lambda$, not $\mu$ |
| $P(X \geq 5) = 1 - e^{-2.4}\left(1 + 2.4 + \frac{2.4^2}{2!} + \frac{2.4^3}{3!} + \frac{2.4^4}{4!}\right)$ | M1* | Attempted with or without "$1-$". Allow one end error |
| $= 1 - 0.9041$ | A1 | Allow incorrect $\lambda$ in otherwise correct expression |
| $= 0.0959$ | A1 | |
| Compare with $0.05$ | M1* | Indep M. (Allow recovery of above 3 marks at this point if comparison with $0.95$ done.) |
| No evidence to believe mean sales increased | A1ft dep [6] | Conclusion, no contradictions. SC: $e^{-2.4} \times \frac{2.4^5}{5!} = 0.0602 > 0.05$: max B1M0A0A0M1A0 |

**Part (ii):**

| Answer/Working | Mark | Guidance |
|---|---|---|
| Need $1^{\text{st}}\ x$ such that $P(X \geq x) < 0.05$ | M1* | Attempt sum of at least 3 relevant Poisson terms, with comparison with $0.05$ (can be implied). Can be implied, e.g. by $P(X \leq 5) = 0.9643$ identified |
| $P(X \geq 6) = 1 - e^{-2.4}\left(1 + 2.4 + \ldots + \frac{2.4^5}{5!}\right)$ | M1*dep | |
| $= 1 - 0.9643 = 0.0357$ | A1 [3] | |

**Part (iii):**

| Answer/Working | Mark | Guidance |
|---|---|---|
| Mean sales still $0.8$ per week, but $\geq 6$ sales in 3 weeks, so reject $0.8$ | B1 [1] | Conclude mean sales have increased when not true |

**Part (iv):**

| Answer/Working | Mark | Guidance |
|---|---|---|
| Value of true (new, changed) mean | B1 [1] | |
7 In the past, the number of house sales completed per week by a building company has been modelled by a random variable which has the distribution $\mathrm { Po } ( 0.8 )$. Following a publicity campaign, the builders hope that the mean number of sales per week will increase. In order to test at the $5 \%$ significance level whether this is the case, the total number of sales during the first 3 weeks after the campaign is noted. It is assumed that a Poisson model is still appropriate.\\
(i) Given that the total number of sales during the 3 weeks is 5 , carry out the test.\\
(ii) During the following 3 weeks the same test is carried out again, using the same significance level. Find the probability of a Type I error.\\
(iii) Explain what is meant by a Type I error in this context.\\
(iv) State what further information would be required in order to find the probability of a Type II error.

\hfill \mbox{\textit{CAIE S2 2010 Q7 [11]}}