Poisson Distribution

Includes hypothesis testingFrom an AS paper

Edexcel

Edexcel · Old spec

A2 June 2025 Q5

5. A football team scores goals at an average rate of 1.8 goals per match.

(a) Give two assumptions that would be necessary to use a Poisson distribution to model the number of goals scored in a match by the team. (2)

Given that in their next match the team scores exactly 3 goals,

(b) find the exact probability that 2 of these goals were scored in the first half of the match. (4)

A hockey team plays 2 games each week during a 40-week season.
Each game consists of 2 periods.
The probability that the team concedes no goals in a period is 0.55

The random variable \(X\) represents the number of periods in a week in which the team concedes no goals.

(c)
(i) Write down a suitable distribution for \(X\)
(ii) For this 40-week season, use the Central Limit Theorem to estimate \(\mathrm{P}(\overline{X} \gt 2)\) (4)

AS June 2025 Q3

EdexcelAS paperCurrent spec11 marksIncludes hypothesis testingPoisson Distribution

3. Raoul, Steffi and Taro are catching butterflies for research.

The number of butterflies caught by Raoul per hour may be assumed to follow a Poisson distribution with mean 4.2

Find the probability that Raoul catches

(a)
(i) exactly 6 butterflies in a randomly selected one-hour period, (1)
(ii) exactly 1 butterfly in a randomly selected 10-minute period. (2)

Following a long period without rain, Raoul believes there will now be a change to the rate at which he catches butterflies.
To test his belief, he uses the random variable \(R\) to represent the number of butterflies he catches in a 4-hour period.

A hypothesis test is to be carried out to determine whether or not there is support for Raoul’s belief. The null hypothesis of the test will be rejected if \(R \leqslant 9\) or \(R \gt 25\)

(b) Stating the hypotheses clearly, find the actual level of significance of the test. (4)

The number of butterflies caught by Steffi per hour may be assumed to follow a Poisson distribution with mean 3.2

(c) Find the probability that in exactly 2 of the next 4 hours, Steffi catches less than or equal to 3 butterflies each hour. (3)

The number of butterflies caught by Taro per hour may be assumed to follow a Poisson distribution with mean 2.7

Steffi and Taro both go to catch butterflies in a field one day.

Taro models the total number of butterflies caught per hour with a Poisson distribution with mean 3.2 + 2.7 = 5.9

(d) State a condition that would be needed for Taro’s model to be valid. (1)

AS June 2025 Q2

EdexcelAS paperCurrent spec12 marksDRVsPoisson Distribution

2. The discrete random variable \(X\) represents the score when a spinner is spun.
The probability distribution of \(X\) is given by

\(x\)259
\(\mathrm{P}(X = x)\)0.60.30.1
(a) Find \(\mathrm{Var}(X)\)
Show your working clearly. (4)

A game is played by spinning the spinner twice.

If the two scores are the same, the number of points earned is 0

If the two scores are different, the number of points earned is the sum of the two scores.

(b) Show that the probability of earning 14 points in one game is 0.06 (1)
(c) Find the expected number of points earned when the game is played once. (4)

Mehmet plays the game 150 times.

(d) Using a Poisson approximation, find the probability that Mehmet earns 14 points in exactly 4 of the games. (3)

A2 June 2024 Q2

EdexcelCurrent spec7 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

2. The number of errors made by a secretary is modelled by a Poisson distribution with a mean of 2.4 per 100 words.

A 100-word piece of work completed by the secretary is selected at random.

(a) Find the probability that
(i) there are exactly 3 errors,
(ii) there are fewer than 2 errors. (2)

After a long holiday, a randomly selected piece of work containing 250 words completed by the secretary is examined to see if the rate of errors has changed.

(b) Stating your hypotheses clearly, and using a 5% level of significance, find the critical region for a suitable test. (4)
(c) Find \(\mathrm{P}(\text{Type I error})\) for the test in part (b) (1)

AS June 2024 Q2

EdexcelAS paperCurrent spec13 marksIncludes hypothesis testingPoisson Distribution

2. A manager keeps a record of accidents in a canteen.

Accidents occur randomly with an average of 2.7 per month. The manager decides to model the number of accidents with a Poisson distribution.

(a) Give a reason why a Poisson distribution could be a suitable model in this situation. (1)
(b) Assuming that a Poisson model is suitable, find the probability of
(i) at least 3 accidents in the next month, (1)
(ii) no more than 10 accidents in a 3-month period, (2)
(iii) at least 2 months with no accidents in an 8-month period. (4)

One day, two members of staff bump into each other in the canteen and each report the accident to the manager. The canteen manager is unsure whether to record this as one or two accidents.

Given that the manager still wants to model the number of accidents per month with a Poisson distribution,

(c) state
  • a property of the Poisson distribution that the manager should consider when deciding how to record this situation
  • whether the manager should record this as one or two accidents
(1)

The manager introduces some new procedures to try and reduce the average number of accidents per month.

During the following 12 months the total number of accidents is 22
The manager claims that the accident rate has been reduced.

(d) Use a 5% level of significance to carry out a suitable test to assess the manager’s claim.
You should state your hypotheses clearly and the p-value used in your test. (4)

AS June 2023 Q3

EdexcelAS paperCurrent spec16 marksIncludes hypothesis testingPoisson Distribution

3. A machine produces cloth. Faults occur randomly in the cloth at a rate of 0.4 per square metre.

The machine is used to produce tablecloths, each of area \(A\) square metres. One of these tablecloths is taken at random.

The probability that this tablecloth has no faults is 0.0907

(a) Find the value of \(A\) (3)

The tablecloths are sold in packets of 20

A randomly selected packet is taken.

(b) Find the probability that more than 1 of the tablecloths in this packet has no faults. (3)

A hotel places an order for 100 tablecloths each of area \(A\) square metres.

The random variable \(X\) represents the number of these tablecloths that have no faults.

(c) Find
(i) \(\mathrm{E}(X)\)
(ii) \(\mathrm{Var}(X)\) (3)
(d) Use a Poisson approximation to estimate \(\mathrm{P}(X = 10)\) (2)

It is claimed that a new machine produces cloth with a rate of faults that is less than 0.4 per square metre.

A piece of cloth produced by this new machine is taken at random.
The piece of cloth has area 30 square metres and is found to have 6 faults.

(e) Stating your hypotheses clearly, use a suitable test to assess the claim made for the new machine. Use a 5% level of significance. (4)
(f) Write down the p-value for the test used in part (e). (1)

A2 June 2023 Q2

EdexcelCurrent spec11 marksIncludes hypothesis testingPoisson Distribution

2. Telephone calls arrive at a call centre randomly, at an average rate of 1.7 per minute.
After the call centre was closed for a week, in a random sample of 10 minutes there were 25 calls to the call centre.

(a) Carry out a suitable test to determine whether or not there is evidence that the rate of calls arriving at the call centre has changed.
Use a 5% level of significance and state your hypotheses clearly. (4)

Only 1.2% of the calls to the call centre last longer than 8 minutes.

One day Tiang has 70 calls.

(b) Find the probability that out of these 70 calls Tiang has more than 2 calls lasting longer than 8 minutes. (3)

The call centre records show that 95% of days have at least one call lasting longer than 30 minutes.
On Wednesday 900 calls arrived at the call centre and none of them lasted longer than 30 minutes.

(c) Use a Poisson approximation to estimate the proportion of calls arriving at the call centre that last longer than 30 minutes. (4)

A2 June 2022 Q3

EdexcelCurrent spec14 marksIncludes hypothesis testingPoisson Distribution

3. During the summer, mountain rescue team \(A\) receives calls for help randomly with a rate of 0.4 per day.

(a) Find the probability that during the summer, mountain rescue team \(A\) receives at least 19 calls for help in 28 randomly selected days. (2)

The leader of mountain rescue team \(A\) randomly selects 250 summer days from the last few years.
She records the number of calls for help received on each of these days.

(b) Using a Poisson approximation, estimate the probability of the leader finding at least 20 of these days when more than 1 call for help was received by mountain rescue team \(A\). (4)

Mountain rescue team \(A\) believes that the number of calls for help per day is lower in the winter than in the summer. The number of calls for help received in 42 randomly selected winter days is 8

(c) Use a suitable test, at the 5% level of significance, to assess whether or not there is evidence that the number of calls for help per day is lower in the winter than in the summer. State your hypotheses clearly. (4)

During the summer, mountain rescue team \(B\) receives calls for help randomly with a rate of 0.2 per day, independently of calls to mountain rescue team \(A\).

