From what I gather from your latest comments, the PDF is given to be
![f_{X,Y}(x,y)=\begin{cases}cxy&\text{for }0\le x,y \le1\\0&\text{otherwise}\end{cases}](https://tex.z-dn.net/?f=f_%7BX%2CY%7D%28x%2Cy%29%3D%5Cbegin%7Bcases%7Dcxy%26%5Ctext%7Bfor%20%7D0%5Cle%20x%2Cy%20%5Cle1%5C%5C0%26%5Ctext%7Botherwise%7D%5Cend%7Bcases%7D)
and in particular, <em>f(x, y)</em> = <em>cxy</em> over the unit square [0, 1]², meaning for 0 ≤ <em>x</em> ≤ 1 and 0 ≤ <em>y</em> ≤ 1. (As opposed to the unbounded domain, <em>x</em> ≤ 0 *and* <em>y</em> ≤ 1.)
(a) Find <em>c</em> such that <em>f</em> is a proper density function. This would require
![\displaystyle\int_0^1\int_0^1 cxy\,\mathrm dx\,\mathrm dy=c\left(\int_0^1x\,\mathrm dx\right)\left(\int_0^1y\,\mathrm dy\right)=\frac c{2^2}=1\implies \boxed{c=4}](https://tex.z-dn.net/?f=%5Cdisplaystyle%5Cint_0%5E1%5Cint_0%5E1%20cxy%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3Dc%5Cleft%28%5Cint_0%5E1x%5C%2C%5Cmathrm%20dx%5Cright%29%5Cleft%28%5Cint_0%5E1y%5C%2C%5Cmathrm%20dy%5Cright%29%3D%5Cfrac%20c%7B2%5E2%7D%3D1%5Cimplies%20%5Cboxed%7Bc%3D4%7D)
(b) Get the marginal density of <em>X</em> by integrating the joint density with respect to <em>y</em> :
![f_X(x)=\displaystyle\int_0^1 4xy\,\mathrm dy=(2xy^2)\bigg|_{y=0}^{y=1}=\begin{cases}2x&\text{for }0\le x\le 1\\0&\text{otherwise}\end{cases}](https://tex.z-dn.net/?f=f_X%28x%29%3D%5Cdisplaystyle%5Cint_0%5E1%204xy%5C%2C%5Cmathrm%20dy%3D%282xy%5E2%29%5Cbigg%7C_%7By%3D0%7D%5E%7By%3D1%7D%3D%5Cbegin%7Bcases%7D2x%26%5Ctext%7Bfor%20%7D0%5Cle%20x%5Cle%201%5C%5C0%26%5Ctext%7Botherwise%7D%5Cend%7Bcases%7D)
(c) Get the marginal density of <em>Y</em> by integrating with respect to <em>x</em> instead:
![f_Y(y)=\displaystyle\int_0^14xy\,\mathrm dx=\begin{cases}2y&\text{for }0\le y\le1\\0&\text{otherwise}\end{cases}](https://tex.z-dn.net/?f=f_Y%28y%29%3D%5Cdisplaystyle%5Cint_0%5E14xy%5C%2C%5Cmathrm%20dx%3D%5Cbegin%7Bcases%7D2y%26%5Ctext%7Bfor%20%7D0%5Cle%20y%5Cle1%5C%5C0%26%5Ctext%7Botherwise%7D%5Cend%7Bcases%7D)
(d) The conditional distribution of <em>X</em> given <em>Y</em> can obtained by dividing the joint density by the marginal density of <em>Y</em> (which follows directly from the definition of conditional probability):
![f_{X\mid Y}(x\mid y)=\dfrac{f_{X,Y}(x,y)}{f_Y(y)}=\begin{cases}2x&\text{for }0\le x\le 1\\0&\text{otherwise}\end{cases}](https://tex.z-dn.net/?f=f_%7BX%5Cmid%20Y%7D%28x%5Cmid%20y%29%3D%5Cdfrac%7Bf_%7BX%2CY%7D%28x%2Cy%29%7D%7Bf_Y%28y%29%7D%3D%5Cbegin%7Bcases%7D2x%26%5Ctext%7Bfor%20%7D0%5Cle%20x%5Cle%201%5C%5C0%26%5Ctext%7Botherwise%7D%5Cend%7Bcases%7D)
(e) From the definition of expectation:
![E[X]=\displaystyle\int_0^1\int_0^1 x\,f_{X,Y}(x,y)\,\mathrm dx\,\mathrm dy=4\left(\int_0^1x^2\,\mathrm dx\right)\left(\int_0^1y\,\mathrm dy\right)=\boxed{\frac23}](https://tex.z-dn.net/?f=E%5BX%5D%3D%5Cdisplaystyle%5Cint_0%5E1%5Cint_0%5E1%20x%5C%2Cf_%7BX%2CY%7D%28x%2Cy%29%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3D4%5Cleft%28%5Cint_0%5E1x%5E2%5C%2C%5Cmathrm%20dx%5Cright%29%5Cleft%28%5Cint_0%5E1y%5C%2C%5Cmathrm%20dy%5Cright%29%3D%5Cboxed%7B%5Cfrac23%7D)
