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If the distribution function of \(F\) is given by $$ F(b)=\left\\{\begin{array}{ll} 0, & b<0 \\ \frac{1}{2}, & 0 \leq b<1 \\ \frac{3}{3}, & 1 \leq b<2 \\ \frac{4}{3}, & 2 \leq b<3 \\ \frac{9}{10}, & 3 \leq b<3.5 \\ 1, & b \geq 3.5 \end{array}\right. $$ calculate the probability mass function of \(X\).

Short Answer

Expert verified
The probability mass function of the random variable \(X\) is: $$ p(x)=\left\{\begin{array}{ll} \frac{1}{2}, & x=0 \\ \frac{1}{2}, & x=1 \\ \frac{1}{3}, & x=2 \\ \frac{1}{10}, & x=3 \end{array}\right. $$

Step by step solution

01

Identify intervals and values for the PMF

First, let's identify the intervals and the values where the distribution function F(b) changes: - \(0 \leq b < 1\) - \(1 \leq b < 2\) - \(2 \leq b < 3\) - \(3 \leq b < 3.5\) - \(3.5 \leq b\) Note that these are the intervals when the distribution function has different values.
02

Calculate the differences of the function F(b) for the intervals

Now, we will calculate the differences in the distribution function for each interval. 1. For interval \(0 \leq b < 1\), \(p(0) = F(0) - F(-0.1) = \frac{1}{2} - 0 = \frac{1}{2}\). 2. For interval \(1 \leq b < 2\), \(p(1) = F(1) - F(0.9) = 1 - \frac{1}{2} = \frac{1}{2}\). 3. For interval \(2 \leq b < 3\), \(p(2) = F(2) - F(1.9) = \frac{4}{3} - 1 = \frac{1}{3}\). 4. For interval \(3 \leq b < 3.5\), \(p(3) = F(3) - F(2.9) = \frac{9}{10} - \frac{4}{3} = \frac{1}{10}\). 5. For interval \(3.5 \leq b\), since \(F(b) = 1\) for \(b \geq 3.5\), there are no more possible values for the random variable after \(b=3.5\).
03

State the probability mass function for X

The probability mass function of the random variable \(X\) is: $$ p(x)=\left\{\begin{array}{ll} \frac{1}{2}, & x=0 \\ \frac{1}{2}, & x=1 \\ \frac{1}{3}, & x=2 \\ \frac{1}{10}, & x=3 \end{array}\right. $$

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Most popular questions from this chapter

Suppose a coin having probability \(0.7\) of coming up heads is tossed three times. Let \(X\) denote the number of heads that appear in the three tosses. Determine the probability mass function of \(X\).

Suppose that an experiment can result in one of \(r\) possible outcomes, the ith outcome having probability \(p_{i}, i=1, \ldots, r, \sum_{i=1}^{r} p_{i}=1 .\) If \(n\) of these experiments are performed, and if the outcome of any one of the \(n\) does not affect the outcome of the other \(n-1\) experiments, then show that the probability that the first outcome appears \(x_{1}\) times, the second \(x_{2}\) times, and the \(r\) th \(x_{r}\) times is $$ \frac{n !}{x_{1} ! x_{2} ! \ldots x_{r} !} p_{1}^{x_{1}} p_{2}^{x_{2}} \cdots p_{r}^{x_{r}} \quad \text { when } x_{1}+x_{2}+\cdots+x_{r}=n $$ This is known as the multinomial distribution.

A television store owner figures that 50 percent of the customers entering his store will purchase an ordinary television set, 20 percent will purchase a color television set, and 30 percent will just be browsing. If five customers enter his store on a certain day, what is the probability that two customers purchase color sets, one customer purchases an ordinary set, and two customers purchase nothing?

Let \(X\) and \(Y\) each take on either the value 1 or \(-1\). Let $$ \begin{aligned} p(1,1) &=P\\{X=1, Y=1\\} \\ p(1,-1) &=P[X=1, Y=-1\\} \\ p(-1,1) &=P[X=-1, Y=1\\} \\ p(-1,-1) &=P\\{X=-1, Y=-1\\} \end{aligned} $$ Suppose that \(E[X]=E[Y]=0\). Show that (a) \(p(1,1)=p(-1,-1) ;\) (b) \(p(1,-1)=p(-1,1)\). Let \(p=2 p(1,1) .\) Find (c) \(\operatorname{Var}(X)\); (d) \(\operatorname{Var}(Y)\) (e) \(\operatorname{Cov}(X, Y)\).

If the coin is assumed fair, then, for \(n=2\), what are the probabilities associated with the values that \(X\) can take on?

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