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Question: A JEE aspirant estimates that she will be successful with an 80 percent chance if she studies 10 hou...

A JEE aspirant estimates that she will be successful with an 80 percent chance if she studies 10 hours per day, with a 60 percent chance if she studies 7 hours per day and with 40 percent chance if she studies 4 hours per day. She further believes that she will study 10 hours, 7 hours, and 4 hours per day with probabilities 0.1, 0.2 and 0.7 respectively. Given that she does not achieve success, the chance she studied for 4 hour, is$$$$
A.\dfrac{18}{26}$$$$$ B. \dfrac{19}{26} C. $\dfrac{20}{26}
D. 2126\dfrac{21}{26}$$$$

Explanation

Solution

We denote the event that that the JEE student will study 10 hours , 7 hours and 4 hours per day as E1,E2,E3{{E}_{1}},{{E}_{2}},{{E}_{3}}. We are given the probabilities P(E1),P(E2),P(E3)P\left( {{E}_{1}} \right),P\left( {{E}_{2}} \right),P\left( {{E}_{3}} \right). We denote the new event of not achieving success as XX. We find the probability that she is not successful subjected to condition that she has studied 10 hours as P(XE1)P\left( X|{{E}_{1}} \right), she has studied 7 hours as P(XE1)P\left( X|{{E}_{1}} \right), she has studied 4 hours as P(XE1)P\left( X|{{E}_{1}} \right). We find the required probability that she has not achieved success subjected to condition that she has studied 4 hours P(E3X)P\left( {{E}_{3}}|X \right) using Bayes’ theorem P(EiX)=P(Ei)P(XEi)i=1nP(Ei)P(XEi)P\left( {{E}_{i}}|X \right)=\dfrac{P\left( {{E}_{i}} \right)P\left( X|{{E}_{i}} \right)}{\sum\limits_{i=1}^{n}{P\left( {{E}_{i}} \right)P\left( X|{{E}_{i}} \right)}} for n=3n=3. $$$$

Complete step-by-step solution:
Bayes’ theorem is used when there are more than to two events dependent on each other.. If there are nn events say E1,E2,E3,...,En(EiΦ){{E}_{1}},{{E}_{2}},{{E}_{3}},...,{{E}_{n}}\left( {{E}_{i}}\ne \Phi \right). Let XX be a new event such that Xi=1nEi,P(X)0X\subset \bigcup\limits_{i=1}^{n}{{{E}_{i}}},P\left( X \right)\ne 0, then the probability of event Ei{{E}_{i}} has happened subjected to XX has happened is given by

P(EiX)=P(Ei)P(XEi)i=1nP(Ei)P(XEi)P\left( {{E}_{i}}|X \right)=\dfrac{P\left( {{E}_{i}} \right)P\left( X|{{E}_{i}} \right)}{\sum\limits_{i=1}^{n}{P\left( {{E}_{i}} \right)P\left( X|{{E}_{i}} \right)}}
Where P(Ei)P\left( {{E}_{i}} \right) are prior probabilities, P(XEi)P\left( X|{{E}_{i}} \right) are posterior probabilities and P(X)P\left( X \right) is the probability of new event. Let us denote the event that that the JEE student will study 10 hours , 7 hours and 4 hours per day as ${{E}_{1}},{{E}_{2}},{{E}_{3}}$. We are given in the question that the probability she will study 10 hours , 7 hours and 4 hours as $P\left( {{E}_{1}} \right)=0.1,P\left( {{E}_{2}} \right)=0.2,P\left( {{E}_{3}} \right)=0.7$ .These are our prior probabilities.
We denote that she will be successful as SS and not successful as XX. We are also given in the question that that the probability that she will be successful subjected to condition that she studies 10 hours is P(SE1)=80%=0.8P\left( S|{{E}_{1}} \right)=80 \%=0.8, she studies 7 hours is P(SE2)=60%=0.6P\left( S|{{E}_{2}} \right)=60 \%=0.6 and she studies 4 hours is P(SE3)=40%=0.4P\left( S|{{E}_{3}} \right)=40 \% =0.4. We are asked to find the probability she has studied four hours after the new event of being not successful which is $P\left( {{E}_{3}}|X \right)$. So we need to find posterior probabilities. The probability that she will not be successful after studying 10 hours is $P\left( X|{{E}_{1}} \right)=1-P\left( S|{{E}_{1}} \right)=1-0.8=0.2$, after studying 7 hours is $P\left( X|{{E}_{2}} \right)=1-P\left( S|{{E}_{2}} \right)=1-0.6=0.4$ and after studying 4 hours is $P\left( X|{{E}_{3}} \right)=1-P\left( S|{{E}_{3}} \right)=1-0.4=0.6$.
Now we can use the Bayes’ theorem for three events to find P(E3X)P\left( {{E}_{3}}|X \right)

& P\left( {{E}_{3}}|X \right)=\dfrac{P\left( {{E}_{3}} \right)P\left( X|{{E}_{3}} \right)}{\sum\limits_{i=1}^{3}{P\left( {{E}_{i}} \right)P\left( X|{{E}_{i}} \right)}} \\\ & \Rightarrow P\left( {{E}_{3}}|X \right)=\dfrac{P\left( {{E}_{3}} \right)P\left( X|{{E}_{3}} \right)}{P\left( {{E}_{1}} \right)P\left( X|{{E}_{1}} \right)+P\left( {{E}_{2}} \right)P\left( X|{{E}_{2}} \right)+P\left( {{E}_{3}} \right)P\left( X|{{E}_{3}} \right)} \\\ & \Rightarrow P\left( {{E}_{3}}|X \right)=\dfrac{0.7\times 0.6}{0.1\times 0.2+0.2\times 0.4+0.7\times 0.6} \\\ & \Rightarrow P\left( {{E}_{3}}|X \right)=\dfrac{0.42}{0.1\times 0.2+0.2\times 0.4+0.7\times 0.6}=\dfrac{0.42}{0.52}=\dfrac{21}{26} \\\ \end{aligned}$$ **So the correct option is D.** **Note:** We note that the events ${{E}_{1}},{{E}_{2}},{{E}_{3}},...,{{E}_{n}}\left( {{E}_{i}}\ne \Phi \right)$ have to be mutually exclusive (do not occur at the same time) and exhaustive (sum of events sets is the sample space) for us to use Bayes’ theorem. . We see in this problem that she can only study in one day for a certain hours. So the events ${{E}_{1}},{{E}_{2}},{{E}_{3}}$ are mutually exclusive. We also see that $P\left( {{E}_{1}} \right)+P\left( {{E}_{2}} \right)+P\left( {{E}_{3}} \right)=0.1+0.2+0.7=1$ . So the events are exhaustive too. We also note that the new event must not be improbable which means $P\left( X \right)\ne 0$ .