Answer:
70cm
Step-by-step explanation:
,
Since, both of the tank posses the same quantity of water then there volume is the same thing
For rectangular base
Volume= (Lenght × breadth × hheight)
If we substitute values
Lenght= 80 cm
Breadth= 70 cm
height= 45 cm
Volume= 80cm × 70cm × 45cm
Volume= 252000cm^3
For square base
Volume= (side)^2 × height
side =60cm.
But volume of square base= volume of
rectangular base
252000cm^3= 60^2 × height
Height= (252000cm^3)/(60^2 )
Height= 70 cm
Hence, the second tank is 70 cm deep
Answer:
see explanation
Step-by-step explanation:
Given
10 = 7 - m ( subtract 3 from both sides )
3 = - m ( multiply both sides by - 1 )
- 3 = m , or
m = - 3
As a check
Substitute m = - 3 into the right side of the equation and if equal to the left side then it is the solution
7 - m = 7 - (- 3) = 7 + 3 = 10 = left side
Thus m = - 3 is the solution to the equation
Answer:
If the question is fix the sentence than here:
Step-by-step explanation:
A fish that has been dried in the sun lasts longer than a wet fish.
Answer:
The equation of the line would be y = -2x + 3
Step-by-step explanation:
In order to find the equation of the line, start by using point-slope form with the known information.
y - y1 = m(x -x1)
y - 1 = -2(x - 1)
Now that we have this, solve for y.
y - 1 = -2x + 2
y = -2x + 3
Answer:
![E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}](https://tex.z-dn.net/?f=E%28X%29%3D%20n%20%5Cint_%7B0%7D%5E1%20x%5En%20dx%20%3D%20n%20%5B%5Cfrac%7B1%7D%7Bn%2B1%7D-%20%5Cfrac%7B0%7D%7Bn%2B1%7D%5D%3D%5Cfrac%7Bn%7D%7Bn%2B1%7D)
Step-by-step explanation:
A uniform distribution, "sometimes also known as a rectangular distribution, is a distribution that has constant probability".
We need to take in count that our random variable just take values between 0 and 1 since is uniform distribution (0,1). The maximum of the finite set of elements in (0,1) needs to be present in (0,1).
If we select a value 
we want this:
And we can express this like that:
for each possible i
We assume that the random variable 
are independent and 
from the definition of an uniform random variable between 0 and 1. So we can find the cumulative distribution like this:
And then cumulative distribution would be expressed like this:
For each value 
we can find the dendity function like this:
So then we have the pdf defined, and given by:
and 0 for other case
And now we can find the expected value for the random variable X like this:
