Respuesta :
Let [tex]X_i[/tex] denote a data point taken from the distribution, where [tex]1\le i\le40[/tex], and let [tex]Y[/tex] denote the average.
You want to find
[tex]\mathbb P\left(\displaystyle\frac1{40}\sum_{i=1}^{40}X_i<490\right)=\mathbb P(Y<490)[/tex]
First, let's recall a few things. The PDF of a normal distribution with mean [tex]\mu[/tex] and variance [tex]\sigma^2[/tex] is
[tex]f(x;\mu,\sigma^2)=\displaystyle\frac1{\sigma\sqrt{2\pi}}\exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right)[/tex]
Each of the [tex]X_i[/tex] are presumably independently selected, so they are i.i.d. random variables.
The MGF of a normal distribution is
[tex]M_X(t)=\mathbb E(e^{tX})[/tex]
[tex]M_X(t)=\displaystyle\int_{-\infty}^\infty e^{tx}f_X(x)\,\mathrm dx[/tex]
[tex]M_X(t)=\exp\left(\mu t+\dfrac12\sigma^2t^2\right)[/tex]
The MGF of a linear combination of i.i.d. random variables is
[tex]M_{c_1X_1+\cdots+c_nX_n}=M_{X_1}(c_1t)\times\cdots\times M_{X_n}(c_nt)=\displaystyle\prod_{i=1}^nM_{X_i}(c_it)[/tex]
In this case, each [tex]c_i=\dfrac1{40}[/tex]. This product of MGFs reduces to an MGF of a normal distribution because the [tex]X_i[/tex] are i.i.d..
[tex]M_Y(t)=\displaystyle\prod_{i=1}^{40}M_{X_i}(t)=\exp\left(\mu\left(\dfrac t{40}\right)+\frac12\sigma^2\left(\dfrac t{40}\right)^2\right)\times\cdots\times\exp\left(\mu\left(\dfrac t{40}\right)+\frac12\sigma^2\left(\dfrac t{40}\right)^2\right)[/tex]
[tex]M_Y(t)=\exp\left(\mu t+\dfrac12\left(\dfrac\sigma{\sqrt{40}}\right)^2t^2\right)[/tex]
which is indeed the MGF of a normal distribution with mean [tex]\displaystyle\mu\sum_{i=1}^{40}\frac1{40}=\mu[/tex] and variance [tex]\sigma^2\displaystyle\sum_{i=1}^{40}\left(\frac1{40}\right)^2=\frac{\sigma^2}{40}[/tex]
So, the PDF of [tex]Y[/tex], given that [tex]\mu=485[/tex] and [tex]\sigma=11.6[/tex], is
[tex]f_Y(y)=\displaystyle\frac1{\frac{11.6}{\sqrt{40}}\sqrt{2\pi}}\exp\left(-\frac{(y-485)^2}{2\left(\frac{11.6}{\sqrt{40}}\right)^2}\right)[/tex]
Now,
[tex]\mathbb P(Y<490)=\displaystyle\int_{-\infty}^{490}f(y)\,\mathrm dy[/tex]
or, using the CDF of [tex]Y[/tex],
[tex]\mathbb P(Y<490)=F_Y(490)\approx0.9968[/tex]
You want to find
[tex]\mathbb P\left(\displaystyle\frac1{40}\sum_{i=1}^{40}X_i<490\right)=\mathbb P(Y<490)[/tex]
First, let's recall a few things. The PDF of a normal distribution with mean [tex]\mu[/tex] and variance [tex]\sigma^2[/tex] is
[tex]f(x;\mu,\sigma^2)=\displaystyle\frac1{\sigma\sqrt{2\pi}}\exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right)[/tex]
Each of the [tex]X_i[/tex] are presumably independently selected, so they are i.i.d. random variables.
The MGF of a normal distribution is
[tex]M_X(t)=\mathbb E(e^{tX})[/tex]
[tex]M_X(t)=\displaystyle\int_{-\infty}^\infty e^{tx}f_X(x)\,\mathrm dx[/tex]
[tex]M_X(t)=\exp\left(\mu t+\dfrac12\sigma^2t^2\right)[/tex]
The MGF of a linear combination of i.i.d. random variables is
[tex]M_{c_1X_1+\cdots+c_nX_n}=M_{X_1}(c_1t)\times\cdots\times M_{X_n}(c_nt)=\displaystyle\prod_{i=1}^nM_{X_i}(c_it)[/tex]
In this case, each [tex]c_i=\dfrac1{40}[/tex]. This product of MGFs reduces to an MGF of a normal distribution because the [tex]X_i[/tex] are i.i.d..
[tex]M_Y(t)=\displaystyle\prod_{i=1}^{40}M_{X_i}(t)=\exp\left(\mu\left(\dfrac t{40}\right)+\frac12\sigma^2\left(\dfrac t{40}\right)^2\right)\times\cdots\times\exp\left(\mu\left(\dfrac t{40}\right)+\frac12\sigma^2\left(\dfrac t{40}\right)^2\right)[/tex]
[tex]M_Y(t)=\exp\left(\mu t+\dfrac12\left(\dfrac\sigma{\sqrt{40}}\right)^2t^2\right)[/tex]
which is indeed the MGF of a normal distribution with mean [tex]\displaystyle\mu\sum_{i=1}^{40}\frac1{40}=\mu[/tex] and variance [tex]\sigma^2\displaystyle\sum_{i=1}^{40}\left(\frac1{40}\right)^2=\frac{\sigma^2}{40}[/tex]
So, the PDF of [tex]Y[/tex], given that [tex]\mu=485[/tex] and [tex]\sigma=11.6[/tex], is
[tex]f_Y(y)=\displaystyle\frac1{\frac{11.6}{\sqrt{40}}\sqrt{2\pi}}\exp\left(-\frac{(y-485)^2}{2\left(\frac{11.6}{\sqrt{40}}\right)^2}\right)[/tex]
Now,
[tex]\mathbb P(Y<490)=\displaystyle\int_{-\infty}^{490}f(y)\,\mathrm dy[/tex]
or, using the CDF of [tex]Y[/tex],
[tex]\mathbb P(Y<490)=F_Y(490)\approx0.9968[/tex]