Problems

Filters
Clear Filters

68 problems found

2013 Paper 3 Q12
D: 1700.0 B: 1500.0

A list consists only of letters \(A\) and \(B\) arranged in a row. In the list, there are \(a\) letter \(A\)s and \(b\) letter \(B\)s, where \(a\ge2\) and \(b\ge2\), and \(a+b=n\). Each possible ordering of the letters is equally probable. The random variable \(X_1\) is defined by \[ X_1 = \begin{cases} 1 & \text{if the first letter in the row is \(A\)};\\ 0 & \text{otherwise.} \end{cases} \] The random variables \(X_k\) (\(2 \le k \le n\)) are defined by \[ X_k = \begin{cases} 1 & \text{if the \((k-1)\)th letter is \(B\) and the \(k\)th is \(A\)};\\ 0 & \text{otherwise.} \end{cases} \] The random variable \(S\) is defined by \(S = \sum\limits_ {i=1}^n X_i\,\).

  1. Find expressions for \(\E(X_i)\), distinguishing between the cases \(i=1\) and \(i\ne1\), and show that \(\E(S)= \dfrac{a(b+1)}n\,\).
  2. Show that:
    1. for \(j\ge3\), \(\E(X_1X_j) = \dfrac{a(a-1)b}{n(n-1)(n-2)}\,\);
    2. \[ \sum\limits_{i=2}^{n-2} \bigg( \sum\limits_{j=i+2}^n \E(X_iX_j)\bigg) = \dfrac{a(a-1)b(b-1)}{2n(n-1)}\,\]
    3. \(\var(S) = \dfrac {a(a-1)b(b+1)}{n^2(n-1)}\,\).


Solution:

  1. Notice that \(\E[X_1] = \frac{a}{n}\) and consider \(\E[X_i]\) with \(i > 1\). the probability that this is \(1\) is \(\frac{b}{n} \cdot \frac{a}{n-1}\). So \begin{align*} && \E[S] &= \E[X_1] + \sum_{i=2}^n \E[X_i] \\ &&&= \frac{a}{n} + (n-1) \frac{ab}{n(n-1)} \\ &&&= \frac{a(b+1)}{n} \end{align*}
    1. The probability \(X_1X_j = 1\) is \(\frac{a}{n} \cdot \frac{b}{n-1} \cdot \frac{a-1}{n-2} = \frac{a(a-1)b}{n(n-1)(n-2)}\) since there is nothing special about the order, and the first is an \(A\) with probability \(\frac{a}{n}\) and given this occurs there are now \(a-1\) \(A\) and \(n-1\) letters left etc... Therefore \(\E[X_1X_j] = \frac{a(a-1)b}{n(n-1)(n-2)}\)
    2. \(\E[X_iX_j]\) when the pairs don't overlap is \(\frac{a}{n} \frac{b}{n-1} \frac{a-1}{n-2} \frac{b-1}{n-3}\), and so \begin{align*} && \sum\limits_{i=2}^{n-2} \bigg( \sum\limits_{j=i+2}^n \E(X_iX_j)\bigg) &= \sum\limits_{i=2}^{n-2} \bigg( \sum\limits_{j=i+2}^n \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)}\bigg) \\ &&&= \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)}\sum\limits_{i=2}^{n-2} \bigg( \sum\limits_{j=i+2}^n 1\bigg) \\ &&&= \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)}\sum\limits_{i=2}^{n-2} (n-(i+1)) \\ &&&= \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)} \left ((n-1)(n-3)-\frac{(n-2)(n-1)}{2}+1 \right) \\ &&&= \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)} \left ( \frac{2n^2-8n-6-n^2+3n-2+2}{2}\right) \\ &&&= \frac{a(a-1)b(b-1)}{n(n-1)(n-2)(n-3)} \left ( \frac{n^2-5n-6}{2}\right) \\ &&&= \frac{a(a-1)b(b-1)}{2n(n-1)} \end{align*}
    3. We also need to consider the other cross terms. \(X_iX_{i+1}=0\). (Since \(X_i = 1\) means the \(i\)th letter is \(A\) and \(X_{i+1} = 1\) means the \(i\)th letter is \(B\)). It's the same story for \(X_1X_2\), and so all the cross terms are accounted for. Therefore \begin{align*} && \E[S^2] &= \E \left [\sum X_i^2 + 2\sum_{i \neq j} X_i X_j \right] \\ &&&= \frac{a(b+1)}{n} +2(n-2)\frac{a(a-1)b}{n(n-1)(n-2)}+ 2 \frac{a(a-1)b(b-1)}{2n(n-1)} \\ &&&= \frac{a(b+1)}{n} +\frac{2a(a-1)b}{n(n-1)} + \frac{a(a-1)b(b-1)}{n(n-1)} \\ &&&= \frac{a(b+1)}{n} +\frac{a(a-1)b(b+1)}{n(n-1)} \\ && \var[S] &= \E[S^2] - \left ( \E[S] \right)^2 \\ &&&= \frac{a(b+1)}{n} + \frac{a(a-1)b(b+1)}{n(n-1)} - \frac{a^2(b+1)^2}{n^2} \\ &&&= \frac{a(b+1) \left (n(n-1) + (a-1)b n -a(b+1)(n-1) \right)}{n^2(n-1)} \\ &&&= \frac{a(b+1) \left ( (n-a)(n-b-1) \right)}{n^2(n-1)} \\ &&&= \frac{a(b+1) \left ( b(a-1) \right)}{n^2(n-1)} \\ \end{align*}

