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Binomial and Poisson Distributions for CUET Applied Maths

About 5 min to learn · 68 questions

The binomial and Poisson distributions are part of Section B2 of CUET Maths, the Applied Mathematics section you answer if you choose Applied Mathematics instead of Section B1. Two formulas and their means and variances cover the whole chapter, and CUET prints any value of e you need. Learn each idea here, from Bernoulli trials and the binomial formula to its mean and variance, the Poisson formula and when it applies, with worked examples, time-saving shortcuts and the traps CUET sets, then practise on free questions in CUET's own formats, each fully solved.

Syllabus area
Section B2: Applied Mathematics
On this page · 9 sections
  1. 01Bernoulli trials
  2. 02Binomial probabilities
  3. 03Mean and variance of a binomial distribution
  4. 04The Poisson distribution
  5. 05How CUET asks this chapter
  6. 06Exam shortcuts
  7. 07Common mistakes
  8. 08Quick revision
  9. 09Check your understanding

Bernoulli trials

A help-line answers each incoming call within a minute 80% of the time, independently of other calls. Out of the next 5 calls, how likely is it that exactly 4 are answered in time? Situations like this, a fixed number of yes-or-no trials, are what the binomial distribution describes.

What are Bernoulli trials?

Bernoulli trials are a fixed number nn of trials, each with just two outcomes, success and failure, in which the trials are independent and the probability of success pp is the same every time. The failure probability is q=1−pq = 1 - p.

Watch out

Drawing without replacement breaks the rules: each draw changes what is left, so the trials are not independent. Throwing until a six appears breaks them too, because the number of trials is not fixed.

Binomial probabilities

What is the binomial formula?

If XX counts the successes in nn Bernoulli trials, then P(X=r)=nCr prqn−rP(X = r) = {}^{n}C_{r}\,p^{r}q^{n - r}, for r=0,1,…,nr = 0, 1, \dots, n. The nCr{}^{n}C_{r} counts the orders in which the rr successes can come.

Example 1 · The help-line

Each call is answered in time with p=0.8p = 0.8. Find the probability that exactly 4 of the next 5 are.

  1. P(X=4)=5C4 (0.8)4(0.2)1=5×0.4096×0.2P(X = 4) = {}^{5}C_{4}\,(0.8)^{4}(0.2)^{1} = 5 \times 0.4096 \times 0.2.

  2. =0.4096= 0.4096.

Answer

0.4096, about a 41% chance.

For "at least one", use the complement: P(X≥1)=1−qnP(X \ge 1) = 1 - q^{n}. For the help-line, 1−(0.2)5=0.999681 - (0.2)^{5} = 0.99968.

Mean and variance of a binomial distribution

What are the mean and variance of a binomial distribution?

The mean is npnp, the variance is npqnpq and the standard deviation is npq\sqrt{npq}. Since q<1q < 1, the variance is always less than the mean.

For the help-line, n=5n = 5 and p=0.8p = 0.8: the mean is 4 calls answered in time, the variance 5×0.8×0.2=0.85 \times 0.8 \times 0.2 = 0.8.

Key idea

Given the mean and variance, divide: npqnp=q\frac{npq}{np} = q. That gives pp, and then n=meanpn = \frac{\text{mean}}{p}.

The Poisson distribution

When is the Poisson distribution used?

It describes how many times a rare event happens in a fixed stretch of time or space, when events happen independently at a steady average rate λ\lambda: misprints on a page, calls in a minute, accidents on a road in a week. Its probabilities are P(X=r)=e−λλrr!P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!}, for r=0,1,2,…r = 0, 1, 2, \dots, and its mean and variance are both λ\lambda.

Example 2 · Customers at an ATM

An ATM averages 4 customers in a 10-minute spell, following a Poisson distribution. Find the probability of exactly 2 customers in a spell, taking e−4=0.0183e^{-4} = 0.0183.

  1. P(X=2)=e−4×422!=8×0.0183P(X = 2) = \frac{e^{-4} \times 4^{2}}{2!} = 8 \times 0.0183.

  2. =0.1464= 0.1464.

Answer

About 0.146.

The Poisson distribution also approximates a binomial with many trials and a small pp: take λ=np\lambda = np.

How CUET asks this chapter

  • Bernoulli trials: which situations qualify, and what nn and pp are.
  • Binomial: exactly, at least or at most rr successes; the formula for a given term.
  • Mean and variance: from nn and pp, or nn and pp from them; why a variance cannot exceed the mean.
  • Poisson: a probability with e−λe^{-\lambda} given, mean and variance, when it applies, the binomial approximation.

Exam shortcuts

  • Binomial: mean npnp, variance npqnpq, SD npq\sqrt{npq}.
  • q=variancemeanq = \frac{\text{variance}}{\text{mean}}.
  • "At least one" =1−qn= 1 - q^{n}.
  • Poisson: mean = variance = λ\lambda; P(0)=e−λP(0) = e^{-\lambda}, P(1)=λe−λP(1) = \lambda e^{-\lambda}.

