Answer:
![P(X>150) = \Phi(-4.3301)](https://img.qammunity.org/2021/formulas/mathematics/college/a7zm5om8df8lg10xiu49msvzjwidbauewc.png)
Explanation:
Let the random variable
denote the time needed to train a contestant.
Suppose that the time it takes to train a contestant has mean 10 days and standard deviation 2 days, independent of the time it takes other contestants to train. Therefore,
![E(X_i) = 10, Var(X_i) = 4](https://img.qammunity.org/2021/formulas/mathematics/college/9sw1aojlehhr0sm6134gsd8xk5cnctfhyl.png)
Let
be a random variable that counts the number of days needed to train all candidates. Thus,
![X = \sum_(i=1)^(12) X_(i)](https://img.qammunity.org/2021/formulas/mathematics/college/izmchl30h3u3vxgot49uz920f4aryfa08m.png)
Then, by taking expectation of both sides, we obtain
![E(X) = E \left(\sum_(i=1)^(12) X_(i) \right)](https://img.qammunity.org/2021/formulas/mathematics/college/y0lyd22nrxnf5g7vbim47upmaulvknffgz.png)
Because of the linearity of expectation, we obtain
![E(X) = \sum_(i=1)^(12) E(X_(i))](https://img.qammunity.org/2021/formulas/mathematics/college/kmea8x889h73codcoe25h2axrapjr88lnv.png)
It is given that for all
![E(X_i) = 10](https://img.qammunity.org/2021/formulas/mathematics/college/rzezc2t5bvsx4rtc5qxkz3f8x4ujvt80b7.png)
Now, we have that
![E(X) = \sum_(i=1)^(12) E(X_(i)) = \sum_(i=1)^(12) 10 = 12 \cdot 10 = 120](https://img.qammunity.org/2021/formulas/mathematics/college/471v4tx3pdneee8uijyxdp8vruirzbqxc6.png)
which means that the total time needed to train all candidates is 120 days.
Let's calculate the variance of
. It is given that the time needed to train one candidate is independent of the time it takes other contestants to train. Hence, we know that
are independent. Therefore, the variance of a sum of 12 random variables equals the sum of variances of each of them and we obtain
![Var(X) = Var\left( \sum_(i=1)^(12) X_i \right) = \sum_(i=1)^(12) Var(X_i)](https://img.qammunity.org/2021/formulas/mathematics/college/ptyi1lmoy3ef42allo8qwpq0cmlxk6gqvy.png)
It is given that for all
![Var(X_i) = 4](https://img.qammunity.org/2021/formulas/mathematics/college/kgq0m5hye8dvudzbf76xdrt9sle4y63us5.png)
Now, we have that
![Var(X) = \sum_(i=1)^(12)Var(X_(i)) = \sum_(i=1)^(12) 4 = 12 \cdot 4 = 48](https://img.qammunity.org/2021/formulas/mathematics/college/7er4yn3ahhywmyrict733lapl8qghg2ct8.png)
Apply the Central Limit Theorem.
![(X- E(X))/(√(Var(X)) ) = (X- 120)/(√(46) ) : N(0,1)](https://img.qammunity.org/2021/formulas/mathematics/college/uvphw0kpdgb8hsvilbpsfqbwr1syzmxjfe.png)
Now, let's calculate the probability that it will take more than 150 days to train all the contestants.
![P(X> 150 ) = 1 - P(X\leq 150) \\\\\phantom{P(X> 150 ) }= 1- P\left( (X- 120)/(√(46) ) \leq (150- 120)/(√(46) ) \right)\\\\\phantom{P(X> 150 )} = 1- P\left( (X- 120)/(√(46) ) \leq (30)/(√(46) ) \right)\\\\\phantom{P(X> 150 )} = 1 - P(Z \leq 4.3301), Z:N(0,1)\\\\\phantom{P(X> 150 )} = 1 - \Phi(4.3301) \\\\\phantom{P(X> 150 )} = \Phi(-4.3301)](https://img.qammunity.org/2021/formulas/mathematics/college/kx6s11g5piwrpw9oj8ufbh7zbg2ljvkmur.png)
Therefore, the approximation by the Central Limit Theorem that it will take more than 150 days to train all the contestants is
![P(X>150) = \Phi(-4.3301)](https://img.qammunity.org/2021/formulas/mathematics/college/a7zm5om8df8lg10xiu49msvzjwidbauewc.png)