/*! 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 1 A player throws a fair die and s... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

A player throws a fair die and simultaneously flips a fair coin. If the coin lands heads, then she wins twice, and if tails, then one-half of the value that appears on the die. Determine her expected winnings.

Short Answer

Expert verified
The expected winnings for the player when throwing a fair die and flipping a fair coin simultaneously is approximately 2.0833 units.

Step by step solution

01

List all possible outcomes and their respective winnings

First, we need to list all possible outcomes of rolling a fair die and flipping a fair coin and determine the respective winnings based on the given rules: 1. Die-roll: 1, Coin-flip: Heads - Winnings: 2(1) = 2 2. Die-roll: 1, Coin-flip: Tails - Winnings: 1/2(1) = 0.5 3. Die-roll: 2, Coin-flip: Heads - Winnings: 2(2) = 4 4. Die-roll: 2, Coin-flip: Tails - Winnings: 1/2(2) = 1 5. Die-roll: 3, Coin-flip: Heads - Winnings: 2(3) = 6 6. Die-roll: 3, Coin-flip: Tails - Winnings: 1/2(3) = 1.5 7. Die-roll: 4, Coin-flip: Heads - Winnings: 2(4) = 8 8. Die-roll: 4, Coin-flip: Tails - Winnings: 1/2(4) = 2 9. Die-roll: 5, Coin-flip: Heads - Winnings: 2(5) = 10 10. Die-roll: 5, Coin-flip: Tails - Winnings: 1/2(5) = 2.5 11. Die-roll: 6, Coin-flip: Heads - Winnings: 2(6) = 12 12. Die-roll: 6, Coin-flip: Tails - Winnings: 1/2(6) = 3
02

Calculate the probability of each outcome and the respective expected value

Next, we need to calculate the probability of each of the listed outcomes. Since there are 6 sides on a fair die and 2 sides on a fair coin, the total number of possible outcomes is 6 x 2 = 12. Therefore, the probability of one specific outcome is 1/12. Now, we can calculate the expected value of the winnings for each of these outcomes by multiplying the probability of each outcome by the respective winnings: 1. \(E(1, H) = \frac{1}{12} * 2 = \frac{1}{6}\) 2. \(E(1, T) = \frac{1}{12} * 0.5 = \frac{1}{24}\) 3. \(E(2, H) = \frac{1}{12} * 4 = \frac{1}{3}\) 4. \(E(2, T) = \frac{1}{12} * 1 = \frac{1}{12}\) 5. \(E(3, H) = \frac{1}{12} * 6 = \frac{1}{2}\) 6. \(E(3, T) = \frac{1}{12} * 1.5 = \frac{1}{8}\) 7. \(E(4, H) = \frac{1}{12} * 8 = \frac{2}{3}\) 8. \(E(4, T) = \frac{1}{12} * 2 = \frac{1}{6}\) 9. \(E(5, H) = \frac{1}{12} * 10 = \frac{5}{6}\) 10. \(E(5, T) = \frac{1}{12} * 2.5 = \frac{5}{24}\) 11. \(E(6, H) = \frac{1}{12} * 12 = 1\) 12. \(E(6, T) = \frac{1}{12} * 3 = \frac{1}{4}\)
03

Calculate the total expected value

Finally, we need to add up the expected values from each outcome to find the overall expected winnings for the player: \(E(total) = E(1, H) + E(1, T) + E(2, H) + E(2, T) + E(3, H) + E(3, T) + E(4, H) + E(4, T) + E(5, H) + E(5, T) + E(6, H) + E(6, T)\) \(E(total) = \frac{1}{6} + \frac{1}{24} + \frac{1}{3} + \frac{1}{12} + \frac{1}{2} + \frac{1}{8} + \frac{2}{3} + \frac{1}{6} + \frac{5}{6} + \frac{5}{24} + 1 + \frac{1}{4}\) \(E(total) = \frac{25}{12} = 2.0833\) The expected winnings for the player when throwing a fair die and flipping a fair coin simultaneously is approximately 2.0833 units.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

An urn contains 4 white and 6 black balls. Two successive random samples of sizes 3 and 5 , respectively, are drawn from the urn without replacement. Let \(X\) and \(Y\) denote the number of white balls in the two samples, and compute, \(E[X \mid Y=i]\), for \(i=1,2,3,4\)

If a die is to be rolled until all sides have appeared at least once, find the expected number of times that outcome 1 appears.

Suppose that \(X_{1}\) and \(X_{2}\) are independent random variables having a common mean \(\mu\). Suppose also that \(\operatorname{Var}\left(X_{1}\right)=\sigma_{1}^{2}\) and \(\operatorname{Var}\left(X_{2}\right)=\sigma_{2}^{2} .\) The value of \(\mu\) is unknown and it is proposed to estimate \(\mu\) by a weighted average of \(X_{1}\) and \(X_{2}\). That is, \(\lambda X_{1}+(1-\lambda) X_{2}\) will be used as an estimate of \(\mu\), for some appropriate value of \(\lambda\). Which value of \(\lambda\) yields the estimate having the lowest possible variance? Explain why it is desirable to use this value of \(\lambda\).

For a group of 100 people compute (a) the expected number of days of the year that are birthdays of exactly 3 people; (b) the expected number of distinct birthdays.

Consider the following dice game. A pair of dice are rolled. If the sum is 7 , then the game ends and you win 0 . If the sum is not 7 , then you have the option of either stopping the game and receiving an amount equal to that sum or starting over again. For each value of \(i, i=2, \ldots, 12\), find your expected return if you employ the strategy of stopping the first time that a value at least as large as \(i\) appears. What value of \(i\) leads to the largest expected return? HINT: Let \(X_{i}\) denote the return when you use the critical value \(i\). To compute \(E\left[X_{i}\right]\), condition on the initial sum:

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.