Common mistakes

  • Leaving out nCr{}^{n}C_{r} and counting one order only.
  • Swapping pp and qq.
  • Giving the variance when the standard deviation is asked.
  • Forgetting the r!r! in the Poisson formula.

Quick revision

Binomial

  • P(r)=nCr prqn−rP(r) = {}^{n}C_{r}\,p^{r}q^{n - r}
  • Mean npnp, variance npqnpq

Poisson

  • P(r)=e−λλrr!P(r) = \frac{e^{-\lambda}\lambda^{r}}{r!}
  • Mean = variance = λ\lambda; λ=np\lambda = np for rare events

Next in Section B2: the most important continuous distribution, the normal distribution.

Next · 08Quick revision

Check your understanding

68 questions, grouped by type, easy to hard. Pick an answer and check it. The worked solution opens when you ask for it.

Bernoulli trialsQ1 of 68Q1

Bernoulli trials

7 questions

Which of these is a set of Bernoulli trials?

Options for question 1

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AnswerB · Tossing a coin 10 times and noting head or tail each time

  1. Bernoulli trials are a fixed number of independent trials, each with two outcomes and the same chance of success.

  2. The coin fits all of these. Drawing without replacement changes the chance each time; 'until a six' has no fixed number; a height is not a two-way outcome.

Worth noting

Drawing cards without replacement has two outcomes each time, but the trials are not independent.

Read the two statements and choose the correct option.

I. In Bernoulli trials the probability of success is the same in every trial.

II. Drawing balls one after another from a bag without putting them back gives Bernoulli trials.

Options for question 2

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AnswerA · Only I

  1. I is one of the conditions for Bernoulli trials. True.

  2. II: without replacement, each draw changes what is left in the bag, so the chance of success changes. False.

A student guesses every question of an 8-question test, each with 5 options of which one is right. A success is a correct answer. What are nn and pp?

Options for question 3

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AnswerC · n=8, p=15n = 8,\ p = \frac{1}{5}

  1. Each of the 8 questions is one trial: n=8n = 8.

  2. A guess is right with probability 15\frac{1}{5}: p=15p = \frac{1}{5}.

Worth noting

45\frac{4}{5} is qq, the chance of a wrong guess.

A pair of dice is thrown 10 times, and a throw counts as a success when it is a doublet (both dice the same). What are nn and pp?

Options for question 4

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AnswerB · n=10, p=16n = 10,\ p = \frac{1}{6}

  1. Each throw of the pair is one trial: n=10n = 10.

  2. A doublet comes in 6 of 36 outcomes: p=16p = \frac{1}{6}.

Worth noting

136\frac{1}{36} is the chance of one particular doublet, such as double six.

A die is thrown 6 times, and a throw counts as a success when it shows a number greater than 4. What are nn and pp?

Options for question 5

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AnswerD · n=6, p=13n = 6,\ p = \frac{1}{3}

  1. nn is the number of trials: 6 throws.

  2. Success is 5 or 6, two faces of six: p=26=13p = \frac{2}{6} = \frac{1}{3}.

Worth noting

p=23p = \frac{2}{3} is the chance of failure, 4 or less.

Which of these is NOT a set of Bernoulli trials?

Options for question 6

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AnswerD · Drawing 4 balls one by one without replacement from a bag of 6 red and 4 blue, and noting red or not

  1. Bernoulli trials need the same probability of success on every trial, independently.

  2. Without replacement, the chance of red changes after each draw (610\frac{6}{10}, then 59\frac{5}{9} or 69\frac{6}{9}), so those draws are not Bernoulli trials.

Worth noting

In a large town, picking 10 people barely changes the share of car owners, so those count as Bernoulli trials.

Which of the following are conditions for Bernoulli trials?

I. The number of trials is finite.

II. The trials are independent.

III. The probability of success changes from trial to trial.

IV. Each trial has exactly two outcomes, success or failure.

Options for question 7

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AnswerD · I, II and IV only

  1. Bernoulli trials are finite in number, independent, and each has exactly two outcomes.

  2. III is the opposite of a condition: the probability of success must stay the same, pp, on every trial.

Binomial probabilities

19 questions

A fair coin is tossed 4 times. What is the probability of exactly 2 heads?

Options for question 8

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AnswerC · 38\frac{3}{8}

  1. P(X=2)=4C2(12)2(12)2=6×116P(X = 2) = {}^{4}C_{2} \left(\frac{1}{2}\right)^{2} \left(\frac{1}{2}\right)^{2} = 6 \times \frac{1}{16}.

  2. =616=38= \frac{6}{16} = \frac{3}{8}.

Worth noting

116\frac{1}{16} is one particular order, such as HHTTHHTT; there are 4C2=6{}^{4}C_{2} = 6 orders.

A fair coin is tossed 6 times. What is the probability of exactly 3 heads?

Options for question 9

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AnswerC · 516\frac{5}{16}

  1. P(X=3)=6C3(12)6P(X = 3) = {}^{6}C_3\left(\frac{1}{2}\right)^{6}.

  2. =2064=516= \frac{20}{64} = \frac{5}{16}.

Worth noting

12\frac{1}{2} treats 'half heads' as the chance itself; only 20 of the 64 sequences have exactly 3.

A shooter hits a target with probability 34\frac{3}{4} on each shot. What is the probability of hitting it with all 4 shots?

Options for question 10

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AnswerA · 81256\frac{81}{256}

  1. P(X=4)=(34)4P(X = 4) = \left(\frac{3}{4}\right)^{4}.

  2. =81256= \frac{81}{256}.

Worth noting

34\frac{3}{4} is the chance of one hit, not four in a row.

A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.

What is the probability that the guessing student gets every quiz answer wrong?

Options for question 11

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AnswerB · 2431024\frac{243}{1024}

  1. Each answer is wrong with probability 34\frac{3}{4}, independently.

  2. (34)5=2431,024\left(\frac{3}{4}\right)^{5} = \frac{243}{1{,}024}.

Worth noting

11,024\frac{1}{1{,}024} is the chance of getting every answer right.

A die is thrown 3 times. What is the probability of at least one six?

Options for question 12

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AnswerB · 91216\frac{91}{216}

  1. 'At least one' is the complement of 'none'. The chance of no six is (56)3=125216\left(\frac{5}{6}\right)^{3} = \frac{125}{216}.

  2. So the chance of at least one six is 1−125216=912161 - \frac{125}{216} = \frac{91}{216}.

Worth noting

12\frac{1}{2} adds 16\frac{1}{6} three times, counting two or three sixes more than once.

10% of the bulbs a factory makes are faulty. In a random sample of 5 bulbs, what is the probability that none is faulty?

Options for question 13

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AnswerC · 0.590490.59049

  1. Each bulb is good with probability 0.9, independently.

  2. The chance that none is faulty is (0.9)5=0.59049(0.9)^{5} = 0.59049.

Worth noting

0.5 takes away 5×0.15 \times 0.1 from 1, which counts samples with two faulty bulbs twice.

XX follows a binomial distribution with n=7n = 7 and p=0.4p = 0.4. Which expression is P(X=3)P(X = 3)?

Options for question 14

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AnswerA · 7C3 (0.4)3(0.6)4{}^{7}C_{3}\,(0.4)^{3}(0.6)^{4}

  1. P(X=r)=nCr prqn−rP(X = r) = {}^{n}C_{r}\,p^{r}q^{n - r}, with q=1−p=0.6q = 1 - p = 0.6.

  2. For r=3r = 3: 7C3 (0.4)3(0.6)4{}^{7}C_{3}\,(0.4)^{3}(0.6)^{4}.

Worth noting

7C3 (0.6)3(0.4)4{}^{7}C_{3}\,(0.6)^{3}(0.4)^{4} gives the success power to the failures: it is P(X=4)P(X = 4).

A student guesses all 5 questions of a quiz, each with 4 options of which one is right. What is the probability that all 5 guesses are right?

Options for question 15

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AnswerA · 11024\frac{1}{1024}

  1. Each guess is right with probability 14\frac{1}{4}, independently.

  2. P(X=5)=(14)5=11,024P(X = 5) = \left(\frac{1}{4}\right)^{5} = \frac{1}{1{,}024}.

Worth noting

120\frac{1}{20} divides by 5×45 \times 4; independent chances multiply.

A die is thrown 4 times. What is the probability of exactly 2 sixes?

Options for question 16

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AnswerC · 25216\frac{25}{216}

  1. P(X=2)=4C2(16)2(56)2P(X = 2) = {}^{4}C_2\left(\frac{1}{6}\right)^{2}\left(\frac{5}{6}\right)^{2}.

  2. =6×251,296=25216= 6 \times \frac{25}{1{,}296} = \frac{25}{216}.

Worth noting

251,296\frac{25}{1{,}296} leaves out 4C2=6{}^{4}C_2 = 6, the number of ways to place the two sixes.

In nn Bernoulli trials with probability of success pp and q=1−pq = 1 - p, which expression is the probability of at least one success?

Options for question 17

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AnswerA · 1−qn1 - q^{n}

  1. The only way to have no success is to fail every time: qnq^{n}.

  2. So at least one success has probability 1−qn1 - q^{n}.

Worth noting

npnp is the mean, the expected number of successes, and can even exceed 1.

20% of a kind of seed fail to sprout. 3 seeds are planted. What is the probability that all 3 sprout?

Options for question 18

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AnswerC · 64125\frac{64}{125}

  1. Each seed sprouts with probability 0.8, independently.

  2. 0.83=0.5120.8^{3} = 0.512, which is 64125\frac{64}{125}.

Worth noting

45\frac{4}{5} is the chance for a single seed; all three must sprout.

A fair coin is tossed 5 times. What is the probability of at most one head?

Options for question 19

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AnswerD · 316\frac{3}{16}

  1. At most one head: P(0)+P(1)=132+532P(0) + P(1) = \frac{1}{32} + \frac{5}{32}.

  2. =632=316= \frac{6}{32} = \frac{3}{16}.

Worth noting

532\frac{5}{32} is exactly one head; 'at most one' includes no heads.

A fair coin is tossed 4 times. What is the probability of at least 2 heads?

Options for question 20

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AnswerB · 1116\frac{11}{16}

  1. Complement: 1−P(0)−P(1)=1−116−4161 - P(0) - P(1) = 1 - \frac{1}{16} - \frac{4}{16}.

  2. =1116= \frac{11}{16}.

Worth noting

38\frac{3}{8} is exactly 2 heads; 3 and 4 heads count too.

A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.

What is the probability that the guessing student gets exactly one quiz answer right?

Options for question 21

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AnswerD · 4051024\frac{405}{1024}

  1. P(X=1)=5C1(14)(34)4P(X = 1) = {}^{5}C_{1}\left(\frac{1}{4}\right)\left(\frac{3}{4}\right)^{4}.

  2. 5×14×81256=4051,0245 \times \frac{1}{4} \times \frac{81}{256} = \frac{405}{1{,}024}.

Worth noting

811,024\frac{81}{1{,}024} leaves out 5C1=5{}^{5}C_{1} = 5, the five places the right answer could be.

A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.

What is the probability that the guessing student gets at least one quiz answer right?

Options for question 22

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AnswerC · 7811024\frac{781}{1024}

  1. P(X≥1)=1−P(X=0)P(X \ge 1) = 1 - P(X = 0).

  2. 1−2431,024=7811,0241 - \frac{243}{1{,}024} = \frac{781}{1{,}024}.

Worth noting

1,0231,024\frac{1{,}023}{1{,}024} takes away the chance of all right, when it is the chance of all wrong that must go.

A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.

What is the variance of the number of quiz answers the guessing student gets right?

Options for question 23

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AnswerC · 1516\frac{15}{16}

  1. n=5n = 5, p=14p = \frac{1}{4}, q=34q = \frac{3}{4}.

  2. Variance =npq=5×14×34=1516= npq = 5 \times \frac{1}{4} \times \frac{3}{4} = \frac{15}{16}.

Worth noting

54\frac{5}{4} is the mean, npnp.

A die is thrown 4 times. What is the probability of at least one six?

Options for question 24

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AnswerB · 6711296\frac{671}{1296}

  1. 'At least one six' is the complement of 'no six': 1−(56)41 - \left(\frac{5}{6}\right)^{4}.

  2. =1−6251,296=6711,296= 1 - \frac{625}{1{,}296} = \frac{671}{1{,}296}.

Worth noting

46\frac{4}{6} adds 16\frac{1}{6} four times, which double-counts throws with more than one six.

A medicine cures 70% of patients. It is given to 3 patients. What is the probability that exactly 2 are cured?

Options for question 25

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AnswerD · 4411000\frac{441}{1000}

  1. P(X=2)=3C2(0.7)2(0.3)P(X = 2) = {}^{3}C_2(0.7)^{2}(0.3).

  2. =3×0.49×0.3=0.441= 3 \times 0.49 \times 0.3 = 0.441.

Worth noting

0.147 forgets the 3 ways to choose which two patients are cured.

A fair coin is tossed 5 times. What is the probability of at least 4 heads?

Options for question 26

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AnswerD · 316\frac{3}{16}

  1. At least 4 means 4 or 5: 5C4+5C5=5+1=6{}^{5}C_{4} + {}^{5}C_{5} = 5 + 1 = 6 of the 25=322^{5} = 32 outcomes.

  2. P=632=316P = \frac{6}{32} = \frac{3}{16}.

Worth noting

532\frac{5}{32} is exactly 4 heads; 'at least 4' takes in all 5 as well.

Mean and variance of a binomial distribution

18 questions

XX follows a binomial distribution with n=20n = 20 and p=0.3p = 0.3. What is its mean?

Options for question 27

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AnswerC · 66

  1. The mean of a binomial distribution is npnp.

  2. 20×0.3=620 \times 0.3 = 6.

Worth noting

4.2 is the variance npq=20×0.3×0.7npq = 20 \times 0.3 \times 0.7.

XX follows a binomial distribution with n=50n = 50 and p=0.4p = 0.4. What is its mean?

Options for question 28

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AnswerB · 2020

  1. Mean =np= np.

  2. 50×0.4=2050 \times 0.4 = 20.

Worth noting

12 is the variance, npqnpq.

A fair die is thrown 30 times. What is the expected number of sixes?

Options for question 29

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AnswerC · 55

  1. The number of sixes is binomial with n=30n = 30, p=16p = \frac{1}{6}.

  2. Mean =np=5= np = 5.

Worth noting

256\frac{25}{6} is the variance, npqnpq.

A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let XX be the number scored.

What is the mean number of free throws the player scores?

Options for question 30

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AnswerD · 2.42.4

  1. Mean =np= np.

  2. 4×0.6=2.44 \times 0.6 = 2.4.

Worth noting

1.6 is nqnq, the mean number missed.

A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let XX be the number scored.

What is the variance of the number of free throws the player scores?

Options for question 31

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AnswerA · 0.960.96

  1. Variance =npq=4×0.6×0.4= npq = 4 \times 0.6 \times 0.4.

  2. 0.960.96.

Worth noting

0.24 is pqpq without the factor n=4n = 4.

A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let XX be the number scored.

What is the probability that the player scores all 4 free throws?

Options for question 32

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AnswerC · 0.12960.1296

  1. P(X=4)=0.64P(X = 4) = 0.6^{4}.

  2. 0.12960.1296.

Worth noting

0.0256 is 0.440.4^{4}, the chance of missing all four.

A binomial distribution has mean 8 and variance 4.8. What are nn and pp?

Options for question 33

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AnswerB · n=20, p=0.4n = 20,\ p = 0.4

  1. npqnp=q=4.88=0.6\frac{npq}{np} = q = \frac{4.8}{8} = 0.6, so p=0.4p = 0.4.

  2. n=80.4=20n = \frac{8}{0.4} = 20.

Worth noting

p=0.6p = 0.6 takes qq for pp; then the mean would be 12.

XX follows a binomial distribution with n=100n = 100 and p=0.2p = 0.2. What is its standard deviation?

Options for question 34

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AnswerB · 44

  1. Variance =npq=100×0.2×0.8=16= npq = 100 \times 0.2 \times 0.8 = 16.

  2. Standard deviation =16=4= \sqrt{16} = 4.

Worth noting

16 is the variance; the standard deviation is its square root.

Assertion (A): No binomial distribution has mean 5 and variance 6.

Reason (R): The variance npqnpq is less than the mean npnp, because q<1q < 1.

Options for question 35

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AnswerA · Both A and R are true, and R is the correct explanation of A.

  1. Variance 6 with mean 5 would need q=65>1q = \frac{6}{5} > 1, which no probability can be. A is true.

  2. R is exactly that reason, so R explains A.

Match each quantity in List I with its value in List II.

List IList II
A. Mean of a binomial distribution with n=10n = 10, p=0.5p = 0.5I. 44
B. Variance of a binomial distribution with n=10n = 10, p=0.5p = 0.5II. 55
C. Variance of a Poisson distribution with λ=4\lambda = 4III. 33
D. Standard deviation of a Poisson distribution with λ=9\lambda = 9IV. 2.52.5
Options for question 36

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AnswerD · A-II, B-IV, C-I, D-III

  1. Binomial: mean np=5np = 5, variance npq=10×0.5×0.5=2.5npq = 10 \times 0.5 \times 0.5 = 2.5.

  2. Poisson: the variance equals λ=4\lambda = 4; with λ=9\lambda = 9 the standard deviation is 9=3\sqrt{9} = 3.

XX follows a binomial distribution with n=60n = 60 and p=13p = \frac{1}{3}. What is its variance?

Options for question 37

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AnswerA · 403\frac{40}{3}

  1. Variance =npq=60×13×23= npq = 60 \times \frac{1}{3} \times \frac{2}{3}.

  2. =403= \frac{40}{3}.

Worth noting

20 is the mean, npnp.

For a binomial distribution with n=16n = 16, what is the largest possible variance?

Options for question 38

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AnswerB · 44

  1. npq=16p(1−p)npq = 16p(1 - p), and p(1−p)p(1 - p) is largest at p=12p = \frac{1}{2}, where it is 14\frac{1}{4}.

  2. Largest variance =16×14=4= 16 \times \frac{1}{4} = 4.

Worth noting

8 is the mean when p=12p = \frac{1}{2}; the variance is half of it.

The mean of a binomial distribution is three times its variance. What is pp?

Options for question 39

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AnswerC · 23\frac{2}{3}

  1. np=3npqnp = 3npq, so q=13q = \frac{1}{3}.

  2. p=1−13=23p = 1 - \frac{1}{3} = \frac{2}{3}.

Worth noting

13\frac{1}{3} is qq, not pp.