2012 Paper 1 Q13
D: 1500.0 B: 1529.2

I choose at random an integer in the range 10000 to 99999, all choices being equally likely. Given that my choice does not contain the digits 0, 6, 7, 8 or 9, show that the expected number of different digits in my choice is 3.3616.


Solution: We are choosing any \(5\) digit number from \(\{1,2,3,4,5\}\). There are \(5^5\) such numbers. \begin{align*} && \mathbb{E}(\text{different digits}) &= \frac1{5^5} \left (1 \cdot 5 + 2 \cdot \binom{5}{2}(2^5-2)+3 \cdot \binom{5}{3}(3^5-3 \cdot 2^5+3)+4 \cdot \binom{5}{4}(4^5 - 4 \cdot 3^5+6 \cdot 2^5-4) + 5 \cdot 5! \right) \\ &&&= \frac{2101}{625} = 3.3616 \end{align*}

2012 Paper 2 Q1
D: 1600.0 B: 1500.0

Write down the general term in the expansion in powers of \(x\) of \((1-x^6)^{-2}\,\).

  1. Find the coefficient of \(x^{24}\) in the expansion in powers of \(x\) of \[ (1-x^6)^{-2} (1-x^3)^{-1}\,.\] Obtain also, and simplify, formulae for the coefficient of \(x^n\) in the different cases that arise.
  2. Show that the coefficient of \(x^{24}\) in the expansion in powers of \(x\) of \[ (1-x^6)^{-2} (1-x^3)^{-1} (1-x)^{-1}\,\] is \(55\), and find the coefficients of \(x^{25}\) and \(x^{66}\).


Solution: \(\displaystyle (1-x^6)^{-2} = \sum_{n=0}^{\infty} (n+1)x^{6n}\)

  1. \(\,\) \begin{align*} && f(x) &= (1-x^6)^{-2}(1-x^3)^{-1} \\ &&&= \left ( \sum_{n=0}^{\infty} (n+1)x^{6n} \right) \left ( \sum_{n=0}^{\infty} x^{3n} \right) \\ [x^{24}]: && c_{24} &= 1 + 2+ 3+4+5 = 15 \end{align*} Clearly \(n\) must be a multiple of \(3\). If \(n = 6k\) then we have \(1 + 2 + \cdots + (k+1) = \frac{(k+1)(k+2)}{2}\) If \(n = 6k+3\) then we have \(1 + 2 + \cdots + (k+1) = \frac{(k+1)(k+2)}{2}\) the same way, we just must always get one extra \(x^3\) term from the second expansion.
  2. We can obtain \(x^{24}\) from the product of \((1-x^6)^{-2}(1-x^3)^{-1}\) and \((1-x)^{-1}\) in the following ways: \begin{array}{c|c|c} (1-x^6)^{-2}(1-x^3)^{-1} & (1-x)^{-1} & \text{product} \\ \hline 15x^{24} & x^0 & 15x^{24} \\ 10x^{21} & x^3 & 10x^{24} \\ 10x^{18} & x^6 & 10x^{24} \\ 6x^{15} & x^9 & 6x^{24} \\ 6x^{12} & x^{12} & 6x^{24} \\ 3x^{9} & x^{15} & 3x^{24} \\ 3x^{6} & x^{18} & 3x^{24} \\ x^{3} & x^{21} & x^{24} \\ x^{0} & x^{24}& x^{24} \end{array} So the total is \(55\). Similarly for \(25\) we can only obtain this in the same ways but also taking an extra power of \(x\) from the geometric series, ie \(55\) For \(66\) we obtain by similar reasoning that it is: \(\frac{13\cdot12}{2} + 2 \left (1 + 3 + \cdots + \frac{13 \cdot 12}{2} \right) = \frac{13\cdot12}{2} + 2 \binom{14}{3} = \frac{13 \cdot 12}2 ( 1 + \frac{30}{3}) = 11 \cdot 6 \cdot 13 = 858\)

2011 Paper 1 Q12
D: 1500.0 B: 1470.2

I am selling raffle tickets for \(\pounds1\) per ticket. In the queue for tickets, there are \(m\) people each with a single \(\pounds1\) coin and \(n\) people each with a single \(\pounds2\) coin. Each person in the queue wants to buy a single raffle ticket and each arrangement of people in the queue is equally likely to occur. Initially, I have no coins and a large supply of tickets. I stop selling tickets if I cannot give the required change.

