Scale:
1 cm : 6 inches
---------------------------------
Length of the aquarium:
---------------------------------
5 feet = 5 x 12 = 60 inches
60 ÷ 6 = 10
The length will be 10 cm on the scale drawing.
---------------------------------
Width of the aquarium:
---------------------------------
2 feet = 2 x 12 = 24 inches
24 ÷ 6 = 4
The width will be 4 cm on the scale drawing.
---------------------------------
Find Perimeter:
---------------------------------
Perimeter = 2 (length + width)
Perimeter = 2( 10 + 4 )
Perimeter = 28 cm
--------------------------------------------------------------------------------------------------
Answer: The perimeter of the scale drawing of the front face is 28 cm.--------------------------------------------------------------------------------------------------
Answer:

Step-by-step explanation:
<u><em>Step 1:</em></u>

<u><em>Step 2:</em></u>

<u><em>Step 3:</em></u>

<u><em>Step 4:</em></u>
Adding
to both sides to complete the square

<u><em>Step 5:</em></u>

<u><em>Step 6:</em></u>
Taking square root on both sides

Answer:
1. Option a
2. Option a
3. Population proportion
4. Sample proportion
Step-by-step explanation:
1The entire city of Athens is the:_______
a. population.
2) The 200 citizens from the researcher's data is the:______
a. sample
3) Which symbol would denote the value of 0.378 in this example?
Population proportion- average estimate of those residents in the entire city that are below the poverty line.
4) Which symbol would denote the value of 0.25 in this example?
Sample proportion - the average estimate of the 200 random sample residents that are below the poverty line.
I don't quite understand the question but the total number of miles is 68
First, you'll need to find the marginal distributions of
. By the law of total probability,

which translates to

Similarly,

Compute the expectations for both random variables:
![E[X]=\displaystyle\int_{-\infty}^\infty x\,f_X(x)\,\mathrm dx=\int_0^12x(1-x)\,\mathrm dx=\frac13](https://tex.z-dn.net/?f=E%5BX%5D%3D%5Cdisplaystyle%5Cint_%7B-%5Cinfty%7D%5E%5Cinfty%20x%5C%2Cf_X%28x%29%5C%2C%5Cmathrm%20dx%3D%5Cint_0%5E12x%281-x%29%5C%2C%5Cmathrm%20dx%3D%5Cfrac13)
![E[Y]=\displaystyle\int_{-\infty}^\infty y\,f_Y(y)\,\mathrm dy=\int_0^12y^2\,\mathrm dy=\frac23](https://tex.z-dn.net/?f=E%5BY%5D%3D%5Cdisplaystyle%5Cint_%7B-%5Cinfty%7D%5E%5Cinfty%20y%5C%2Cf_Y%28y%29%5C%2C%5Cmathrm%20dy%3D%5Cint_0%5E12y%5E2%5C%2C%5Cmathrm%20dy%3D%5Cfrac23)
Compute the variances and thus standard deviations:
![V[X]=E[(X-E[X])^2]=E[X^2]-E[X]^2](https://tex.z-dn.net/?f=V%5BX%5D%3DE%5B%28X-E%5BX%5D%29%5E2%5D%3DE%5BX%5E2%5D-E%5BX%5D%5E2)
where
![E[X^2]=\displaystyle\int_{-\infty}^\infty x^2\,f_X(x)\,\mathrm dx=\int_0^12x^2(1-x)\,\mathrm dx=\frac16](https://tex.z-dn.net/?f=E%5BX%5E2%5D%3D%5Cdisplaystyle%5Cint_%7B-%5Cinfty%7D%5E%5Cinfty%20x%5E2%5C%2Cf_X%28x%29%5C%2C%5Cmathrm%20dx%3D%5Cint_0%5E12x%5E2%281-x%29%5C%2C%5Cmathrm%20dx%3D%5Cfrac16)
![\implies V[X]=\dfrac16-\left(\dfrac13\right)^2=\dfrac1{18}\implies\sqrt{V[X]}=\dfrac1{3\sqrt2}](https://tex.z-dn.net/?f=%5Cimplies%20V%5BX%5D%3D%5Cdfrac16-%5Cleft%28%5Cdfrac13%5Cright%29%5E2%3D%5Cdfrac1%7B18%7D%5Cimplies%5Csqrt%7BV%5BX%5D%7D%3D%5Cdfrac1%7B3%5Csqrt2%7D)
![E[Y^2]=\displaystyle\int_{\infty}^\infty y^2f_Y(y)\,\mathrm dy=\int_0^12y^3\,\mathrm dy=\frac12](https://tex.z-dn.net/?f=E%5BY%5E2%5D%3D%5Cdisplaystyle%5Cint_%7B%5Cinfty%7D%5E%5Cinfty%20y%5E2f_Y%28y%29%5C%2C%5Cmathrm%20dy%3D%5Cint_0%5E12y%5E3%5C%2C%5Cmathrm%20dy%3D%5Cfrac12)
![\implies V[Y]=\dfrac12-\left(\dfrac23\right)^2=\dfrac1{18}\implies\sqrt{V[Y]}=\dfrac1{3\sqrt2}](https://tex.z-dn.net/?f=%5Cimplies%20V%5BY%5D%3D%5Cdfrac12-%5Cleft%28%5Cdfrac23%5Cright%29%5E2%3D%5Cdfrac1%7B18%7D%5Cimplies%5Csqrt%7BV%5BY%5D%7D%3D%5Cdfrac1%7B3%5Csqrt2%7D)
Compute the covariance:
![\operatorname{Cov}[X,Y]=E[(X-E[X])(Y-E[Y])]=E[XY]-E[X]E[Y]](https://tex.z-dn.net/?f=%5Coperatorname%7BCov%7D%5BX%2CY%5D%3DE%5B%28X-E%5BX%5D%29%28Y-E%5BY%5D%29%5D%3DE%5BXY%5D-E%5BX%5DE%5BY%5D)
We have
![E[XY]=\displaystyle\int_{-\infty}^\infty\int_{-\infty}^\infty xy\,f_{X,Y}(x,y)\,\mathrm dx\,\mathrm dy=\int_0^1\int_0^y2xy\,\mathrm dx\,\mathrm dy=\frac14](https://tex.z-dn.net/?f=E%5BXY%5D%3D%5Cdisplaystyle%5Cint_%7B-%5Cinfty%7D%5E%5Cinfty%5Cint_%7B-%5Cinfty%7D%5E%5Cinfty%20xy%5C%2Cf_%7BX%2CY%7D%28x%2Cy%29%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3D%5Cint_0%5E1%5Cint_0%5Ey2xy%5C%2C%5Cmathrm%20dx%5C%2C%5Cmathrm%20dy%3D%5Cfrac14)
and so
![\operatorname{Cov}[X,Y]=\dfrac14-\dfrac13\dfrac23=\dfrac1{36}](https://tex.z-dn.net/?f=%5Coperatorname%7BCov%7D%5BX%2CY%5D%3D%5Cdfrac14-%5Cdfrac13%5Cdfrac23%3D%5Cdfrac1%7B36%7D)
Finally, the correlation:
![\operatorname{Corr}[X,Y]=\dfrac{\operatorname{Cov}[X,Y]}{\sqrt{V[X]}\sqrt{V[Y]}}=\dfrac{\frac1{36}}{\left(\frac1{3\sqrt2}\right)^2}=\dfrac12](https://tex.z-dn.net/?f=%5Coperatorname%7BCorr%7D%5BX%2CY%5D%3D%5Cdfrac%7B%5Coperatorname%7BCov%7D%5BX%2CY%5D%7D%7B%5Csqrt%7BV%5BX%5D%7D%5Csqrt%7BV%5BY%5D%7D%7D%3D%5Cdfrac%7B%5Cfrac1%7B36%7D%7D%7B%5Cleft%28%5Cfrac1%7B3%5Csqrt2%7D%5Cright%29%5E2%7D%3D%5Cdfrac12)