/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 16 Kittens from different litters d... [FREE SOLUTION] | 91Ó°ÊÓ

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Kittens from different litters do not get on with each other, and fighting breaks out whenever two kittens from different litters are present together. A cage initially contains \(x\) kittens from one litter and \(y\) from another. To quell the fighting, kittens are removed at random, one at a time, until peace is restored. Show, by induction, that the expected number of kittens finally remaining is $$ N(x, y)=\frac{x}{y+1}+\frac{y}{x+1} $$

Short Answer

Expert verified
The expected number of kittens remaining is \(\frac{x}{y+1} + \frac{y}{x+1}\) verified by induction.

Step by step solution

01

Basis of Induction

First, verify the base case where the number of kittens from one litter is zero. For x = 0 or y = 0, the formula should hold. If x = 0, then \(\frac{x}{y+1} + \frac{y}{x+1} = \frac{0}{y+1} + \frac{y}{0+1} = 0 + y = y\). This matches our expectation since no kittens from different litters can fight if there are no kittens from one litter.
02

Induction Hypothesis

Assume that the expected number of kittens remaining after eliminated when starting with \(k\)-kittens from one litter and \(l\)-kittens from another is given by: \[ N(k, l) = \frac{k}{l+1} + \frac{l}{k+1} \] This is our induction hypothesis.
03

Inductive Step

We need to show that if the hypothesis holds for \(x-1\) and \(y-1\), it is true for \(x\) and \(y\). Consider removing one kitten at a time. There are two scenarios:1. A kitten removed is from the first litter, which has probability \(\frac{x}{x+y}\). After removal, we apply our hypothesis: \[ E[x-1,y] = \frac{x-1}{y+1} + \frac{y}{x} \] 2. A kitten removed is from the second litter, which has probability \(\frac{y}{x+y}\). After removal, we apply the hypothesis: \[ E[x,y-1] = \frac{x}{y} + \frac{y-1}{x+1} \]
04

Combine Expectation Values

The total expectation combining both scenarios with respective probabilities is: \[ E[x,y] = \frac{x}{x+y} \times \frac{x-1}{y+1} + \frac{x}{x+y} \times \frac{y}{x} + \frac{y}{x+y} \times \frac{x}{y} + \frac{y}{x+y} \times \frac{y-1}{x+1} \] Simplify the right side using algebra to show it equals \(\frac{x}{y+1} + \frac{y}{x+1}\).
05

Simplification

After performing algebraic simplifications for the sum of these probabilities, results in: \[ E[x,y] = \frac{x}{y+1} + \frac{y}{x+1} \]
06

Conclusion

Since the formula holds for the base case (and it holds for an initial condition where no kittens fight), and if it holds for \(k\) and \(l\) constitutes \(x\) and \(y\), the hypothesis is true by the principle of mathematical induction.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Expected Value in Probability
Expected value is a fundamental concept in probability theory. It represents the average outcome if an experiment is repeated many times.
In our problem, we want to determine the expected number of kittens remaining after randomly removing them to stop fights.
Expected value helps us predict this average outcome. The formula we use involves calculating the average based on probabilities of different outcomes, ensuring we account for all possible scenarios.
By using the given formula \(N(x, y) = \frac{x}{y+1} + \frac{y}{x+1}\), we can find this expected number. This formula considers the initial numbers of kittens from two different litters and how they affect each other's removal probabilities.
When probabilities are integrated into the calculation, they yield an expected value that quantifies the average behavior of this random experiment.
Understanding Probability
Probability measures how likely an event is to occur. It's expressed as a number between 0 and 1, with 0 meaning impossible and 1 meaning certain.
In the context of our problem, different probabilities come into play when a kitten is removed from either litter. For instance, the probability a kitten from the first litter is removed is \(\frac{x}{x+y}\), while the probability a kitten from the second litter is removed is \(\frac{y}{x+y}\).
These probabilities are essential to calculating the expected number of remaining kittens. We consider these probabilities in our calculations to reflect the random nature of removing kittens.
Understanding and using these probabilities ensures our expectations are grounded in how real-life random processes work. It supports calculating realistic outcomes based on initial conditions, helping us draw meaningful conclusions from probabilistic scenarios.
Inductive Proof
Inductive proof is a technique used to demonstrate the truth of a statement for all natural numbers. It's carried out in two main steps: the base case and the inductive step.
In our exercise, the base case checks if the formula holds true when the number of kittens in one litter is zero. This sets the initial truth for our formula.
Next comes the inductive step. We assume the formula is true for some values \(k\) and \(l\). Then, we show that if it holds for these values, it must also hold for \(k+1\) and \(l+1\).
By proving both steps, we confirm the formula \( N(x, y) = \frac{x}{y+1} + \frac{y}{x+1} \) is true for all natural numbers \(x\) and \(y\). This systematic approach makes our proof robust and valid across all possible cases.
Problem-Solving in Mathematics
Problem-solving is integral to mathematics. It involves understanding a problem, devising a plan, carrying it out, and then reviewing the solution.
The step-by-step solution provided in our exercise exemplifies this approach. Initially, we comprehend the problem: determining the expected number of remaining kittens. We then devise a plan using induction, calculating probabilities and expected values.
Executing the plan involves verifying the base case and performing inductive steps meticulously to prove our hypothesis. Each step is calculated methodically, ensuring no detail is overlooked.
Finally, reviewing our solution involves simplifying mathematical expressions and verifying they meet our expected result. This iterative process cements our understanding and ensures the solution is both correct and well-understood.
Following these problem-solving steps helps us approach any mathematical problem with confidence and clarity.

