See the attached figure to better understand the problem
let
L-----> length side of the cuboid
W----> width side of the cuboid
H----> height of the cuboid
we know that
One edge of the cuboid has length 2 cm-----> <span>I'll assume it's L
so
L=2 cm
[volume of a cuboid]=L*W*H-----> 2*W*H
40=2*W*H------> 20=W*H-------> H=20/W------> equation 1
[surface area of a cuboid]=2*[L*W+L*H+W*H]----->2*[2*W+2*H+W*H]
100=</span>2*[2*W+2*H+W*H]---> 50=2*W+2*H+W*H-----> equation 2
substitute 1 in 2
50=2*W+2*[20/W]+W*[20/W]----> 50=2w+(40/W)+20
multiply by W all expresion
50W=2W²+40+20W------> 2W²-30W+40=0
using a graph tool------> to resolve the second order equation
see the attached figure
the solutions are
13.52 cm x 1.48 cm
so the dimensions of the cuboid are
2 cm x 13.52 cm x 1.48 cm
or
2 cm x 1.48 cm x 13.52 cm
<span>Find the length of a diagonal of the cuboid
</span>diagonal=√[(W²+L²+H²)]------> √[(1.48²+2²+13.52²)]-----> 13.75 cm
the answer is the length of a diagonal of the cuboid is 13.75 cm
For me personally, the easiest way to do this is by isolating the x² term, and finding the square root of both sides. The hardest way (well actually, the longest way) would be to use the quadratic formula. It just complicates things unnecessarily.
The probability that Rachel will win the game is: 1/12
Step-by-step explanation:
The number cubes has six sides numbered between 1 to 6. the chances of each number are equally likely
Let S be the sample space
The sample space has 6*6 = 36 outcomes.
Now, Let A be the event that the sum of numbers on both number cubes is 10
A = {(4,6),(5,5), (6,4)
n(A) = 3

The probability that Rachel will win the game is: 1/12
Keywords: Probability, Sample
Learn more about probability at:
#LearnwithBrainly
Answer:
<h3>
The option B) is correct.</h3><h3>
That is the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible is correct answer</h3>
Step-by-step explanation:
Given that " The least-squares regression line "
The least-squares regression line is <u>the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.</u>
Therefore option B) is correct