The random variable \(C\) is the total number of calls for help received by mountain rescue teams \(A\) and \(B\) during a period of \(n\) days in the summer.
On a Monday in the summer, mountain rescue teams \(A\) and \(B\) each receive a call for help.

Given that over the next \(n\) days \(\mathrm{P}(C = 0) \lt 0.001\)

(d) calculate the minimum value of \(n\) (3)
(e) Write down an assumption that needs to be made for the model to be appropriate. (1)

AS June 2022 Q2

EdexcelAS paperCurrent spec10 marksIncludes hypothesis testingPoisson Distribution

2. Xena catches fish at random, at a constant rate of 0.6 per hour.

(a) Find the probability that Xena catches exactly 4 fish in a 5-hour period. (2)

The probability of Xena catching no fish in a period of \(t\) hours is less than 0.16

(b) Find the minimum value of \(t\), giving your answer to one decimal place. (3)

Independently of Xena, Zion catches fish at random with a mean rate of 0.8 per hour.

Xena and Zion try using new bait to catch fish. The number of fish caught in total by Xena and Zion after using the new bait, in a randomly selected 4-hour period, is 12

(c) Use a suitable test to determine, at the 5% level of significance, whether or not there is evidence that the rate at which fish are caught has increased after using the new bait. State your hypotheses clearly and the p-value used in your test. (5)

A2 October 2021 Q4

EdexcelCurrent spec10 marksDRVsPoisson Distribution

4. Members of a photographic group may enter a maximum of 5 photographs into a members only competition.
Past experience has shown that the number of photographs, \(N\), entered by a member follows the probability distribution shown below.

\(n\)012345
\(\mathrm{P}(N = n)\)\(a\)0.20.050.25\(b\)\(c\)

Given that \(\mathrm{E}(4N + 2) = 14.8\) and \(\mathrm{P}(N = 5 \mid N \gt 2) = \dfrac{1}{2}\)

(a) show that \(\mathrm{Var}(N) = 2.76\) (6)

The group decided to charge a 50p entry fee for the first photograph entered and then 20p for each extra photograph entered into the competition up to a maximum of £1 per person. Thus a member who enters 3 photographs pays 90p and a member who enters 4 or 5 photographs just pays £1

Assuming that the probability distribution for the number of photographs entered by a member is unchanged,

(b) calculate the expected entry fee per member. (3)

Bai suggests that, as the mean and variance are close, a Poisson distribution could be used to model the number of photographs entered by a member next year.

(c) State a limitation of the Poisson distribution in this case. (1)

A2 October 2021 Q2

EdexcelCurrent spec14 marksIncludes hypothesis testingPoisson Distribution

2. On a weekday, a garage receives telephone calls randomly, at a mean rate of 1.25 per 10 minutes.

(a) Show that the probability that on a weekday at least 2 calls are received by the garage in a 30-minute period is 0.888 to 3 decimal places. (2)
(b) Calculate the probability that at least 2 calls are received by the garage in fewer than 4 out of 6 randomly selected, non-overlapping 30-minute periods on a weekday. (2)

The manager of the garage randomly selects 150 non-overlapping 30-minute periods on weekdays.
She records the number of calls received in each of these 30-minute periods.

(c) Using a Poisson approximation show that the probability of the manager finding at least 3 of these 30-minute periods when exactly 8 calls are received by the garage is 0.664 to 3 significant figures. (4)
(d) Explain why the Poisson approximation may be reasonable in this case. (1)

The manager of the garage decides to test whether the number of calls received on a Saturday is different from the number of calls received on a weekday.  She selects a Saturday at random and records the number of telephone calls received by the garage in the first 4 hours.

(e) Write down the hypotheses for this test. (1)

The manager found that there had been 40 telephone calls received by the garage in the first 4 hours.

(f) Carry out the test using a 5% level of significance. (4)

AS October 2020 Q4

EdexcelAS paperCurrent spec8 marksIncludes hypothesis testingPoisson Distribution

4. During the morning, the number of cyclists passing a particular point on a cycle path in a 10-minute interval travelling eastbound can be modelled by a Poisson distribution with mean 8

The number of cyclists passing the same point in a 10-minute interval travelling westbound can be modelled by a Poisson distribution with mean 3

(a) Suggest a model for the total number of cyclists passing the point on the cycle path in a 10-minute interval, stating a necessary assumption. (2)

Given that exactly 12 cyclists pass the point in a 10-minute interval,

(b) find the probability that at least 11 are travelling eastbound. (3)

After some roadworks were completed, the total number of cyclists passing the point in a randomly selected 20-minute interval one morning is found to be 14

(c) Test, at the 5% level of significance, whether there is evidence of a decrease in the rate of cyclists passing the point.
State your hypotheses clearly. (3)

A2 October 2020 Q2

EdexcelCurrent spec4 marksPoisson Distribution

2. The discrete random variables \(W\), \(X\) and \(Y\) are distributed as follows

\[W \sim \mathrm{B}(10, 0.4) \qquad\qquad X \sim \mathrm{Po}(4) \qquad\qquad Y \sim \mathrm{Po}(3)\]
(a) Explain whether or not \(\mathrm{Po}(4)\) would be a good approximation to \(\mathrm{B}(10, 0.4)\) (1)
(b) State the assumption required for \(X + Y\) to be distributed as \(\mathrm{Po}(7)\) (1)

Given the assumption in part (b) holds,

(c) find \(\mathrm{P}(X + Y \lt \mathrm{Var}(W))\) (2)

A2 October 2020 Q1

EdexcelCurrent spec13 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

1. The number of customers entering Jeff’s supermarket each morning follows a Poisson distribution.

Past information shows that customers enter at an average rate of 2 every 5 minutes.

Using this information,

(a)
(i) find the probability that exactly 26 customers enter Jeff’s supermarket during a randomly selected 1-hour period one morning, (2)
(ii) find the probability that at least 21 customers enter Jeff’s supermarket during a randomly selected 1-hour period one morning. (2)

A rival supermarket is opened nearby.  Following its opening, the number of customers entering Jeff’s supermarket over a randomly selected 40-minute period is found to be 10

(b) Test, at the 5% significance level, whether or not there is evidence of a decrease in the rate of customers entering Jeff’s supermarket.  State your hypotheses clearly. (4)

A further randomly selected 20-minute period is observed and the hypothesis test is repeated.
Given that the true rate of customers entering Jeff’s supermarket is now 1 every 5 minutes,

(c) calculate the probability of a Type II error. (5)

AS October 2020 Q1

EdexcelAS paperCurrent spec10 marksPoisson Distribution

1. A plumbing company receives call-outs during the working day at an average rate of 2.4 per hour.

(a) Find the probability that the company receives exactly 7 call-outs in a randomly selected 3-hour period of a working day. (2)

The company has enough staff to respond to 28 call-outs in an 8-hour working day.

(b) Show that the probability that the company receives more than 28 call-outs in a randomly selected 8-hour working day is 0.022 to 3 decimal places. (2)

In a random sample of 100 working days each of 8 hours,

(c)
(i) find the expected number of days that the company receives more than 28 call-outs, (1)
(ii) find the standard deviation of the number of days that the company receives more than 28 call-outs, (2)
(iii) use a Poisson approximation to estimate the probability that the company receives more than 28 call-outs on at least 6 of these days. (3)

A2 June 2019 Q5

EdexcelCurrent spec12 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

5. Information was collected about accidents on the Seapron bypass.  It was found that the number of accidents per month could be modelled by a Poisson distribution with mean 2.5

Following some work on the bypass, the numbers of accidents during a series of 3-month periods were recorded.  The data were used to test whether or not there was a change in the mean number of accidents per month.

(a) Stating your hypotheses clearly and using a 5% level of significance, find the critical region for this test.  You should state the probability in each tail. (5)
(b) State \(\mathrm{P}(\text{Type I error})\) using this test. (1)

Data from the series of 3-month periods are recorded for 2 years.

(c) Find the probability that at least 2 of these 3-month periods give a significant result. (3)

Given that the number of accidents per month on the bypass, after the work is completed, is actually 2.1 per month,

(d) find \(\mathrm{P}(\text{Type II error})\) for the test in part (a) (3)

AS June 2019 Q3

EdexcelAS paperCurrent spec13 marksIncludes hypothesis testingPoisson Distribution

3. Andreia’s secretary makes random errors in his work at an average rate of 1.7 errors every 100 words.

(a) Find the probability that the secretary makes fewer than 2 errors in the next 100-word piece of work. (2)

Andreia asks the secretary to produce a 250-word article for a magazine.

