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Two students,AandB,are both registered for a certain course. Assume that studentAattends class 80 percent of the time, studentBattends class 60 percent of the time, and the absences of the two students are independent. Consider the conditions of Exercise 7 of Sec. 2.2 again. If exactly one of the two students,AandB,is in class on a given day, what is the probability that it isA?

Short Answer

Expert verified

Finally, \(21\) the percent of probability that it is \(A\).

Step by step solution

01

Given information

Two students\(A\)and\(B\)are both registered for a certain course. Given that\(A\)attends class\(80\)percent of that time and\(B\)attends class\(60\)percent of that time.

Also, given the condition absence of the two students are independent.

02

State the condition

\(A\) attends class \(80\) percent at that time, so \(A\) absent \(20\) percent of the time. Then the probability of \(A\) is \(p\left( A \right) = \frac{{20}}{{100}} = 0.2\).

\(B\)present class\(60\)percent of that time. Then\(B\)not present\(40\)percent of that time. Therefore, the probability of\(B\)given by\(p\left( B \right) = \frac{{40}}{{100}} = 0.4\).

Both are absent from the class on the same day. Then both absences are given by

\(\begin{aligned}{c}p\left( {A \cap B} \right)& = p\left( A \right) \times p\left( B \right)\\ &= 0.2 \times 0.4\\& = 0.08\end{aligned}\)

Therefore, the attends of both \(A\) \(B\) is find that

\(\begin{aligned}{c}p\left( {A \cap B} \right) &= 1 - p\left( {A \cup B} \right)\\& = 1 - 0.08\\ &= 0.92\end{aligned}\)

03

Compute the probability

To compute the probability by the method of the conditional theorem. The theorem is given by

\(p\left( {A\left| B \right.} \right) = \frac{{p\left( {A \cap B} \right)}}{{p\left( B \right)}}\) . In this condition, we use the theorem is

\(\begin{aligned}{c}p\left( {A\left| {A B} \right.} \right) &= \frac{{p\left( {A \cap \left( {A \cup B} \right)} \right)}}{{p\left( {A \cup B} \right)}}\\ &= \frac{{p\left( A \right)}}{{p\left( {A \cup B} \right)}}\end{aligned}\)

Hence the probability that it is \(A\) is given by

\(\begin{aligned}{c}\frac{{p\left( A \right)}}{{p\left( {A \cup B} \right)}} &= \frac{{0.2}}{{0.92}}\\ &= 0.21\end{aligned}\)

Finally, \(21\) the percent of probability that it is \(A\).

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Most popular questions from this chapter

Question:Suppose that two random variables X and Y have the joint p.d.f.\(f\left( {x,y} \right) = \left\{ \begin{array}{l}k{x^2}{y^2}\,\,\,\,\,\,\,\,\,\,\,\,for\,{x^2} + {y^2} \le 1\\0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,otherwise\end{array} \right.\). Compute the conditional p.d.f. of X given

Y = y for each y.

Question:Suppose thatXandYhave a discrete joint distributionfor which the joint p.f. is defined as follows:

\({\bf{f}}\left( {{\bf{x,y}}} \right){\bf{ = }}\left\{ \begin{array}{l}\frac{{\bf{1}}}{{{\bf{30}}}}\left( {{\bf{x + y}}} \right)\;{\bf{for}}\;{\bf{x = 0,1,2}}\;{\bf{and}}\;{\bf{y = 0,1,2,3}}\\{\bf{0}}\;{\bf{otherwise}}\end{array} \right.\)

a. Determine the marginal p.f.’s ofXandY.

b. AreXandYindependent?

The definition of the conditional p.d.f. of X given\({\bf{Y = y}}\)is arbitrary if\({{\bf{f}}_{\bf{2}}}\left( {\bf{y}} \right){\bf{ = 0}}\). The reason that this causes no serious problem is that it is highly unlikely that we will observe Y close to a value\({{\bf{y}}_{\bf{0}}}\)such that\({{\bf{f}}_{\bf{2}}}\left( {{{\bf{y}}_{\bf{0}}}} \right){\bf{ = 0}}\). To be more precise, let\({{\bf{f}}_{\bf{2}}}\left( {{{\bf{y}}_{\bf{0}}}} \right){\bf{ = 0}}\), and let\({{\bf{A}}_{\bf{0}}}{\bf{ = }}\left( {{{\bf{y}}_{\bf{0}}}{\bf{ - }} \in {\bf{,}}{{\bf{y}}_{\bf{0}}}{\bf{ + }} \in } \right)\). Also, let\({{\bf{y}}_{\bf{1}}}\)be such that\({{\bf{f}}_{\bf{2}}}\left( {{{\bf{y}}_{\bf{1}}}} \right){\bf{ > 0}}\), and let\({{\bf{A}}_{\bf{1}}}{\bf{ = }}\left( {{{\bf{y}}_{\bf{1}}}{\bf{ - }} \in {\bf{,}}{{\bf{y}}_{\bf{1}}}{\bf{ + }} \in } \right)\). Assume that\({{\bf{f}}_{\bf{2}}}\)is continuous at both\({{\bf{y}}_{\bf{0}}}\)and\({{\bf{y}}_{\bf{1}}}\).

Show that

\(\mathop {{\bf{lim}}}\limits_{ \in \to {\bf{0}}} \,\frac{{{\bf{Pr}}\left( {{\bf{Y}} \in {{\bf{A}}_{\bf{0}}}} \right)}}{{{\bf{Pr}}\left( {{\bf{Y}} \in {{\bf{A}}_{\bf{1}}}} \right)}}{\bf{ = 0}}{\bf{.}}\)

That is, the probability that Y is close to\({{\bf{y}}_{\bf{0}}}\)is much smaller than the probability that Y is close to\({{\bf{y}}_{\bf{1}}}\).

Each time that a shopper purchases a tube of toothpaste, she chooses either brand A or brand B. Suppose that the probability is 1/3 that she will choose the same brand chosen on her previous purchase, and the probability is 2/3 that she will switch brands.

a. If her first purchase is brand A, what is the probability that her fifth purchase will be brand B?

b. If her first purchase is brand B, what is the probability that her fifth purchase will be brand B?

For the conditions of Exercise 1, find the p.d.f. of the

average \(\frac{{\left( {{{\bf{X}}_{\bf{1}}}{\bf{ + }}{{\bf{X}}_{\bf{2}}}} \right)}}{{\bf{2}}}\)

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