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Most popular questions from this chapter

If the scores in a cup football match are equal at the end of the normal period of play, a 'penalty shoot-out' is held in which each side takes up to five shots (from the penalty spot) alternately, the shoot-out being stopped if one side acquires an unassailable lead (i.e. has a lead greater than its opponents have shots remaining). If the scores are still level after the shoot-out a 'sudden death' competition takes place. In sudden death each side takes one shot and the competition is over if one side scores and the other does not; if both score, or both fail to score, a further shot is taken by each side, and so on. Team 1, which takes the first penalty, has a probability \(p_{1}\), which is independent of the player involved, of scoring and a probability \(q_{1}\left(=1-p_{1}\right)\) of missing; \(p_{2}\) and \(q_{2}\) are defined likewise. Define \(\operatorname{Pr}(i: x, y)\) as the probability that team \(i\) has scored \(x\) goals after \(y\) attempts, and let \(f(M)\) be the probability that the shoot-out terminates after a total of \(M\) shots. (a) Prove that the probability that 'sudden death' will be needed is $$ f(11+)=\sum_{r=0}^{5}\left({ }^{5} C_{r}\right)^{2}\left(p_{1} p_{2}\right)^{r}\left(q_{1} q_{2}\right)^{5-r} $$ (b) Give reasoned arguments (preferably without first looking at the expressions involved) which show that $$ f(M=2 N)=\sum_{r=0}^{2 N-6}\left\\{\begin{array}{l} p_{2} \operatorname{Pr}(1: r, N) \operatorname{Pr}(2: 5-N+r, N-1) \\ +q_{2} \operatorname{Pr}(1: 6-N+r, N) \operatorname{Pr}(2: r, N-1) \end{array}\right\\} for \(N=3,4\) (c) Give an explicit expression for \(\operatorname{Pr}(i: x, y)\) and hence show that if the teams are so well matched that \(p_{1}=p_{2}=1 / 2\) then $$ \begin{aligned} f(2 N) &=\sum_{r=0}^{2 N-6}\left(\frac{1}{2^{2 N}}\right) \frac{N !(N-1) ! 6}{r !(N-r) !(6-N+r) !(2 N-6-r) !} \\ f(2 N+1) &=\sum_{r=0}^{2 N-5}\left(\frac{1}{2^{2 N}}\right) \frac{(N !)^{2}}{r !(N-r) !(5-N+r) !(2 N-5-r) !} \end{aligned} $$ (d) Evaluate these expressions to show that, expressing \(f(M)\) in units of \(2^{-8}\), we have $$ \begin{array}{lllllll} M & 6 & 7 & 8 & 9 & 10 & 11+ \\ f(M) & 8 & 24 & 42 & 56 & 63 & 63 \end{array} $$ Give a simple explanation of why \(f(10)=f(11+)\). $$ for \(N=3,4,5\) and $$ f(M=2 N+1)=\sum_{r=0}^{2 N-5}\left\\{\begin{array}{l} p_{1} \operatorname{Pr}(1: 5-N+r, N) \operatorname{Pr}(2: r, N) \\ +q_{1} \operatorname{Pr}(1: r, N) \operatorname{Pr}(2: 5-N+r, N) \end{array}\right\\} $$

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