Chapter 3: Problem 18
If a fair coin is tossed at random five independent times, find the conditional probability of five heads given that there are at least four heads.
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Chapter 3: Problem 18
If a fair coin is tossed at random five independent times, find the conditional probability of five heads given that there are at least four heads.
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Suppose that \(g(x, 0)=0\) and that $$D_{w}[g(x, w)]=-\lambda g(x, w)+\lambda g(x-1, w)$$ for \(x=1,2,3, \ldots\) If \(g(0, w)=e^{-\lambda w}\), show by mathematical induction that $$g(x, w)=\frac{(\lambda w)^{x} e^{-\lambda w}}{x !}, \quad x=1,2,3, \ldots$$
Let the pmf \(p(x)\) be positive on and only on the nonnegative integers. Given that \(p(x)=(4 / x) p(x-1), x=1,2,3, \ldots\), find the formula for \(p(x)\). Hint: Note that \(p(1)=4 p(0), p(2)=\left(4^{2} / 2 !\right) p(0)\), and so on. That is, find each \(p(x)\) in terms of \(p(0)\) and then determine \(p(0)\) from $$1=p(0)+p(1)+p(2)+\cdots$$
Compute the measures of skewness and kurtosis of a gamma distribution which has parameters \(\alpha\) and \(\beta\).
If \(M\left(t_{1}, t_{2}\right)\) is the mgf of a bivariate normal distribution, compute the covariance by using the formula $$\frac{\partial^{2} M(0,0)}{\partial t_{1} \partial t_{2}}-\frac{\partial M(0,0)}{\partial t_{1}} \frac{\partial M(0,0)}{\partial t_{2}}$$ Now let \(\psi\left(t_{1}, t_{2}\right)=\log M\left(t_{1}, t_{2}\right) .\) Show that \(\partial^{2} \psi(0,0) / \partial t_{1} \partial t_{2}\) gives this covariance directly.
Let \(T\) have a \(t\) -distribution with 10 degrees of freedom. Find \(P(|T|>2.228)\) from either Table IV or, if available, by using \(\mathrm{R}\).
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