(b) Find the probability that there are exactly 5 errors in this article. (2)

Andreia offers the secretary a choice of one of two bonus schemes, based on a random sample of 40 pieces of work each consisting of 100 words.

In scheme A the secretary will receive the bonus if more than 10 of the 40 pieces of work contain no errors.

In scheme B the bonus is awarded if the total number of errors in all 40 pieces of work is fewer than 56

(c) Showing your calculations clearly, explain which bonus scheme you would advise the secretary to choose. (5)

Following the bonus scheme, Andreia randomly selects a single 500-word piece of work from the secretary to test if there is any evidence that the secretary’s rate of errors has decreased.

(d) Stating your hypotheses clearly and using a 5% level of significance, find the critical region for this test. (4)

A2 June 2019 Q2

EdexcelCurrent spec8 marksPoisson Distribution

2. Indre works on reception in an office and deals with all the telephone calls that arrive.  Calls arrive randomly and, in a 4-hour morning shift, there are on average 80 calls.

(a) Using a suitable model, find the probability of more than 4 calls arriving in a particular 20-minute period one morning. (3)

Indre is allowed 20 minutes of break time during each 4-hour morning shift, which she can take in 5-minute periods.  When she takes a break, a machine records details of any call in the office that Indre has missed.

One morning Indre took her break time in 4 periods of 5 minutes each.

(b) Find the probability that in exactly 3 of these periods there were no calls. (2)

On another occasion Indre took 1 break of 5 minutes and 1 break of 15 minutes.

(c) Find the probability that Indre missed exactly 1 call in each of these 2 breaks. (3)

AS June 2018 Q2

EdexcelAS paperCurrent spec11 marksIncludes hypothesis testingPoisson Distribution

2. The number of heaters, \(H\), bought during one day from Warmup supermarket can be modelled by a Poisson distribution with mean 0.7

(a) Calculate \(\mathrm{P}(H \geqslant 2)\) (1)

The number of heaters, \(G\), bought during one day from Pumraw supermarket can be modelled by a Poisson distribution with mean 3, where \(G\) and \(H\) are independent.

(b) Show that the probability that a total of fewer than 4 heaters are bought from these two supermarkets in a day is 0.494 to 3 decimal places. (2)
(c) Calculate the probability that a total of fewer than 4 heaters are bought from these two supermarkets on at least 5 out of 6 randomly chosen days. (3)

December was particularly cold. Two days in December were selected at random and the total number of heaters bought from these two supermarkets was found to be 14

(d) Test whether or not the mean of the total number of heaters bought from these two supermarkets had increased. Use a 5% level of significance and state your hypotheses clearly. (5)

AS June 2018 Q1

EdexcelAS paperCurrent spec10 marksIncludes hypothesis testingChi-Squared TestsPoisson Distribution

1. A researcher is investigating the distribution of orchids in a field. He believes that the Poisson distribution with a mean of 1.75 may be a good model for the number of orchids in each square metre. He randomly selects 150 non-overlapping areas, each of one square metre, and counts the number of orchids present in each square.

The results are recorded in the table below.

Number of orchids in each square metre0123456
Number of squares304235261160

He calculates the expected frequencies as follows

Number of orchids in each square metre012345More than 5
Number of squares26.0745.6239.9123.2810.193.57\(r\)
(a) Find the value of \(r\) giving your answer to 2 decimal places. (1)

The researcher will test, at the 5% level of significance, whether or not the data can be modelled by a Poisson distribution with mean 1.75

(b) State clearly the hypotheses required to test whether or not this Poisson distribution is a suitable model for these data. (1)

The test statistic for this test is 2.0 and the number of degrees of freedom to be used is 4

(c) Explain fully why there are 4 degrees of freedom. (2)
(d) Stating your critical value clearly, determine whether or not these data support the researcher’s belief. (2)

The researcher works in another field where the number of orchids in each square metre is known to have a Poisson distribution with mean 1.5

He randomly selects 200 non-overlapping areas, each of one square metre, in this second field, and counts the number of orchids present in each square.

(e) Using a Poisson approximation, show that the probability that he finds at least one square with exactly 6 orchids in it is 0.506 to 3 decimal places. (4)

S2 June 2018 Q1

EdexcelOld spec12 marksPoisson Distribution

1. In a call centre, the number of telephone calls, \(X\), received during any 10-minute period follows a Poisson distribution with mean 9

(a) Find
(i) \(\mathrm{P}(X \gt 5)\)
(ii) \(\mathrm{P}(4 \leqslant X \lt 10)\) (4)

The length of a working day is 7 hours.

(b) Using a suitable approximation, find the probability that there are fewer than 370 telephone calls in a randomly selected working day. (5)

A week, consisting of 5 working days, is selected at random.

(c) Find the probability that in this week at least 4 working days have fewer than 370 telephone calls. (3)

S4 June 2017 Q2

EdexcelOld spec8 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

2. The number of accidents per year in Daftstown follows a Poisson distribution with mean \(\lambda\). The value of \(\lambda\) has previously been 6 but Jonty claims that since the Council increased the speed limit, the value of \(\lambda\) has increased.

Jonty records the number of accidents in Daftstown in the first year after the speed limit was increased. He plans to test, at the 5% significance level, whether or not there is evidence of an increase in the mean number of accidents in Daftstown per year.

(a) Stating your hypotheses clearly, calculate the probability of a Type I error for this test. (4)

Given that there were 9 accidents in the first year after the speed limit was increased,

(b) state, giving a reason, whether or not there is evidence to support Jonty’s claim. (2)
(c) Given that the value of \(\lambda\) has actually increased to 8, calculate the probability of drawing the conclusion, using this test, that the number of accidents per year in Daftstown has not increased. (2)

S2 June 2017 Q2

EdexcelOld spec13 marksIncludes hypothesis testingPoisson Distribution

2. A company receives telephone calls at random at a mean rate of 2.5 per hour.

(a) Find the probability that the company receives
(i) at least 4 telephone calls in the next hour,
(ii) exactly 3 telephone calls in the next 15 minutes. (5)
(b) Find, to the nearest minute, the maximum length of time the telephone can be left unattended so that the probability of missing a telephone call is less than 0.2 (3)

The company puts an advert in the local newspaper. The number of telephone calls received in a randomly selected 2 hour period after the paper is published is 10

(c) Test at the 5% level of significance whether or not the mean rate of telephone calls has increased. State your hypotheses clearly. (5)

S2 June 2016 Q1

EdexcelOld spec14 marksPoisson Distribution

1. A student is investigating the numbers of cherries in a Rays fruit cake. A random sample of Rays fruit cakes is taken and the results are shown in the table below.

Number of cherries012345\(\geqslant 6\)
Frequency24372112420
(a) Calculate the mean and the variance of these data. (3)
(b) Explain why the results in part (a) suggest that a Poisson distribution may be a suitable model for the number of cherries in a Rays fruit cake. (1)

The number of cherries in a Rays fruit cake follows a Poisson distribution with mean 1.5

A Rays fruit cake is to be selected at random.

Find the probability that it contains

(c)
(i) exactly 2 cherries,
(ii) at least 1 cherry. (4)

Rays fruit cakes are sold in packets of 5

(d) Show that the probability that there are more than 10 cherries, in total, in a randomly selected packet of Rays fruit cakes, is 0.1378 correct to 4 decimal places. (3)

Twelve packets of Rays fruit cakes are selected at random.

(e) Find the probability that exactly 3 packets contain more than 10 cherries. (3)

S2 June 2015 Q5

EdexcelOld spec12 marksIncludes hypothesis testingPoisson Distribution

5. Liftsforall claims that the lift they maintain in a block of flats breaks down at random at a mean rate of 4 times per month. To test this, the number of times the lift breaks down in a month is recorded.

(a) Using a 5% level of significance, find the critical region for a two-tailed test of the null hypothesis that ‘the mean rate at which the lift breaks down is 4 times per month’. The probability of rejection in each of the tails should be as close to 2.5% as possible. (3)

Over a randomly selected 1 month period the lift broke down 3 times.

(b) Test, at the 5% level of significance, whether Liftsforall’s claim is correct. State your hypotheses clearly. (2)
(c) State the actual significance level of this test. (1)

The residents in the block of flats have a maintenance contract with Liftsforall. The residents pay Liftsforall £500 for every quarter (3 months) in which there are at most 3 breakdowns. If there are 4 or more breakdowns in a quarter then the residents do not pay for that quarter.

Liftsforall installs a new lift in the block of flats.

Given that the new lift breaks down at a mean rate of 2 times per month,

(d) find the probability that the residents do not pay more than £500 to Liftsforall in the next year. (6)

S2 June 2015 Q1

EdexcelOld spec11 marksPoisson Distribution

1. In a survey it is found that barn owls occur randomly at a rate of 9 per 1000 km2.

(a) Find the probability that in a randomly selected area of 1000 km2 there are at least 10 barn owls. (2)
(b) Find the probability that in a randomly selected area of 200 km2 there are exactly 2 barn owls. (3)
(c) Using a suitable approximation, find the probability that in a randomly selected area of 50 000 km2 there are at least 470 barn owls. (6)

S2 June 2014 (R) Q5

EdexcelOld spec13 marksIncludes hypothesis testingPoisson Distribution

5. Sammy manufactures wallpaper. She knows that defects occur randomly in the manufacturing process at a rate of 1 every 8 metres. Once a week the machinery is cleaned and reset. Sammy then takes a random sample of 40 metres of wallpaper from the next batch produced to test if there has been any change in the rate of defects.

(a) Stating your hypotheses clearly and using a 10% level of significance, find the critical region for this test. You should choose your critical region so that the probability of rejection is less than 0.05 in each tail. (4)
(b) State the actual significance level of this test. (2)

Thomas claims that his new machine would reduce the rate of defects and invites Sammy to test it. Sammy takes a random sample of 200 metres of wallpaper produced on Thomas’ machine and finds 19 defects.

(c) Using a suitable approximation, test Thomas’ claim. You should use a 5% level of significance and state your hypotheses clearly. (7)

S2 June 2014 (R) Q3

EdexcelOld spec10 marksPoisson Distribution

3. Accidents occur randomly at a road junction at a rate of 18 every year.
The random variable \(X\) represents the number of accidents at this road junction in the next 6 months.

(a) Write down the distribution of \(X\). (2)
(b) Find \(\mathrm{P}(X \gt 7)\). (2)
(c) Show that the probability of at least one accident in a randomly selected month is 0.777 (correct to 3 decimal places). (3)
(d) Find the probability that there is at least one accident in exactly 4 of the next 6 months. (3)

S4 June 2014 (R) Q2

EdexcelOld spec7 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

2. The cloth produced by a certain manufacturer has defects that occur randomly at a constant rate of \(\lambda\) per square metre. If \(\lambda\) is thought to be greater than 1.5 then action has to be taken.

Using \(\mathrm{H}_0 : \lambda = 1.5\) and \(\mathrm{H}_1 : \lambda \gt 1.5\) a quality control officer takes a 4 m\(^2\) sample of cloth and rejects \(\mathrm{H}_0\) if there are 11 or more defects. If there are 8 or fewer defects she accepts \(\mathrm{H}_0\). If there are 9 or 10 defects a second sample of 4 m\(^2\) is taken and \(\mathrm{H}_0\) is rejected if there are 11 or more defects in this second sample, otherwise it is accepted.

(a) Find the size of this test. (4)
(b) Find the power of this test when \(\lambda = 2\) (3)

S2 June 2014 Q3

EdexcelOld spec13 marksIncludes hypothesis testingPoisson Distribution

3. A company claims that it receives emails at a mean rate of 2 every 5 minutes.

(a) Give two reasons why a Poisson distribution could be a suitable model for the number of emails received. (2)
(b) Using a 5% level of significance, find the critical region for a two-tailed test of the hypothesis that the mean number of emails received in a 10 minute period is 4. The probability of rejection in each tail should be as close as possible to 0.025 (2)
(c) Find the actual level of significance of this test. (2)

To test this claim, the number of emails received in a random 10 minute period was recorded.

During this period 8 emails were received.

(d) Comment on the company’s claim in the light of this value. Justify your answer. (2)

During a randomly selected 15 minutes of play in the Wimbledon Men’s Tennis Tournament final, 2 emails were received by the company.

(e) Test, at the 10% level of significance, whether or not the mean rate of emails received by the company during the Wimbledon Men’s Tennis Tournament final is lower than the mean rate received at other times. State your hypotheses clearly. (5)

S4 June 2014 Q2

EdexcelOld spec7 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

2.

(a) Define
(i) a Type I error,
(ii) a Type II error. (2)

Rolls of material, manufactured by a machine, contain defects at a mean rate of 6 per roll.

The machine is modified. A single roll is selected at random and a test is carried out to see whether or not the mean number of defects per roll has decreased. The significance level is chosen to be as close as possible to 5%.

(b) Calculate the probability of a Type I error for this test. (3)
(c) Given that the true mean number of defects per roll of material made by the machine is now 4, calculate the probability of a Type II error. (2)

S2 June 2014 Q1

EdexcelOld spec8 marksPoisson Distribution

1. Patients arrive at a hospital accident and emergency department at random at a rate of 6 per hour.

(a) Find the probability that, during any 90 minute period, the number of patients arriving at the hospital accident and emergency department is
(i) exactly 7
(ii) at least 10
(5)

A patient arrives at 11.30 a.m.

(b) Find the probability that the next patient arrives before 11.45 a.m. (3)

S2 June 2013 (R) Q6

EdexcelOld spec14 marksIncludes hypothesis testingPoisson Distribution

6. Frugal bakery claims that their packs of 10 muffins contain on average 80 raisins per pack. A Poisson distribution is used to describe the number of raisins per muffin.

A muffin is selected at random to test whether or not the mean number of raisins per muffin has changed.

(a) Find the critical region for a two-tailed test using a 10% level of significance. The probability of rejection in each tail should be less than 0.05 (4)
(b) Find the actual significance level of this test. (2)

The bakery has a special promotion claiming that their muffins now contain even more raisins.

A random sample of 10 muffins is selected and is found to contain a total of 95 raisins.

(c) Use a suitable approximation to test the bakery’s claim. You should state your hypotheses clearly and use a 5% level of significance. (8)

S2 June 2013 (R) Q5

EdexcelOld spec13 marksPoisson Distribution

5. In a village shop the customers must join a queue to pay. The number of customers joining the queue in a 10 minute interval is modelled by a Poisson distribution with mean 3

Find the probability that

(a) exactly 4 customers join the queue in the next 10 minutes, (2)
(b) more than 10 customers join the queue in the next 20 minutes. (3)

When a customer reaches the front of the queue the customer pays the assistant. The time each customer takes paying the assistant, \(T\) minutes, has a continuous uniform distribution over the interval [0, 5]. The random variable \(T\) is independent of the number of people joining the queue.

(c) Find \(\mathrm{P}(T \gt 3.5)\) (1)

In a random sample of 5 customers, the random variable \(C\) represents the number of customers who took more than 3.5 minutes paying the assistant.

(d) Find \(\mathrm{P}(C \geqslant 3)\) (3)

Bethan has just reached the front of the queue and starts paying the assistant.

(e) Find the probability that in the next 4 minutes Bethan finishes paying the assistant and no other customers join the queue. (4)

S4 June 2013 (R) Q3

EdexcelOld spec10 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

3. The number of houses sold per week by a firm of estate agents follows a Poisson distribution with mean 2. The firm believes that the appointment of a new salesman will increase the number of houses sold. The firm tests its belief by recording the number of houses sold, \(x\), in the week following the appointment. The firm sets up the hypotheses \(\mathrm{H}_0 : \lambda = 2\) and \(\mathrm{H}_1 : \lambda \gt 2\), where \(\lambda\) is the mean number of houses sold per week, and rejects the null hypothesis if \(x \geqslant 3\)

(a) Find the size of the test. (2)
(b) Show that the power function for this test is \[1 - \frac{1}{2}\mathrm{e}^{-\lambda}(2 + 2\lambda + \lambda^2)\] (3)

The table below gives the values of the power function to 2 decimal places.