A binomial distribution has mean 6 and variance 4.2. What are nn and pp?

Options for question 40

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AnswerD · n=20, p=0.3n = 20,\ p = 0.3

  1. q=npqnp=4.26=0.7q = \frac{npq}{np} = \frac{4.2}{6} = 0.7, so p=0.3p = 0.3.

  2. n=60.3=20n = \frac{6}{0.3} = 20.

Worth noting

0.7 is qq, the ratio of variance to mean, not pp.

XX follows a binomial distribution with n=400n = 400 and p=0.1p = 0.1. What is its standard deviation?

Options for question 41

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AnswerA · 66

  1. Variance =npq=400×0.1×0.9=36= npq = 400 \times 0.1 \times 0.9 = 36.

  2. Standard deviation =36=6= \sqrt{36} = 6.

Worth noting

36 is the variance; 210=402\sqrt{10} = \sqrt{40} roots the mean instead.

Assertion (A): A binomial distribution with mean 4 and variance 2 has n=8n = 8.

Reason (R): The ratio of the variance to the mean is qq.

Options for question 42

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AnswerA · Both A and R are true, and R is the correct explanation of A.

  1. R: npqnp=q\frac{npq}{np} = q. True.

  2. By R, q=24=12q = \frac{2}{4} = \frac{1}{2}, so p=12p = \frac{1}{2} and n=41/2=8n = \frac{4}{1/2} = 8: A is true and follows from R.

A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let XX be the number scored.

What is the probability that the player scores exactly 3 of the 4 free throws?

Options for question 43

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AnswerD · 0.34560.3456

  1. P(X=3)=4C3(0.6)3(0.4)P(X = 3) = {}^{4}C_{3}(0.6)^{3}(0.4).

  2. 4×0.216×0.4=0.34564 \times 0.216 \times 0.4 = 0.3456.

Worth noting

0.0864 leaves out 4C3=4{}^{4}C_{3} = 4, the four places the miss could be.

A binomial distribution has mean 4 and variance 3. What is P(X=0)P(X = 0)?

Options for question 44

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AnswerD · (34)16\left(\frac{3}{4}\right)^{16}

  1. q=34q = \frac{3}{4}, so p=14p = \frac{1}{4} and n=41/4=16n = \frac{4}{1/4} = 16.

  2. P(X=0)=qn=(34)16P(X = 0) = q^{n} = \left(\frac{3}{4}\right)^{16}.

Worth noting

(14)16\left(\frac{1}{4}\right)^{16} is P(X=16)P(X = 16), every trial a success.

The Poisson distribution

24 questions

A Poisson distribution has mean 3. What is its variance?

Options for question 45

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AnswerC · 33

  1. In a Poisson distribution the mean and the variance are both λ\lambda.

  2. So the variance is 3 as well.

Worth noting

9 squares the mean; for a Poisson variable the variance is the mean itself.

A Poisson distribution has mean 4. What is its standard deviation?

Options for question 46

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AnswerB · 22

  1. For a Poisson distribution the variance equals the mean: 4.

  2. Standard deviation =4=2= \sqrt{4} = 2.

Worth noting

4 is the variance; the standard deviation is its square root.

A shop gets an average of 3 complaints a day, following a Poisson distribution. What is the probability of no complaint on a given day? (Take e−3=0.0498e^{-3} = 0.0498.)

Options for question 47

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AnswerA · 0.04980.0498

  1. P(X=r)=e−λλrr!P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} with λ=3\lambda = 3, r=0r = 0.

  2. =0.0498×300!=0.0498= \frac{0.0498 \times 3^{0}}{0!} = 0.0498.

Worth noting

0.9502 is the probability of at least one complaint.

Accidents at a crossing follow a Poisson distribution with an average of 0.5 a day. What is the expected number of accidents in 30 days?

Options for question 48

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AnswerC · 1515

  1. The mean adds over days: 0.50.5 a day for 30 days.

  2. 0.5×30=150.5 \times 30 = 15.

Worth noting

60 divides by 0.5 instead of multiplying.

Read the two statements and choose the correct option.

I. The Poisson distribution suits a large number of trials, each with a small chance of success.

II. A Poisson random variable can take negative values.

Options for question 49

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AnswerA · Only I

  1. I: it is the limit of the binomial when nn is large and pp small, with np=λnp = \lambda. True.

  2. II: it counts events, 0,1,2,…0, 1, 2, \dots, so it is never negative. False.

A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e−0.5=0.6065e^{-0.5} = 0.6065.)

What is the mean number of defective bulbs in a box?

Options for question 50

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AnswerB · 0.50.5

  1. λ=np=10×0.05\lambda = np = 10 \times 0.05.

  2. λ=0.5\lambda = 0.5.

Worth noting

0.05 is the chance for one bulb; a box holds 10.

A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e−3=0.0498e^{-3} = 0.0498.)

What is the mean number of calls the helpline gets in a 10-minute spell?

Options for question 51

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AnswerB · 66

  1. Calls come at 3 per 5 minutes.

  2. In 10 minutes, λ=2×3=6\lambda = 2 \times 3 = 6.

Worth noting

9 squares the mean, which is the variance rule mixed up with the time.

A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e−0.5=0.6065e^{-0.5} = 0.6065.)

What is the probability that a box of bulbs has no defective bulb?

Options for question 52

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AnswerD · 0.60650.6065

  1. P(X=0)=e−λ=e−0.5P(X = 0) = e^{-\lambda} = e^{-0.5}.

  2. 0.60650.6065.

Worth noting

0.3935 is the chance of at least one defective bulb.

A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e−3=0.0498e^{-3} = 0.0498.)

What is the probability that the helpline gets no call in a 5-minute spell?

Options for question 53

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AnswerA · 0.04980.0498

  1. λ=3\lambda = 3 for a 5-minute spell.

  2. P(X=0)=e−3=0.0498P(X = 0) = e^{-3} = 0.0498.

Worth noting

0.9502 is the chance of at least one call.

Calls reach a help desk at an average of 2 a minute, following a Poisson distribution. What is the probability of no call in a given minute? (Take e−2=0.1353e^{-2} = 0.1353.)

Options for question 54

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AnswerA · 0.13530.1353

  1. P(X=0)=e−λλ00!=e−λP(X = 0) = \frac{e^{-\lambda}\lambda^{0}}{0!} = e^{-\lambda}.

  2. With λ=2\lambda = 2: e−2=0.1353e^{-2} = 0.1353.

Worth noting

0.8647 is the probability of at least one call.

XX follows a Poisson distribution with λ=2\lambda = 2. What is P(X=2)P(X = 2)? (Take e−2=0.1353e^{-2} = 0.1353.)

Options for question 55

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AnswerB · 0.27060.2706

  1. P(X=2)=e−2×222!=42×0.1353P(X = 2) = \frac{e^{-2} \times 2^{2}}{2!} = \frac{4}{2} \times 0.1353.

  2. =2×0.1353=0.2706= 2 \times 0.1353 = 0.2706.

Worth noting

0.5412 forgets to divide by 2!=22! = 2.

Which of these is best described by a Poisson distribution?

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AnswerD · The number of typing errors on a page of a long book

  1. A Poisson variable counts rare events that happen independently in a fixed stretch of time or space, with no fixed upper limit.

  2. Typing errors on a page fit this. Ten tosses are binomial, heights are continuous, and draws without replacement are not independent.

Worth noting

Heads in 10 tosses is a count too, but of a fixed number of trials with a large chance each: that is binomial.

Which of the following statements are true?

I. The mean and the variance of a Poisson distribution are equal.

II. A Poisson distribution needs two numbers, nn and pp, to describe it.

III. A Poisson variable can take any of the values 0, 1, 2, 3, …

IV. It suits rare events among a large number of chances.

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AnswerB · I, III and IV only

  1. Mean = variance = λ\lambda (I), the values run 0, 1, 2, … with no upper limit (III), and it models rare events (IV).

  2. II is false: a Poisson distribution has one parameter, λ\lambda; nn and pp belong to the binomial.

XX follows a Poisson distribution with λ=1.5\lambda = 1.5. What is P(X≤1)P(X \le 1)? (Take e−1.5=0.2231e^{-1.5} = 0.2231.)

Options for question 58

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AnswerD · 0.557750.55775

  1. P(X≤1)=P(0)+P(1)=e−1.5(1+1.5)P(X \le 1) = P(0) + P(1) = e^{-1.5}(1 + 1.5).

  2. =0.2231×2.5=0.55775= 0.2231 \times 2.5 = 0.55775.

Worth noting

0.33465 is P(X=1)P(X = 1) alone.

XX follows a Poisson distribution with λ=2\lambda = 2. What is P(X=1)P(X = 1)? (Take e−2=0.1353e^{-2} = 0.1353.)

Options for question 59

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AnswerC · 0.27060.2706

  1. P(X=r)=e−λλrr!P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} with λ=2\lambda = 2, r=1r = 1.

  2. =0.1353×211!=0.2706= \frac{0.1353 \times 2^{1}}{1!} = 0.2706.

Worth noting

0.1353 is P(X=0)P(X = 0); P(X=1)P(X = 1) carries the factor λ=2\lambda = 2.

XX follows a Poisson distribution with λ=1\lambda = 1. What is P(X=3)P(X = 3)? (Take e−1=0.3679e^{-1} = 0.3679.)

Options for question 60

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AnswerA · 367960000\frac{3679}{60000}

  1. P(X=r)=e−λλrr!P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} with λ=1\lambda = 1, r=3r = 3.

  2. =0.3679×133!=367960000= \frac{0.3679 \times 1^{3}}{3!} = \frac{3679}{60000}.

Worth noting

0.1226 divides by 3 instead of 3!=63! = 6.

2% of the pens a machine makes are faulty. Using the Poisson distribution, what is the probability that a pack of 100 pens has no faulty pen? (Take e−2=0.1353e^{-2} = 0.1353.)

Options for question 61

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AnswerB · 0.13530.1353

  1. λ=np=100×0.02=2\lambda = np = 100 \times 0.02 = 2.

  2. P(X=0)=e−2=0.1353P(X = 0) = e^{-2} = 0.1353.

Worth noting

0.98 is the chance that one pen is good, not all 100.

A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e−0.5=0.6065e^{-0.5} = 0.6065.)

What is the probability that a box of bulbs has exactly one defective bulb?

Options for question 62

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AnswerA · 0.303250.30325

  1. P(X=1)=e−0.5(0.5)11!P(X = 1) = \frac{e^{-0.5}(0.5)^{1}}{1!}.

  2. 0.5×0.6065=0.303250.5 \times 0.6065 = 0.30325.

Worth noting

0.151625 halves again, as if 1!1! were 2.

A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e−3=0.0498e^{-3} = 0.0498.)

What is the probability that the helpline gets at most one call in a 5-minute spell?

Options for question 63

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AnswerD · 0.19920.1992

  1. P(X≤1)=e−3+3e−3=4e−3P(X \le 1) = e^{-3} + 3e^{-3} = 4e^{-3}.

  2. 4×0.0498=0.19924 \times 0.0498 = 0.1992.

Worth noting

0.1494 is exactly one call; 'at most one' takes in no call as well.

A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e−3=0.0498e^{-3} = 0.0498.)

What is the probability that the helpline gets exactly 2 calls in a 5-minute spell?

Options for question 64

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AnswerC · 0.22410.2241

  1. P(X=2)=e−3×322!P(X = 2) = \frac{e^{-3} \times 3^{2}}{2!}.

  2. 4.5×0.0498=0.22414.5 \times 0.0498 = 0.2241.

Worth noting

0.4482 forgets to divide by 2!=22! = 2.

XX follows a Poisson distribution with λ=1\lambda = 1. What is P(X≥2)P(X \ge 2)? (Take e−1=0.3679e^{-1} = 0.3679.)

Options for question 65

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AnswerC · 0.26420.2642

  1. P(X≥2)=1−P(X=0)−P(X=1)P(X \ge 2) = 1 - P(X = 0) - P(X = 1), and with λ=1\lambda = 1 both are e−1=0.3679e^{-1} = 0.3679.

  2. =1−0.7358=0.2642= 1 - 0.7358 = 0.2642.

Worth noting

0.6321 takes away only P(X=0)P(X = 0); that is P(X≥1)P(X \ge 1).

1% of the screws a machine makes are faulty. Using the Poisson distribution, what is the probability that a box of 300 screws has no faulty screw? (Take e−3=0.0498e^{-3} = 0.0498.)

Options for question 66

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AnswerA · 0.04980.0498

  1. For many trials with a small chance, use Poisson with λ=np=300×0.01=3\lambda = np = 300 \times 0.01 = 3.

  2. P(X=0)=e−3=0.0498P(X = 0) = e^{-3} = 0.0498.

Worth noting

0.99 is the chance that one screw is good, not all 300.

For a Poisson variable XX, P(X=1)=P(X=2)P(X = 1) = P(X = 2). What is its mean?

Options for question 67

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AnswerD · 22

  1. e−λλ=e−λλ22e^{-\lambda}\lambda = \frac{e^{-\lambda}\lambda^{2}}{2}.

  2. Dividing by e−λλe^{-\lambda}\lambda: 1=λ21 = \frac{\lambda}{2}, so λ=2\lambda = 2, which is the mean.

Worth noting

1 forgets the 2!2! under λ2\lambda^{2}.

A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e−0.5=0.6065e^{-0.5} = 0.6065.)

What is the probability that a box of bulbs has two or more defective bulbs?

Options for question 68

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AnswerB · 0.090250.09025

  1. P(X≥2)=1−P(0)−P(1)P(X \ge 2) = 1 - P(0) - P(1).

  2. 1−0.6065−0.30325=0.090251 - 0.6065 - 0.30325 = 0.09025.

Worth noting

0.0758125 is exactly two; 'two or more' takes in every larger count as well.

NTA's Mathematics / Applied Mathematics syllabus has three sections: Section A1, which every candidate answers, then Section B1 (Mathematics) or Section B2 (Applied Mathematics), of which a candidate answers one. Each section lists units and topics but no chapters, so the chapters here are ours. Where both B sections list a topic that Section A also lists, its questions sit once, in Section A.