  1. In the case \(n=1\) and \(m\ge1\), find the probability that I am able to sell one ticket to each person in the queue.
  2. By considering the first three people in the queue, show that the probability that I am able to sell one ticket to each person in the queue in the case \(n=2\) and \(m\ge2\) is \(\dfrac{m-1}{m+1}\,\).
  3. Show that the probability that I am able to sell one ticket to each person in the queue in the case \(n=3\) and \(m\ge3\) is \(\dfrac{m-2}{m+1}\,\).


Solution:

  1. The only way you wont be able to sell to them is if they are first, ie \(\frac1{m+1}\)
  2. If \(n=2\), the the only way you fail to sell to them is if one comes first or they both appear before two people with pound coins, ie \(2\) or \(122\). These have probabilities \(\frac{2}{m+2}\) and \(\frac{m}{m+2} \cdot \frac{2}{m+1} \frac{1}{m} = \frac{2}{(m+1)(m+2)}\). Therefore the total probability you don't sell all the tickets is \(\frac{2}{m+2}\left ( 1 + \frac{1}{m+1} \right) = \frac{2}{m+2} \frac{m+2}{m+1} = \frac{2}{m+1}\). Therefore the probability you do sell all the tickets is \(1 - \frac{2}{m+1} = \frac{m-1}{m+1}\)
  3. The only ways to fail when \(n=3\) are: \(2\), \(122\), or if all three \(2\)s appear before three \(1\)s. this can happen in \(11222\), \(12122\) These happen with probability: \begin{align*} 2: && \frac{3}{m+3} \\ 122: && \frac{m}{m+3} \cdot \frac{3}{m+2} \cdot \frac{2}{m+1} \\ 11222: && \frac{m(m-1) 6}{(m+3)(m+2)(m+1)m(m-1)} \\ 12122: && \frac{m(m-1) 6}{(m+3)(m+2)(m+1)m(m-1)} \\ \end{align*} Therefore the total probability is: \begin{align*} P &= \frac{1}{(m+3)(m+2)(m+1)} \left (3(m+2)(m+1)+6m + 12 \right) \\ &= \frac{1}{(m+3)(m+2)(m+1)} \left (3(m+1)(m+2) \right) \\ &= \frac{3}{m+1} \end{align*} and the result follows

2010 Paper 2 Q13
D: 1600.0 B: 1502.2

Rosalind wants to join the Stepney Chess Club. In order to be accepted, she must play a challenge match consisting of several games against Pardeep (the Club champion) and Quentin (the Club secretary), in which she must win at least one game against each of Pardeep and Quentin. From past experience, she knows that the probability of her winning a single game against Pardeep is \(p\) and the probability of her winning a single game against Quentin is \(q\), where \(0 < p < q < 1\).

  1. The challenge match consists of three games. Before the match begins, Rosalind must choose either to play Pardeep twice and Quentin once or to play Quentin twice and Pardeep once. Show that she should choose to play Pardeep twice.
  2. In order to ease the entry requirements, it is decided instead that the challenge match will consist of four games. Now, before the match begins, Rosalind must choose whether to play Pardeep three times and Quentin once (strategy 1), or to play Pardeep twice and Quentin twice (strategy 2) or to play Pardeep once and Quentin three times (strategy 3). Show that, if \(q-p > \frac 12\), Rosalind should choose strategy 1. If \(q-p<\frac12\), give examples of values of \(p\) and \(q\) to show that strategy 2 can be better or worse than strategy 1.


Solution:

  1. If she plays \(P\) twice her probability is \(q(p^2+2p(1-p)) = qp(2-p)\). If she plays \(Q\) twice her probability is \(pq(2-q)\). Since \(p < q\) she should play \(P\) twice.
  2. Under strategy 1, her probability is \(q(p^3+3p^2(1-p)+3p(1-p)^2) = qp(p^2+3p-3p^2+3-6p+3p^2) = qp(3-3p+p^2)\) Under strategy 2 her probability is \((p^2+2p(1-p))(q^2+2q(1-q)) = pq(2-p)(2-q)\). Under strategy 3 her probability is \(qp(3-3q+q^2)\) \begin{align*} && q - p &> \frac12 \\ \Rightarrow && (2-p)(2-q) & < (2-p)(\frac32 - p) \\ &&&= 3 - \frac72p + p^2 \\ &&&< 3- 3p + p^2 \end{align*} Therefore Strategy 1 dominates if \(q-p > \frac12\). If \(p = \frac14, q = \frac12\) then \((2-p)(2-q) =\frac74 \cdot \frac32 = \frac{21}{8}\) and \(3-3p + p^2 = 3 - \frac34 + \frac1{16} = \frac{48-12+1}{16} = \frac{37}{16} < \frac{42}{16}\) so strategy 2 dominates. Notice that strategy 1 always dominates strategy 3 since \(f(x) = 3-3x+x^2\) is decreasing for \(x < 1.5\). If \(p = \frac14, q = \frac12\) then \((2-p)(2-q) =\frac74 \cdot \frac32 = \frac{21}{8}\) and \(3-3p + p^2 = 3 - \frac34 + \frac1{16} = \frac{48-12+1}{16} = \frac{37}{16} < \frac{42}{16}\) so strategy 2 dominates. For strategy 1 to dominate, we need \(3-3p+p^2 > (2-q)(2-p)\) or \(\frac{3-3p+p^2}{2-p} > 2-q\). When \(p = \frac12\) this is \(\frac{3-\frac32 + \frac14}{2 - \frac12} = \frac{\frac{7}{4}}{\frac{3}{2}} = \frac76 = 2-\frac{5}{6}\) so take any value of \(q\) larger than \(\frac56\).

