Chapter 7: Problem 1
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.
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Chapter 7: Problem 1
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.
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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:
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