\(\lambda\)2.53.03.54.05.07.0
Power0.46\(r\)0.68\(s\)0.880.97

Table 1

(c) Calculate the values of \(r\) and \(s\). (2)
(d) Draw a graph of the power function. (2)
(e) Find the range of values of \(\lambda\) for which the power of this test is greater than 0.6 (1)

S4 June 2013 Q5

EdexcelOld spec17 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

5. Water is tested at various stages during a purification process by an environmental scientist. A certain organism occurs randomly in the water at a rate of \(\lambda\) every 10 ml. The scientist selects a random sample of 20 ml of water to check whether there is evidence that \(\lambda\) is greater than 1. The criterion the scientist uses for rejecting the hypothesis that \(\lambda = 1\) is that there are 4 or more organisms in the sample of 20 ml.

(a) Find the size of the test. (2)
(b) When \(\lambda = 2.5\) find P(Type II error). (2)

A statistician suggests using an alternative test. The statistician’s test involves taking a random sample of 10 ml and rejecting the hypothesis that \(\lambda = 1\) if 2 or more organisms are present but accepting the hypothesis if no organisms are in the sample. If only 1 organism is found then a second random sample of 10 ml is taken and the hypothesis is rejected if 2 or more organisms are present, otherwise the hypothesis is accepted.

(c) Show that the power of the statistician’s test is given by \[1 - \mathrm{e}^{-\lambda} - \lambda(1 + \lambda)\mathrm{e}^{-2\lambda}\] (4)

Table 1 below gives some values, to 2 decimal places, of the power function of the statistician’s test.

\(\lambda\)1.522.533.54
Power0.590.750.86\(r\)0.960.97

Table 1

(d) Find the value of \(r\). (1)

Figure 1 shows a graph of the power function for the scientist’s test.

Figure 1: graph of power against lambda for the scientist’s test, lambda from 1.5 to 4
Figure 1
(e) On the same axes draw the graph of the power function for the statistician’s test. (2)

Given that it takes 20 minutes to collect and test a 20 ml sample and 15 minutes to collect and test a 10 ml sample

(f) show that the expected time of the statistician’s test is slower than the scientist’s test for \(\lambda\mathrm{e}^{-\lambda} \gt \dfrac{1}{3}\) (4)
(g) By considering the times when \(\lambda = 1\) and \(\lambda = 2\) together with the power curves in part (e) suggest, giving a reason, which test you would use. (2)

S2 June 2013 Q3

EdexcelOld spec13 marksIncludes hypothesis testingPoisson Distribution

3. An online shop sells a computer game at an average rate of 1 per day.

(a) Find the probability that the shop sells more than 10 games in a 7 day period. (3)

Once every 7 days the shop has games delivered before it opens.

(b) Find the least number of games the shop should have in stock immediately after a delivery so that the probability of running out of the game before the next delivery is less than 0.05 (3)

In an attempt to increase sales of the computer game, the price is reduced for six months. A random sample of 28 days is taken from these six months. In the sample of 28 days, 36 computer games are sold.

(c) Using a suitable approximation and a 5% level of significance, test whether or not the average rate of sales per day has increased during these six months. State your hypotheses clearly. (7)

S2 June 2013 Q2

EdexcelOld spec10 marksPoisson Distribution

2. The number of defects per metre in a roll of cloth has a Poisson distribution with mean 0.25

Find the probability that

(a) a randomly chosen metre of cloth has 1 defect, (2)
(b) the total number of defects in a randomly chosen 6 metre length of cloth is more than 2 (3)

A tailor buys 300 metres of cloth.

(c) Using a suitable approximation find the probability that the tailor’s cloth will contain less than 90 defects. (5)

S2 January 2013 Q2

EdexcelOld spec11 marksPoisson Distribution

2. In a village, power cuts occur randomly at a rate of 3 per year.

(a) Find the probability that in any given year there will be
(i) exactly 7 power cuts,
(ii) at least 4 power cuts.
(5)
(b) Use a suitable approximation to find the probability that in the next 10 years the number of power cuts will be less than 20 (6)

S2 January 2013 Q1

EdexcelOld spec5 marksPoisson Distribution

1.

(a) Write down the conditions under which the Poisson distribution can be used as an approximation to the binomial distribution. (2)

The probability of any one letter being delivered to the wrong house is 0.01

On a randomly selected day Peter delivers 1000 letters.

(b) Using a Poisson approximation, find the probability that Peter delivers at least 4 letters to the wrong house.
Give your answer to 4 decimal places. (3)

S2 June 2012 Q4

EdexcelOld spec14 marksPoisson Distribution

4. The number of houses sold by an estate agent follows a Poisson distribution, with a mean of 2 per week.

(a) Find the probability that in the next 4 weeks the estate agent sells,
(i) exactly 3 houses,
(ii) more than 5 houses.
(5)

The estate agent monitors sales in periods of 4 weeks.

(b) Find the probability that in the next twelve of these 4 week periods there are exactly nine periods in which more than 5 houses are sold. (3)

The estate agent will receive a bonus if he sells more than 25 houses in the next 10 weeks.

(c) Use a suitable approximation to estimate the probability that the estate agent receives a bonus. (6)

S2 June 2012 Q3

EdexcelOld spec9 marksIncludes hypothesis testingPoisson Distribution

3.

(a) Write down two conditions needed to approximate the binomial distribution by the Poisson distribution. (2)

A machine which manufactures bolts is known to produce 3% defective bolts. The machine breaks down and a new machine is installed. A random sample of 200 bolts is taken from those produced by the new machine and 12 bolts were defective.

(b) Using a suitable approximation, test at the 5% level of significance whether or not the proportion of defective bolts is higher with the new machine than with the old machine. State your hypotheses clearly. (7)

S2 January 2012 Q7

EdexcelOld spec10 marksIncludes hypothesis testingPoisson Distribution

7.

(a) Explain briefly what you understand by
(i) a critical region of a test statistic,
(ii) the level of significance of a hypothesis test. (2)
(b) An estate agent has been selling houses at a rate of 8 per month. She believes that the rate of sales will decrease in the next month.
(i) Using a 5% level of significance, find the critical region for a one tailed test of the hypothesis that the rate of sales will decrease from 8 per month.
(ii) Write down the actual significance level of the test in part (b)(i). (3)

The estate agent is surprised to find that she actually sold 13 houses in the next month. She now claims that this is evidence of an increase in the rate of sales per month.

(c) Test the estate agent’s claim at the 5% level of significance. State your hypotheses clearly. (5)

S2 January 2012 Q5

EdexcelOld spec7 marksPoisson Distribution

5. The probability of an electrical component being defective is 0.075

The component is supplied in boxes of 120

(a) Using a suitable approximation, estimate the probability that there are more than 3 defective components in a box. (5)

A retailer buys 2 boxes of components.

(b) Estimate the probability that there are at least 4 defective components in each box. (2)

S2 January 2012 Q4

EdexcelOld spec16 marksPoisson Distribution

4. A website receives hits at a rate of 300 per hour.

(a) State a distribution that is suitable to model the number of hits obtained during a 1 minute interval. (1)
(b) State two reasons for your answer to part (a). (2)

Find the probability of

(c) 10 hits in a given minute, (3)
(d) at least 15 hits in 2 minutes. (3)

The website will go down if there are more than 70 hits in 10 minutes.

(e) Using a suitable approximation, find the probability that the website will go down in a particular 10 minute interval. (7)

S3 June 2011 Q5

EdexcelOld spec13 marksIncludes hypothesis testingChi-Squared TestsPoisson Distribution

5. The number of hurricanes per year in a particular region was recorded over 80 years. The results are summarised in Table 1 below.

No of hurricanes, \(h\)01234567
Frequency0251720121212

Table 1

(a) Write down two assumptions that will support modelling the number of hurricanes per year by a Poisson distribution. (2)
(b) Show that the mean number of hurricanes per year from Table 1 is 4.4875 (2)
(c) Use the answer in part (b) to calculate the expected frequencies \(r\) and \(s\) given in Table 2 below to 2 decimal places. (3)
\(h\)01234567 or more
Expected frequency0.904.04\(r\)13.55\(s\)13.6510.2113.39

Table 2

(d) Test, at the 5% level of significance, whether or not the data can be modelled by a Poisson distribution. State your hypotheses clearly. (6)

S2 June 2011 Q5

EdexcelOld spec13 marksPoisson Distribution

5. Defects occur at random in planks of wood with a constant rate of 0.5 per 10 cm length. Jim buys a plank of length 100 cm.