2009 Paper 1 Q12
D: 1500.0 B: 1501.5

Prove that, for any real numbers \(x\) and \(y\), \(x^2+y^2\ge2xy\,\).

  1. Carol has two bags of sweets. The first bag contains \(a\) red sweets and \(b\) blue sweets, whereas the second bag contains \(b\) red sweets and \(a\) blue sweets, where \(a\) and \(b\) are positive integers. Carol shakes the bags and picks one sweet from each bag without looking. Prove that the probability that the sweets are of the same colour cannot exceed the probability that they are of different colours.
  2. Simon has three bags of sweets. The first bag contains \(a\) red sweets, \(b\) white sweets and \(c\) yellow sweets, where \(a\), \(b\) and \(c\) are positive integers. The second bag contains \(b\) red sweets, \(c\) white sweets and \(a\) yellow sweets. The third bag contains \(c\) red sweets, \(a\) white sweets and \(b\) yellow sweets. Simon shakes the bags and picks one sweet from each bag without looking. Show that the probability that exactly two of the sweets are of the same colour is \[ \frac {3(a^2b+b^2c+c^2a+ab^2 + bc^2 +ca^2)}{(a+b+c)^3}\,, \] and find the probability that the sweets are all of the same colour. Deduce that the probability that exactly two of the sweets are of the same colour is at least 6 times the probability that the sweets are all of the same colour.

2009 Paper 1 Q13
D: 1500.0 B: 1504.1

I seat \(n\) boys and \(3\) girls in a line at random, so that each order of the \(n+3\) children is as likely to occur as any other. Let \(K\) be the maximum number of consecutive girls in the line so, for example, \(K=1\) if there is at least one boy between each pair of girls.

  1. Find \(\P(K=3)\).
  2. Show that \[\P(K=1)= \frac{n(n-1)}{(n+2)(n+3)}\,. \]
  3. Find \(\E(K)\).


Solution:

  1. If all the girls are say together there are \(n+1\) ways to place the block of 3 girls. There are \(\binom{n+3}{3}\) ways to choose where to place the girls in total, therefore: \begin{align*} && \mathbb{P}(K =3) &= \frac{n+1}{\binom{n+3}3} \\ &&&= \frac{6(n+1)}{(n+3)(n+2)(n+1)} \\ &&&= \frac{6}{(n+3)(n+2)} \end{align*}
  2. If \(K= 1\) then all of the girls are separated. We can place three girls and two boys separating them, then we are allocating \(N-2\) boys to \(4\) gaps, ie \(\binom{N-2+3}{3} = \binom{N+1}{3}\). \begin{align*} && \mathbb{P}(K=3) &= \frac{\binom{n+1}{3}}{\binom{n+3}{3}} \\ &&&= \frac{(n+1)n(n-1)}{(n+3)(n+2)(n+1)} \\ &&&= \frac{n(n-1)}{(n+3)(n+2)} \end{align*}
  3. \(\,\) \begin{align*} \mathbb{E}(K) &= \sum_{k=1}^3 k \mathbb{P}(K=k) \\ &= \frac{6}{(n+3)(n+2)} + 2 \left (1 - \frac{6}{(n+3)(n+2)} - \frac{n(n-1)}{(n+3)(n+2)} \right) + 3\frac{n(n-1)}{(n+3)(n+2)} \\ &= 2+\frac{6-12+n(n-1)}{(n+3)(n+2)} \\ &= 2 + \frac{n^2-n-6}{(n+2)(n+3)}\\ &= 2 + \frac{(n-3)(n+2)}{(n+2)(n+3)} \\ &= 2 + \frac{n-3}{n+3} \\ &= \frac{2n}{n+3} \end{align*}

2008 Paper 1 Q13
D: 1500.0 B: 1452.7

Three married couples sit down at a round table at which there are six chairs. All of the possible seating arrangements of the six people are equally likely.

  1. Show that the probability that each husband sits next to his wife is \(\frac{2}{15}\).
  2. Find the probability that exactly two husbands sit next to their wives.
  3. Find the probability that no husband sits next to his wife.

2008 Paper 2 Q13
D: 1600.0 B: 1516.0

Bag \(P\) and bag \(Q\) each contain \(n\) counters, where \(n\ge2\). The counters are identical in shape and size, but coloured either black or white. First, \(k\) counters (\(0\le k\le n\)) are drawn at random from bag \(P\) and placed in bag \(Q\). Then, \(k\) counters are drawn at random from bag \(Q\) and placed in bag \(P\).

  1. If initially \(n-1\) counters in bag \(P\) are white and one is black, and all \(n\) counters in bag \(Q\) are white, find the probability in terms of \(n\) and \(k\) that the black counter ends up in bag \(P\). Find the value or values of \(k\) for which this probability is maximised.
  2. If initially \(n-1\) counters in bag \(P\) are white and one is black, and \(n-1\) counters in bag \(Q\) are white and one is black, find the probability in terms of \(n\) and \(k\) that the black counters end up in the same bag. Find the value or values of \(k\) for which this probability is maximised.


Solution:

  1. \(\,\) \begin{align*} \mathbb{P}(\text{black counter in }P) &= \mathbb{P}(\text{black counter moves twice})+\mathbb{P}(\text{black counter doesn't move}) \\ &= \mathbb{P}(\text{black counter moves out})\mathbb{P}(\text{black counter moves back}) + (1-\mathbb{P}(\text{black counter moves out})) \\ &= \frac{k}n\cdot \frac{k}{n+k}+\frac{n-k}{n} \\ &= \frac{k^2+n^2-k^2}{n(n+k)} \\ &= \frac{n^2}{n(n+k)} = \frac{n}{n+k} \end{align*} This is maximised if \(k\) is as small as possibe, ie \(k = 0\) (ie it doesn't leave it's bag)
  2. \(\,\) \begin{align*} && \mathbb{P}(\text{both counters in same bag}) &= \mathbb{P}(\text{both in }P)+ \mathbb{P}(\text{both in }Q) \\ &&&= \mathbb{P}(B_P \to Q \to P, B_Q \to P)+\mathbb{P}(B_P \text{ stays}, B_Q \to P)+\mathbb{P}(B_P \to Q, \text{both stay}) \\ &&&= \frac{k}{n} \cdot \frac{k(k-1)}{(n+k)(n+k-1)} + \frac{n-k}{n} \frac{k}{n+k} + \frac{k}{n} \frac{n(n-1)}{(n+k)(n+k-1)} \\ &&&= \frac{(k^3-k^2)+(n-k)k(n+k-1)+kn(n-1)}{n(n+k)(n+k-1)}\\ &&&= \frac{2kn(n-1)}{n(n+k)(n+k-1)}\\ &&&= \frac{2k(n-1)}{(n+k)(n+k-1)} \end{align*} \begin{align*} && \frac{P_{k+1}}{P_k} &= \frac{2(k+1)(n-1)}{(n+k+1)(n+k)} \frac{(n+k)(n+k-1)}{2k(n-1)} \\ &&&= \frac{(k+1)(n+k-1)}{k(n+k+1)} \\ &&& \geq 1 \\ \Leftrightarrow && (k+1)(n+k-1) &\geq k(n+k+1) \\ \Leftrightarrow && n-1 &\geq k \\ \end{align*} Therefore this probability is increasing while \(k \leq n-1\), ie it's maximised \(k = n-1\) or \(k=n\)

2008 Paper 3 Q13
D: 1700.0 B: 1500.0

A box contains \(n\) pieces of string, each of which has two ends. I select two string ends at random and tie them together. This creates either a ring (if the two ends are from the same string) or a longer piece of string. I repeat the process of tying together string ends chosen at random until there are none left. Find the expected number of rings created at the first step and hence obtain an expression for the expected number of rings created by the end of the process. Find also an expression for the variance of the number of rings created. Given that \(\ln 20 \approx 3\) and that \(1+ \frac12 + \cdots + \frac 1n \approx \ln n\) for large \(n\), determine approximately the expected number of rings created in the case \(n=40\,000\).


Solution: Let \(X_i\) be the indicator variable a loop is formed when there are \(i\) strings in the bag, so \(\mathbb{P}(X_i = 1) = \frac{1}{2i-1}\). Therefore \begin{align*} && Y_n &= X_n + Y_{n-1} \\ && Y_n &= X_n + \cdots + X_1 \\ \Rightarrow && \E[Y_n] &= \frac{1}{2n-1} + \frac{1}{2n-3} + \cdots + \frac{1}{1} \\ && \var[Y_n] &= \sum_{i=1}^n \frac{1}{2i-1} \frac{2i-2}{2i-1} \\ &&&= 2\sum_{i=1}^n \frac{i-1}{(2i-1)^2} \end{align*} \begin{align*} && \E[Y_{n}] &= 1 + \frac13 + \cdots + \frac{1}{2n-1} \\ &&&= 1 + \frac12 + \cdots + \frac1{2n} - \frac12\left (1 + \frac12 + \cdots + \frac1n \right) \\ &&&\approx \ln 2n -\frac12 \ln n \\ &&&= \ln 2 \sqrt{n} \\ \\ && \E[Y_{40\,000}] &= \ln 2 \sqrt{40\,000} \\ &&&= \ln 400 \\ &&&= 2 \ln 20 \approx 6 \end{align*}

2007 Paper 1 Q1
D: 1500.0 B: 1500.0

A positive integer with \(2n\) digits (the first of which must not be \(0\)) is called a balanced number if the sum of the first \(n\) digits equals the sum of the last \(n\) digits. For example, \(1634\) is a \(4\)-digit balanced number, but \(123401\) is not a balanced number.

  1. Show that seventy \(4\)-digit balanced numbers can be made using the digits \(0, 1, 2, 3\) and \(4\).
  2. Show that \(\frac16 {k \left( k+1 \right) \left( 4k+5 \right) }\) \(4\)-digit balanced numbers can be made using the digits \(0\) to \(k\). You may use the identity $\displaystyle \sum _{r=0}^{n} r^2 \equiv \tfrac{1}{6} {n \left( n+1 \right) \left( 2n+1 \right) } \;$.


Solution:

  1. For each number from \(1\) to \(8\) (4+4), we can count the number of ways it can be achieved in any way, or without including a leading \(0\). \begin{array}{c|c|c|c} \text{total} & \text{ways with }0 & \text{ways without } 0 & \text{comb}\\ \hline 8 & 1 & 1 & 1\\ 7 & 2 & 2 & 4 \\ 6 & 3 & 3 & 9 \\ 5 & 4 & 4 & 16 \\ 4 & 5 & 4 & 20 \\ 3 & 4 & 3 & 12 \\ 2 & 3 & 2 & 6 \\ 1 & 2 & 1 & 2 \\ \hline &&& 70 \end{array}
  2. For \(2k\) to \(k+1\) there are \(1 \times 1 + 2 \times 2 + \cdots i\times i+\cdots + k\times k\) ways to achieve this, (we can choose anything from \(k\) to \(k-i+1\) for the first digit, and we can never have a \(0\). For \(1\) to \(k\) we can have \(1 \times 2 + 2 \times 3 + \cdots + k \times (k+1)\) since we cannot start with a \(0\), but can have anything less than or equal to \(i\) for the first digit, and then with the \(0\) we can have the same thing starting with \(0\). Hence the answer is: \begin{align*} && S &= \sum_{i=1}^k i^2 + \sum_{i=1}^k i (i+1) \\ &&&= 2\sum_{i=1}^k i^2 + \sum_{i=1}^k i \\ &&&= \frac{1}{3} k(k+1)(2k+1) + \frac12k(k+1) \\ &&&= k(k+1) \left (\frac{2k+1}{3} + \frac{1}{2} \right) \\ &&&= \frac16 k(k+1)(4k+2+3) \\ &&&= \frac16 k(k+1)(4k+5) \end{align*}

2007 Paper 1 Q12
D: 1500.0 B: 1484.0

  1. A bag contains \(N\) sweets (where \(N \ge 2\)), of which \(a\) are red. Two sweets are drawn from the bag without replacement. Show that the probability that the first sweet is red is equal to the probability that the second sweet is red.
  2. There are two bags, each containing \(N\) sweets (where \(N \ge 2\)). The first bag contains \(a\) red sweets, and the second bag contains \(b\) red sweets. There is also a biased coin, showing Heads with probability \(p\) and Tails with probability \(q\), where \(p+q = 1\). The coin is tossed. If it shows Heads then a sweet is chosen from the first bag and transferred to the second bag; if it shows Tails then a sweet is chosen from the second bag and transferred to the first bag. The coin is then tossed a second time: if it shows Heads then a sweet is chosen from the first bag, and if it shows Tails then a sweet is chosen from the second bag. Show that the probability that the first sweet is red is equal to the probability that the second sweet is red.

2007 Paper 1 Q13
D: 1500.0 B: 1469.5

A bag contains eleven small discs, which are identical except that six of the discs are blank and five of the discs are numbered, using the numbers 1, 2, 3, 4 and 5. The bag is shaken, and four discs are taken one at a time without replacement. Calculate the probability that:

  1. all four discs taken are numbered;
  2. all four discs taken are numbered, given that the disc numbered ``3'' is taken first;
  3. exactly two numbered discs are taken, given that the disc numbered ``3'' is taken first;
  4. exactly two numbered discs are taken, given that the disc numbered ``3'' is taken;
  5. exactly two numbered discs are taken, given that a numbered disc is taken first;
  6. exactly two numbered discs are taken, given that a numbered disc is taken.


Solution: There are many ways to do the counting in each question, possibly the clearest way is to always consider the order in which discs are taken, although all methods should work equally well. For some examples Bayes rule also offers a fast solution.

  1. There are we are choose \(4\) objects in order from \(5\) (ie \({^5\P_4}\)) to obtain valid draws, this is out of a total of picking \(4\) objects from \(11\) (\({^{11}\P_4}\)). Ie the probability is: \(\displaystyle \frac{{^5\P_4}}{{^{11}\P_4}} = \frac{5! \cdot 7!}{11!} = \frac{1}{66}\) Alternatively, there are \(\binom{5}{4}\) ways to choose four numbered discs, out of \(\binom{11}{4}\) ways to choose four discs. ie \(\displaystyle \binom{5}{4} \Big / \binom{11}{4} = \frac{5 \cdot 4! \cdot 7!}{11!} = \frac{5 \cdot 4 \cdot 3 \cdot 2}{11 \cdot 10 \cdot 9 \cdot 8} = \frac1{66}\)
  2. \begin{align*} \mathbb{P}(\text{all four discs are numbered} | \text{first disc is 3}) &= \frac{\mathbb{P}(\text{all four discs are numbered and first disc is 3})}{\mathbb{P}( \text{first disc is 3})} \\ &= \frac{^4\P_3 \big / {^{11}\P_4}}{1/11} \\ &= 11\cdot \frac{4!}{1!} \Bigg / \frac{11!}{7!} \\ &= \frac{4! \cdot 7! \cdot 11}{11!} \\ &= \frac{4\cdot 3 \cdot 2}{10 \cdot 9 \cdot 8} \\ &= \frac{1}{30} \end{align*} Alternatively, \begin{align*} \mathbb{P}(\text{all four discs are numbered} | \text{first disc is 3}) &= \frac{\mathbb{P}(\text{all four discs are numbered and first disc is 3})}{\mathbb{P}( \text{first disc is 3})} \\ &= \frac{\binom{4}{3} \Bigg / 11 \cdot \binom{10}{3}}{1/11} \\ &= \frac{1}{30} \end{align*} Where we are calculating this as "choose one number", then "choose 3 more", which can happen ending up with 3, number, number, number in \(\binom{4}{3}\) ways, and there are \(11 \cdot \binom{10}{3}\) was overall. Another alternative using Bayes rule: \begin{align*} \mathbb{P}(\text{all four discs are numbered} | \text{first disc is 3}) &= \mathbb{P}( \text{first disc is 3} | \text{all four discs are numbered}) \frac{ \mathbb{P}( \text{all four discs are numbered} }{ \mathbb{P}( \text{first disc is 3} )} \\ &= \frac{\frac{1}{5} \cdot \frac{1}{66}}{\frac{1}{11}} \\ &= \frac1{30} \end{align*}
  3. \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{first disc is 3}) &=\frac{\mathbb{P}(\text{exactly two discs are numbered and first disc is 3})}{\mathbb{P}( \text{first disc is 3})} \\ &= \frac{3 \cdot {^4\P_1} \cdot {^{6}\P_2} \big / {^{11}\P_4}}{\frac1{11} } \\ &= \frac12 \end{align*} Alternatively, \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{first disc is 3}) &=\frac{\mathbb{P}(\text{exactly two discs are numbered and first disc is 3})}{\mathbb{P}( \text{first disc is 3})} \\ &= \frac{\binom{4}{1}\binom{6}{2} \Big / 11 \cdot \binom{10}{3}}{1/11} \\ &= \frac12 \end{align*}
  4. \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{3 taken}) &= \frac{\mathbb{P}(\text{exactly two discs are numbered and 3 taken})}{\mathbb{P}( \text{3 taken})} \\ &= \frac{\binom{4}{1}\binom{6}{2} \Big / \binom{11}{4}}{\frac{4}{11}} \\ &= \frac{\frac{2}{11}}{\frac4{11}} \\ &= \frac12 \end{align*} Using Bayes rule: \(\mathbb{P}( \text{3 taken}) = \frac{1}{11} + \frac{10}{11}\frac{1}{10} + \frac{10}{11}\frac{9}{10}\frac{1}{9} + \frac{10}{11}\frac{9}{10}\frac89\frac18 = \frac{4}{11}\) \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{3 taken}) &= \frac{\mathbb{P}(\text{3 taken | exactly two discs are numbered})\mathbb{P}(\text{exactly two discs are numbered})}{\mathbb{P}( \text{3 taken})} \\ &= \frac{\frac{4}{10} \cdot \binom{5}{2} \binom{6}{2} \Big / \binom{11}{4}}{4/11} \\ &= \frac{\frac4{10}{5 / 11}}{4/11} \\ &= \frac{1}{2} \end{align*}
  5. \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{numbered disc first}) &= \frac{\mathbb{P}(\text{exactly two discs are numbered and numbered disc first})}{\mathbb{P}( \text{numbered disc first})} \\ &= \frac{3 \cdot {^5\P_1}\cdot{^4\P_1}\cdot{^6\P_2} \Big / {^{11}\P_4}}{\frac{5}{11}} \\ &= \frac{1}{2} \end{align*}
  6. \begin{align*} \mathbb{P}(\text{exactly two discs are numbered} | \text{numbered disc taken}) &= \frac{\mathbb{P}(\text{exactly two discs are numbered and numbered disc taken})}{\mathbb{P}(\text{numbered disc taken})} \\ &= \frac{\mathbb{P}(\text{exactly two discs are numbered})}{1 - \mathbb{P}(\text{not numbered discs taken})} \\ &= \frac{\binom{5}{2}\binom{6}{2} \Big / \binom{11}{4}}{1 - \binom{6}{4} \Big / \binom{11}{4}} \\ &= \frac{\frac{5}{11}}{\frac{21}{22}} \\ &= \frac{10}{21} \neq \frac12 \end{align*}

2007 Paper 2 Q13
D: 1600.0 B: 1669.0

Given that \(0 < r < n\) and \(r\) is much smaller than \(n\), show that \(\dfrac {n-r}n \approx \e^{-r/n}\). There are \(k\) guests at a party. Assuming that there are exactly 365 days in the year, and that the birthday of any guest is equally likely to fall on any of these days, show that the probability that there are at least two guests with the same birthday is approximately \(1-\e^{-k(k-1)/730}\). Using the approximation \( \frac{253}{365} \approx \ln 2\), find the smallest value of \(k\) such that the probability that at least two guests share the same birthday is at least \(\frac12\). How many guests must there be at the party for the probability that at least one guest has the same birthday as the host to be at least \(\frac12\)?


Solution: Given \(0 < r \ll n\), then \(\frac{r}{n}\) is small and so, \(e^x \approx 1+x\), therefore: \(\displaystyle e^{-r/n} \approx 1 - \frac{r}{n} = \frac{n-r}{n}\). Line everyone in the room up in some order. The first person is always going to have a birthday we haven't seen before. The probability the second person has a new birthday is \(\displaystyle 1 - \frac{1}{365}\) since they can't be born on the same day as the first person. The third person has a \(\displaystyle 1 - \frac{2}{365}\) probability of having a birthday we've not seen before, since they can't share a birthday with either of the first two people. Similarly the \(k\)th person has a \(\displaystyle 1 - \frac{k-1}{365}\) chance of having a unique birthday. \begin{align*} \prod_{i=1}^k \mathbb{P}(\text{the } i \text{th person has a new birthday}) &= \prod_{i=1}^k \l 1 - \frac{i-1}{365}\r \\ &\approx \prod_{i=1}^k \exp \l -\frac{i-1}{365}\r \\ &= \exp\l - \sum_{i=1}^k\frac{i-1}{365}\r \\ &= \exp\l - \frac{k(k-1)}{2\cdot365}\r \\ &= e^{-k(k-1)/730} \end{align*} But this the probability no-one shares a birthday, so the answer we are looking for is \(1-\) this, ie \(1 - e^{-k(k-1)/730}\) Suppose \(1 - e^{-k(k-1)/730} = \frac12\), then \begin{align*} && 1 - e^{-k(k-1)/730} &= \frac12 \\ \Rightarrow && e^{-k(k-1)/730} &= \frac12 \\ \Rightarrow && -k(k-1)/730 &= -\ln 2 \\ \Rightarrow && k(k-1)/730 &\approx \frac{253}{365} \\ \Rightarrow && k(k-1) &\approx 506 \end{align*} Therefore since \(22 \cdot 23 = 506\), we should expect the number to be approximately \(23\). Since \(e^{-r/n} > \frac{n-r}{n}\) we should expect this to be an overestimate, therefore \(23\) should suffice.

2006 Paper 2 Q13
D: 1600.0 B: 1516.0

I know that ice-creams come in \(n\) different sizes, but I don't know what the sizes are. I am offered one of each in succession, in random order. I am certainly going to choose one - the bigger the better - but I am not allowed more than one. My strategy is to reject the first ice-cream I am offered and choose the first one thereafter that is bigger than the first one I was offered; if the first ice-cream offered is in fact the biggest one, then I have to put up with the last one, however small. Let \(\P_n(k)\) be the probability that I choose the \(k\)th biggest ice-cream, where \(k=1\) is the biggest and \(k=n\) is the smallest.

  1. Show that \(\P_4(1) = \frac{11}{24}\) and find \(\P_4(2)\), \(\P_4(3)\) and \(\P_4(4)\).
  2. Find an expression for \(\P_n(1)\).