![E[Y]=\displaystyle\int_0^1\int_0^1 y\,f_{X,Y}(x,y)\,\mathrm dx\,\mathrm dy=4\left(\int_0^1x\,\mathrm dx\right)\left(\int_0^1y^2\,\mathrm dy\right)=\boxed{\frac23}](https://tex.z-dn.net/?f=E%5BY%5D%3D%5Cdisplaystyle%5Cint_0%5E1%5Cint_0%5E1%20y%5C%2Cf_%7BX%2CY%7D%28x%2Cy%29%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3D4%5Cleft%28%5Cint_0%5E1x%5C%2C%5Cmathrm%20dx%5Cright%29%5Cleft%28%5Cint_0%5E1y%5E2%5C%2C%5Cmathrm%20dy%5Cright%29%3D%5Cboxed%7B%5Cfrac23%7D)
![E[XY]=\displaystyle\int_0^1\int_0^1xy\,f_{X,Y}(x,y)\,\mathrm dx\,\mathrm dy=4\left(\int_0^1x^2\,\mathrm dx\right)\left(\int_0^1y^2\,\mathrm dy\right)=\boxed{\frac49}](https://tex.z-dn.net/?f=E%5BXY%5D%3D%5Cdisplaystyle%5Cint_0%5E1%5Cint_0%5E1xy%5C%2Cf_%7BX%2CY%7D%28x%2Cy%29%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3D4%5Cleft%28%5Cint_0%5E1x%5E2%5C%2C%5Cmathrm%20dx%5Cright%29%5Cleft%28%5Cint_0%5E1y%5E2%5C%2C%5Cmathrm%20dy%5Cright%29%3D%5Cboxed%7B%5Cfrac49%7D)
(f) Note that the density of <em>X</em> | <em>Y</em> in part (d) identical to the marginal density of <em>X</em> found in (b), so yes, <em>X</em> and <em>Y</em> are indeed independent.
The result in (e) agrees with this conclusion, since E[<em>XY</em>] = E[<em>X</em>] E[<em>Y</em>] (but keep in mind that this is a property of independent random variables; equality alone does not imply independence.)
Answer:
4/10, 5/10, 6/10 are all fractions in between 3/10 and 9/10
Step-by-step explanation:
I don't think this is possible...
But if you mean 9 then the root is 3
Answer:
7.5cm²
Complete question:What is the surface area of the 8-sided figure?
Step-by-step explanation:
Consider the half of the 8-sided die i.e the pyramid ABCDE:
As the base is 6 of the perimeter, so each side of the base is 6/4=3/2
Now, the distance between the tallest points is 2 cm, so the height of Pyramid ABCDE is 1 cm.
Considering the right triangle EOM:
EO=1, OM= 3/4 (it is half of AB=3/2) and EM is the hypothenuse, so by the pythagorean theorem length of EM is
![\sqrt{1+ ( \frac{3}{4} )^{2} }= \sqrt{1+ \frac{9}{16} }= \sqrt{ \frac{25}{16} }= \frac{5}{4} (cm)](https://tex.z-dn.net/?f=%5Csqrt%7B1%2B%20%28%20%5Cfrac%7B3%7D%7B4%7D%20%29%5E%7B2%7D%20%7D%3D%20%20%5Csqrt%7B1%2B%20%5Cfrac%7B9%7D%7B16%7D%20%7D%3D%20%5Csqrt%7B%20%5Cfrac%7B25%7D%7B16%7D%20%7D%3D%20%5Cfrac%7B5%7D%7B4%7D%20%20%20%28cm%29)
5. So side EBC has area
![\frac{1}{2}BC*EM= \frac{1}{2}* \frac{3}{2}* \frac{5}{4}= \frac{15}{16}](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B2%7DBC%2AEM%3D%20%20%5Cfrac%7B1%7D%7B2%7D%2A%20%5Cfrac%7B3%7D%7B2%7D%2A%20%5Cfrac%7B5%7D%7B4%7D%3D%20%5Cfrac%7B15%7D%7B16%7D)
6. The total area is 8*Area(EBC)=![8* \frac{15}{16}= \frac{15}{2}=7.5 ( cm^{2} )](https://tex.z-dn.net/?f=8%2A%20%5Cfrac%7B15%7D%7B16%7D%3D%20%5Cfrac%7B15%7D%7B2%7D%3D7.5%20%28%20cm%5E%7B2%7D%20%29)
Answer:
-3 and 2
Step-by-step explanation: