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2 years ago

IT IS EXTREAMLY URGENT!!! I WILL GIVE BRANLIEST!!!!AT LEAST TAKE A LOOK!!!!!! HELPPPPPPPPP

Mathematics

1 answer:

RideAnS [48]2 years ago

6 0

Answer: D) 8 inches

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Work Shown:

Refer to the diagram below.

A = 50 degrees

B = unknown

C = 80 degrees

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For any triangle, the three angles always add to 180

A+B+C = 180

50+B+80 = 180

B+130 = 180

B = 180-130

B = 50 degrees

Since angles B and C are the same measure, their opposite sides are the same length. Triangle ABC is isosceles. Therefore, a = x = 8

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The ratio of Mary’s downloaded iPad apps to Katie’s is 12 to 8. If Katie has 96 apps how many does Mary have?

sladkih [1.3K]

8×12=96. Katie
12×12=144. Mary
you have to Divide Katie's 96 by 8 to get 12
then take Mary's 12 and multiply it by 12 to get the answer of 144 apps that Mary owns

3 0

2 years ago

Let X ~ N(0, 1) and Y = eX. Y is called a log-normal random variable.

Cloud [144]

If $F_Y(y)$ is the cumulative distribution function for $Y$ , then

$F_Y(y)=P(Y\le y)=P(e^X\le y)=P(X\le\ln y)=F_X(\ln y)$

Then the probability density function for $Y$ is $f_Y(y)={F_Y}'(y)$ :

$f_Y(y)=\dfrac{\mathrm d}{\mathrm dy}F_X(\ln y)=\dfrac1yf_X(\ln y)=\begin{cases}\frac1{y\sqrt{2\pi}}e^{-\frac12(\ln y)^2}&\text{for }y>0\\0&\text{otherwise}\end{cases}$

The $n$ th moment of $Y$ is

$E[Y^n]=\displaystyle\int_{-\infty}^\infty y^nf_Y(y)\,\mathrm dy=\frac1{\sqrt{2\pi}}\int_0^\infty y^{n-1}e^{-\frac12(\ln y)^2}\,\mathrm dy$

Let $u=\ln y$ , so that $\mathrm du=\frac{\mathrm dy}y$ and $y^n=e^{nu}$ :

$E[Y^n]=\displaystyle\frac1{\sqrt{2\pi}}\int_{-\infty}^\infty e^{nu}e^{-\frac12u^2}\,\mathrm du=\frac1{\sqrt{2\pi}}\int_{-\infty}^\infty e^{nu-\frac12u^2}\,\mathrm du$

Complete the square in the exponent:

$nu-\dfrac12u^2=-\dfrac12(u^2-2nu+n^2-n^2)=\dfrac12n^2-\dfrac12(u-n)^2$

$E[Y^n]=\displaystyle\frac1{\sqrt{2\pi}}\int_{-\infty}^\infty e^{\frac12(n^2-(u-n)^2)}\,\mathrm du=\frac{e^{\frac12n^2}}{\sqrt{2\pi}}\int_{-\infty}^\infty e^{-\frac12(u-n)^2}\,\mathrm du$

But $\frac1{\sqrt{2\pi}}e^{-\frac12(u-n)^2}$ is exactly the PDF of a normal distribution with mean $n$ and variance 1; in other words, the 0th moment of a random variable $U\sim N(n,1)$ :