(a) Find the probability that Jim’s plank contains at most 3 defects. (2)

Shivani buys 6 planks each of length 100 cm.

(b) Find the probability that fewer than 2 of Shivani’s planks contain at most 3 defects. (5)
(c) Using a suitable approximation, estimate the probability that the total number of defects on Shivani’s 6 planks is less than 18. (6)

S2 June 2011 Q2

EdexcelOld spec10 marksIncludes hypothesis testingPoisson Distribution

2. A traffic officer monitors the rate at which vehicles pass a fixed point on a motorway. When the rate exceeds 36 vehicles per minute he must switch on some speed restrictions to improve traffic flow.

(a) Suggest a suitable model to describe the number of vehicles passing the fixed point in a 15 s interval. (1)

The traffic officer records 12 vehicles passing the fixed point in a 15 s interval.

(b) Stating your hypotheses clearly, and using a 5% level of significance, test whether or not the traffic officer has sufficient evidence to switch on the speed restrictions. (6)
(c) Using a 5% level of significance, determine the smallest number of vehicles the traffic officer must observe in a 10 s interval in order to have sufficient evidence to switch on the speed restrictions. (3)

S2 January 2011 Q6

EdexcelOld spec16 marksPoisson Distribution

6. Cars arrive at a motorway toll booth at an average rate of 150 per hour.

(a) Suggest a suitable distribution to model the number of cars arriving at the toll booth, \(X\), per minute. (2)
(b) State clearly any assumptions you have made by suggesting this model. (2)

Using your model,

(c) find the probability that in any given minute
(i) no cars arrive,
(ii) more than 3 cars arrive. (3)
(d) In any given 4 minute period, find \(m\) such that \(\mathrm{P}(X \gt m) = 0.0487\) (3)
(e) Using a suitable approximation find the probability that fewer than 15 cars arrive in any given 10 minute period. (6)

S2 January 2011 Q4

EdexcelOld spec6 marksIncludes hypothesis testingPoisson Distribution

4. Richard regularly travels to work on a ferry. Over a long period of time, Richard has found that the ferry is late on average 2 times every week. The company buys a new ferry to improve the service. In the 4-week period after the new ferry is launched, Richard finds the ferry is late 3 times and claims the service has improved. Assuming that the number of times the ferry is late has a Poisson distribution, test Richard’s claim at the 5% level of significance. State your hypotheses clearly. (6)

S4 June 2010 Q6

EdexcelOld spec14 marksPoisson Distribution

6. Faults occur in a roll of material at a rate of \(\lambda\) per m2. To estimate \(\lambda\), three pieces of material of sizes 3 m2, 7 m2 and 10 m2 are selected and the number of faults \(X_1\), \(X_2\) and \(X_3\) respectively are recorded.

The estimator \(\hat{\lambda}\), where

\[\hat{\lambda} = k(X_1 + X_2 + X_3)\]

is an unbiased estimator of \(\lambda\).

(a) Write down the distributions of \(X_1\), \(X_2\) and \(X_3\) and find the value of \(k\). (4)
(b) Find \(\mathrm{Var}(\hat{\lambda})\). (3)

A random sample of \(n\) pieces of this material, each of size 4 m2, was taken. The number of faults on each piece, \(Y\), was recorded.

(c) Show that \(\dfrac{1}{4}\bar{Y}\) is an unbiased estimator of \(\lambda\). (2)
(d) Find \(\mathrm{Var}\left(\dfrac{1}{4}\bar{Y}\right)\). (3)
(e) Find the minimum value of \(n\) for which \(\dfrac{1}{4}\bar{Y}\) becomes a better estimator of \(\lambda\) than \(\hat{\lambda}\). (2)

S2 June 2010 Q5

EdexcelOld spec15 marksIncludes hypothesis testingPoisson Distribution

5. A company has a large number of regular users logging onto its website. On average 4 users every hour fail to connect to the company’s website at their first attempt.

(a) Explain why the Poisson distribution may be a suitable model in this case. (1)

Find the probability that, in a randomly chosen 2 hour period,

(b)
(i) all users connect at their first attempt,
(ii) at least 4 users fail to connect at their first attempt. (5)

The company suffered from a virus infecting its computer system. During this infection it was found that the number of users failing to connect at their first attempt, over a 12 hour period, was 60.

(c) Using a suitable approximation, test whether or not the mean number of users per hour who failed to connect at their first attempt had increased. Use a 5% level of significance and state your hypotheses clearly. (9)

S2 January 2010 Q5

EdexcelOld spec9 marksPoisson Distribution

5. A café serves breakfast every morning. Customers arrive for breakfast at random at a rate of 1 every 6 minutes.

Find the probability that

(a) fewer than 9 customers arrive for breakfast on a Monday morning between 10 am and 11 am. (3)

The café serves breakfast every day between 8 am and 12 noon.

(b) Using a suitable approximation, estimate the probability that more than 50 customers arrive for breakfast next Tuesday. (6)

S2 January 2010 Q3

EdexcelOld spec10 marksPoisson Distribution

3. A robot is programmed to build cars on a production line. The robot breaks down at random at a rate of once every 20 hours.

(a) Find the probability that it will work continuously for 5 hours without a breakdown. (3)

Find the probability that, in an 8 hour period,

(b) the robot will break down at least once, (3)
(c) there are exactly 2 breakdowns. (2)

In a particular 8 hour period, the robot broke down twice.

(d) Write down the probability that the robot will break down in the following 8 hour period. Give a reason for your answer. (2)

S2 June 2009 Q8

EdexcelOld spec13 marksPoisson Distribution

8. A cloth manufacturer knows that faults occur randomly in the production process at a rate of 2 every 15 metres.

(a) Find the probability of exactly 4 faults in a 15 metre length of cloth. (2)
(b) Find the probability of more than 10 faults in 60 metres of cloth. (3)

A retailer buys a large amount of this cloth and sells it in pieces of length \(x\) metres. He chooses \(x\) so that the probability of no faults in a piece is 0.80

(c) Write down an equation for \(x\) and show that \(x = 1.7\) to 2 significant figures. (4)

The retailer sells 1200 of these pieces of cloth. He makes a profit of 60p on each piece of cloth that does not contain a fault but a loss of £1.50 on any pieces that do contain faults.

(d) Find the retailer’s expected profit. (4)

S3 June 2009 Q5

EdexcelOld spec12 marksIncludes hypothesis testingChi-Squared TestsPoisson Distribution

5. The number of goals scored by a football team is recorded for 100 games. The results are summarised in Table 1 below.

Number of goalsFrequency
040
133
214
38
45

Table 1

(a) Calculate the mean number of goals scored per game. (2)

The manager claimed that the number of goals scored per match follows a Poisson distribution. He used the answer in part (a) to calculate the expected frequencies given in Table 2.

Number of goalsExpected Frequency
034.994
1\(r\)
2\(s\)
36.752
\(\geqslant 4\)2.221

Table 2

(b) Find the value of \(r\) and the value of \(s\) giving your answers to 3 decimal places. (3)
(c) Stating your hypotheses clearly, use a 5% level of significance to test the manager’s claim. (7)

S2 June 2009 Q5

EdexcelOld spec10 marksPoisson Distribution

5. An administrator makes errors in her typing randomly at a rate of 3 errors every 1000 words.

(a) In a document of 2000 words find the probability that the administrator makes 4 or more errors. (3)

The administrator is given an 8000 word report to type and she is told that the report will only be accepted if there are 20 or fewer errors.

(b) Use a suitable approximation to calculate the probability that the report is accepted. (7)

S2 June 2009 Q2

EdexcelOld spec6 marksIncludes hypothesis testingPoisson Distribution

2. An effect of a certain disease is that a small number of the red blood cells are deformed. Emily has this disease and the deformed blood cells occur randomly at a rate of 2.5 per ml of her blood. Following a course of treatment, a random sample of 2 ml of Emily’s blood is found to contain only 1 deformed red blood cell.

Stating your hypotheses clearly and using a 5% level of significance, test whether or not there has been a decrease in the number of deformed red blood cells in Emily’s blood. (6)

S2 June 2009 Q1

EdexcelOld spec5 marksPoisson Distribution

1. A bag contains a large number of counters of which 15% are coloured red. A random sample of 30 counters is selected and the number of red counters is recorded.

(a) Find the probability of no more than 6 red counters in this sample. (2)

A second random sample of 30 counters is selected and the number of red counters is recorded.

(b) Using a Poisson approximation, estimate the probability that the total number of red counters in the combined sample of size 60 is less than 13. (3)

S2 January 2009 Q6

EdexcelOld spec14 marksIncludes hypothesis testingPoisson Distribution

6. A web server is visited on weekdays, at a rate of 7 visits per minute. In a random one minute on a Saturday the web server is visited 10 times.

(a)
(i) Test, at the 10% level of significance, whether or not there is evidence that the rate of visits is greater on a Saturday than on weekdays. State your hypotheses clearly.
(ii) State the minimum number of visits required to obtain a significant result. (7)
(b) State an assumption that has been made about the visits to the server. (1)

In a random two minute period on a Saturday the web server is visited 20 times.

(c) Using a suitable approximation, test at the 10% level of significance, whether or not the rate of visits is greater on a Saturday. (6)

S2 January 2009 Q5

EdexcelOld spec9 marksPoisson Distribution

5. A factory produces components of which 1% are defective. The components are packed in boxes of 10. A box is selected at random.

(a) Find the probability that the box contains exactly one defective component. (2)
(b) Find the probability that there are at least 2 defective components in the box. (3)
(c) Using a suitable approximation, find the probability that a batch of 250 components contains between 1 and 4 (inclusive) defective components. (4)

S2 January 2009 Q1

EdexcelOld spec11 marksPoisson Distribution

1. A botanist is studying the distribution of daisies in a field. The field is divided into a number of equal sized squares. The mean number of daisies per square is assumed to be 3. The daisies are distributed randomly throughout the field.

Find the probability that, in a randomly chosen square there will be

(a) more than 2 daisies, (3)
(b) either 5 or 6 daisies. (2)

The botanist decides to count the number of daisies, \(x\), in each of 80 randomly selected squares within the field. The results are summarised below

\[\textstyle\sum x = 295 \qquad \sum x^2 = 1386\]
(c) Calculate the mean and the variance of the number of daisies per square for the 80 squares. Give your answers to 2 decimal places. (3)
(d) Explain how the answers from part (c) support the choice of a Poisson distribution as a model. (1)
(e) Using your mean from part (c), estimate the probability that exactly 4 daisies will be found in a randomly selected square. (2)

S2 June 2008 Q6

EdexcelOld spec13 marksIncludes hypothesis testingPoisson Distribution

6. A call centre agent handles telephone calls at a rate of 18 per hour.

(a) Give two reasons to support the use of a Poisson distribution as a suitable model for the number of calls per hour handled by the agent. (2)
(b) Find the probability that in any randomly selected 15 minute interval the agent handles
(i) exactly 5 calls,
(ii) more than 8 calls. (5)

The agent received some training to increase the number of calls handled per hour. During a randomly selected 30 minute interval after the training the agent handles 14 calls.

(c) Test, at the 5% level of significance, whether or not there is evidence to support the suggestion that the rate at which the agent handles calls has increased. State your hypotheses clearly. (6)

S2 June 2008 Q4

EdexcelOld spec8 marksPoisson Distribution

4. Each cell of a certain animal contains 11000 genes. It is known that each gene has a probability 0.0005 of being damaged.

A cell is chosen at random.

(a) Suggest a suitable model for the distribution of the number of damaged genes in the cell. (2)
(b) Find the mean and variance of the number of damaged genes in the cell. (2)
(c) Using a suitable approximation, find the probability that there are at most 2 damaged genes in the cell. (4)

S2 June 2008 Q3

EdexcelOld spec5 marksIncludes hypothesis testingPoisson Distribution

3. A test statistic has a Poisson distribution with parameter \(\lambda\).

Given that

\[\mathrm{H}_0 : \lambda = 9,\ \ \mathrm{H}_1 : \lambda \ne 9\]
(a) find the critical region for the test statistic such that the probability in each tail is as close as possible to 2.5%. (3)
(b) State the probability of incorrectly rejecting \(\mathrm{H}_0\) using this critical region. (2)

S2 January 2008 Q7

EdexcelOld spec14 marksIncludes hypothesis testingPoisson Distribution

7.

(a) Explain what you understand by
(i) a hypothesis test,
(ii) a critical region. (3)

During term time, incoming calls to a school are thought to occur at a rate of 0.45 per minute. To test this, the number of calls during a random 20 minute interval, is recorded.

(b) Find the critical region for a two-tailed test of the hypothesis that the number of incoming calls occurs at a rate of 0.45 per 1 minute interval. The probability in each tail should be as close to 2.5% as possible. (5)
(c) Write down the actual significance level of the above test. (1)

In the school holidays, 1 call occurs in a 10 minute interval.

(d) Test, at the 5% level of significance, whether or not there is evidence that the rate of incoming calls is less during the school holidays than in term time. (5)

S2 January 2008 Q3

EdexcelOld spec11 marksPoisson Distribution

3.

(a) State two conditions under which a Poisson distribution is a suitable model to use in statistical work. (2)

The number of cars passing an observation point in a 10 minute interval is modelled by a Poisson distribution with mean 1.

(b) Find the probability that in a randomly chosen 60 minute period there will be
(i) exactly 4 cars passing the observation point,
(ii) at least 5 cars passing the observation point. (5)

The number of other vehicles, other than cars, passing the observation point in a 60 minute interval is modelled by a Poisson distribution with mean 12.

(c) Find the probability that exactly 1 vehicle, of any type, passes the observation point in a 10 minute period. (4)

S4 June 2007 Q5

EdexcelOld spec7 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

5. The number of tornadoes per year to hit a particular town follows a Poisson distribution with mean \(\lambda\). A weatherman claims that due to climate changes the mean number of tornadoes per year has decreased. He records the number of tornadoes \(x\) to hit the town last year.

To test the hypotheses \(\mathrm{H}_0 : \lambda = 7\) and \(\mathrm{H}_1 : \lambda \lt 7\), a critical region of \(x \leqslant 3\) is used.

(a) Find, in terms \(\lambda\) the power function of this test. (3)
(b) Find the size of this test. (2)
(c) Find the probability of a Type II error when \(\lambda = 4\). (2)

S2 June 2007 Q5

EdexcelOld spec12 marksPoisson Distribution

5.

(a) Write down the conditions under which the Poisson distribution may be used as an approximation to the Binomial distribution. (2)

A call centre routes incoming telephone calls to agents who have specialist knowledge to deal with the call. The probability of the caller being connected to the wrong agent is 0.01

(b) Find the probability that 2 consecutive calls will be connected to the wrong agent. (2)
(c) Find the probability that more than 1 call in 5 consecutive calls are connected to the wrong agent. (3)

The call centre receives 1000 calls each day.

(d) Find the mean and variance of the number of wrongly connected calls. (3)
(e) Use a Poisson approximation to find, to 3 decimal places, the probability that more than 6 calls each day are connected to the wrong agent. (2)

S2 June 2007 Q3

EdexcelOld spec8 marksPoisson Distribution

3. An engineering company manufactures an electronic component. At the end of the manufacturing process, each component is checked to see if it is faulty.

Faulty components are detected at a rate of 1.5 per hour.

(a) Suggest a suitable model for the number of faulty components detected per hour. (1)
(b) Describe, in the context of this question, two assumptions you have made in part (a) for this model to be suitable. (2)
(c) Find the probability of 2 faulty components being detected in a 1 hour period. (2)
(d) Find the probability of at least one faulty component being detected in a 3 hour period. (3)

S2 June 2007 Q2

EdexcelOld spec7 marksIncludes hypothesis testingPoisson Distribution

2. Bacteria are randomly distributed in a river at a rate of 5 per litre of water. A new factory opens and a scientist claims it is polluting the river with bacteria. He takes a sample of 0.5 litres of water from the river near the factory and finds that it contains 7 bacteria. Stating your hypotheses clearly test, at the 5% level of significance, the claim of the scientist. (7)

S2 January 2007 Q4

EdexcelOld spec12 marksPoisson Distribution

4.

(a) State the condition under which the normal distribution may be used as an approximation to the Poisson distribution. (1)
(b) Explain why a continuity correction must be incorporated when using the normal distribution as an approximation to the Poisson distribution. (1)

A company has yachts that can only be hired for a week at a time. All hiring starts on a Saturday.

During the winter the mean number of yachts hired per week is 5.

(c) Calculate the probability that fewer than 3 yachts are hired on a particular Saturday in winter. (2)

During the summer the mean number of yachts hired per week increases to 25.

The company has only 30 yachts for hire.

(d) Using a suitable approximation find the probability that the demand for yachts cannot be met on a particular Saturday in the summer. (6)

In the summer there are 16 Saturdays on which a yacht can be hired.

(e) Estimate the number of Saturdays in the summer that the company will not be able to meet the demand for yachts. (2)

S4 June 2006 Q5

EdexcelOld spec17 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

5. Rolls of cloth delivered to a factory contain defects at an average rate of \(\lambda\) per metre. A quality assurance manager selects a random sample of 15 metres of cloth from each delivery to test whether or not there is evidence that \(\lambda \gt 0.3\). The criterion that the manager uses for rejecting the hypothesis that \(\lambda = 0.3\) is that there are 9 or more defects in the sample.

(a) Find the size of the test. (2)

Table 1 gives some values, to 2 decimal places, of the power function of this test.

\(\lambda\)0.40.50.60.70.80.91.0
Power0.150.34\(r\)0.720.850.920.96

Table 1

(b) Find the value of \(r\). (2)

The manager would like to design a test, of whether or not \(\lambda \gt 0.3\), that uses a smaller length of cloth. He chooses a length of 10 m and requires the probability of a type I error to be less than 10%.

(c) Find the criterion to reject the hypothesis that \(\lambda = 0.3\) which makes the test as powerful as possible. (2)
(d) Hence state the size of this second test. (1)

Table 2 gives some values, to 2 decimal places, of the power function for the test in part (c).

\(\lambda\)0.40.50.60.70.80.91.0
Power0.210.380.550.70\(s\)0.880.93

Table 2

(e) Find the value of \(s\). (2)
(f) Using the same axes, on graph paper draw the graphs of the power functions of these two tests. (4)
(g)
(i) State the value of \(\lambda\) where the graphs cross.
(ii) Explain the significance of \(\lambda\) being greater than this value. (2)

The cost of wrongly rejecting a delivery of cloth with \(\lambda = 0.3\) is low. Deliveries of cloth with \(\lambda \gt 0.7\) are unusual.

(h) Suggest, giving your reasons, which the test manager should adopt. (2)

S2 June 2006 Q4

EdexcelOld spec11 marksIncludes hypothesis testingPoisson Distribution

4. Breakdowns occur on a particular machine at random at a mean rate of 1.25 per week.

(a) Find the probability that fewer than 3 breakdowns occurred in a randomly chosen week. (4)

Over a 4 week period the machine was monitored. During this time there were 11 breakdowns.

(b) Test, at the 5% level of significance, whether or not there is evidence that the rate of breakdowns has changed over this period. State your hypotheses clearly. (7)

S2 June 2006 Q3

EdexcelOld spec11 marksPoisson Distribution

3. An estate agent sells properties at a mean rate of 7 per week.

(a) Suggest a suitable model to represent the number of properties sold in a randomly chosen week. Give two reasons to support your model. (3)
(b) Find the probability that in any randomly chosen week the estate agent sells exactly 5 properties. (2)
(c) Using a suitable approximation find the probability that during a 24 week period the estate agent sells more than 181 properties. (6)

S3 January 2006 Q6

EdexcelOld spec13 marksIncludes hypothesis testingChi-Squared TestsPoisson Distribution

6. An area of grass was sampled by placing a 1 m × 1 m square randomly in 100 places. The numbers of daisies in each of the squares were counted. It was decided that the resulting data could be modelled by a Poisson distribution with mean 2. The expected frequencies were calculated using the model.

The following table shows the observed and expected frequencies.

Number of daisiesObserved frequencyExpected frequency
0813.53
13227.07
227\(r\)
318\(s\)
4109.02
533.61
611.20
700.34
\(\geqslant 8\)1\(t\)
(a) Find values for \(r\), \(s\) and \(t\). (4)
(b) Using a 5% significance level, test whether or not this Poisson model is suitable. State your hypotheses clearly. (7)

An alternative test might have been to estimate the population mean by using the data given.

(c) Explain how this would have affected the test. (2)

S4 January 2006 Q4

EdexcelOld spec6 marksIncludes hypothesis testingPoisson DistributionQuality of Tests

4. The number of accidents that occur at a crossroads has a mean of 3 per month. In order to improve the flow of traffic the priority given to traffic is changed. Colin believes that since the change in priority the number of accidents has increased. He tests his belief by recording the number of accidents \(x\) in the month following the change. Colin sets up the hypotheses \(\mathrm{H}_0 : \lambda = 3\) and \(\mathrm{H}_1 : \lambda \gt 3\), where \(\lambda\) is the mean number of accidents per month, and rejects the null hypothesis if \(x \gt 4\).

(a) Find the size of the test. (3)

The table gives the values of the power function of the test to two decimal places.

\(\lambda\)4567
Power\(r\)0.56\(s\)0.83
(b) Calculate the value of \(r\) and the value of \(s\). (2)
(c) Comment on the suitability of the test when \(\lambda = 4\). (1)

S2 January 2006 Q4

EdexcelOld spec4 marksPoisson Distribution

4. The random variable \(X \sim \mathrm{B}(150, 0.02)\).

Use a suitable approximation to estimate \(\mathrm{P}(X \gt 7)\). (4)

S2 January 2006 Q2

EdexcelOld spec9 marksPoisson Distribution

2. Accidents on a particular stretch of motorway occur at an average rate of 1.5 per week.

(a) Write down a suitable model to represent the number of accidents per week on this stretch of motorway. (1)

Find the probability that

(b) there will be 2 accidents in the same week, (2)
(c) there is at least one accident per week for 3 consecutive weeks, (3)
(d) there are more than 4 accidents in a 2 week period. (2)

S3 June 2005 Q5

EdexcelOld spec12 marksIncludes hypothesis testingChi-Squared TestsPoisson Distribution

5. The number of times per day a computer fails and has to be restarted is recorded for 200 days. The results are summarised in the table.

Number of restartsFrequency
099
165
222
312
42

Test whether or not a Poisson model is suitable to represent the number of restarts per day. Use a 5% level of significance and state your hypothesis clearly. (12)

S2 June 2005 Q5

EdexcelOld spec7 marksPoisson Distribution

5. In a manufacturing process, 2% of the articles produced are defective. A batch of 200 articles is selected.

(a) Giving a justification for your choice, use a suitable approximation to estimate the probability that there are exactly 5 defective articles. (5)
(b) Estimate the probability there are less than 5 defective articles. (2)

S2 June 2005 Q3

EdexcelOld spec14 marksPoisson Distribution

3. The random variable \(X\) is the number of misprints per page in the first draft of a novel.

(a) State two conditions under which a Poisson distribution is a suitable model for \(X\). (2)

The number of misprints per page has a Poisson distribution with mean 2.5. Find the probability that

(b) a randomly chosen page has no misprints, (2)
(c) the total number of misprints on 2 randomly chosen pages is more than 7. (3)

The first chapter contains 20 pages.

(d) Using a suitable approximation find, to 2 decimal places, the probability that the chapter will contain less than 40 misprints. (7)

S2 January 2005 Q6

EdexcelOld spec16 marksIncludes hypothesis testingPoisson Distribution

6. Over a long period of time, accidents happened on a stretch of road at random at a rate of 3 per month.

Find the probability that

(a) in a randomly chosen month, more than 4 accidents occurred, (3)
(b) in a three-month period, more than 4 accidents occurred. (2)

At a later date, a speed restriction was introduced on this stretch of road. During a randomly chosen month only one accident occurred.

(c) Test, at the 5% level of significance, whether or not there is evidence to support the claim that this speed restriction reduced the mean number of road accidents occurring per month. (4)

The speed restriction was kept on this road. Over a two-year period, 55 accidents occurred.

(d) Test, at the 5% level of significance, whether or not there is now evidence that this speed restriction reduced the mean number of road accidents occurring per month. (7)

S2 January 2005 Q5

EdexcelOld spec13 marksPoisson Distribution

5. From company records, a manager knows that the probability that a defective article is produced by a particular production line is 0.032.

A random sample of 10 articles is selected from the production line.

(a) Find the probability that exactly 2 of them are defective. (3)

On another occasion, a random sample of 100 articles is taken.

(b) Using a suitable approximation, find the probability that fewer than 4 of them are defective. (4)

At a later date, a random sample of 1000 is taken.

(c) Using a suitable approximation, find the probability that more than 42 are defective. (6)