Answer:
The diameter of the hemisphere is 20.4 centimeters.
Step-by-step explanation:
The volume of an hemisphere is given by:
In this problem, we have that:
So
![r = \sqrt[3]{\frac{3*2233}{2\pi}}](https://tex.z-dn.net/?f=r%20%3D%20%5Csqrt%5B3%5D%7B%5Cfrac%7B3%2A2233%7D%7B2%5Cpi%7D%7D)
The radius is 10.2 centimeters.
The diameter is double the radius. So:
2*10.2 = 20.4
The diameter of the hemisphere is 20.4 centimeters.
Answer:
sharon
Step-by-step explanation:
Answer:
Cody has solved (12 × 12) = 144 problems.
Step-by-step explanation:
For every one problem that Julia completes, Cody completes twelve.
If Julia Completes x problems and Cody completes y problems, then we can write y = 12x ........ (1)
Now, given that the number of problems solved by Cody is one hundred twenty more than two times the number of problems solved by Julia.
Hence, 2x + 120 = y ......... (2)
Now, from equations (1) and (2) we get,
2x + 120 = 12x
⇒ 10x = 120
⇒ x = 12
Therefore, Cody has solved (12 × 12) = 144 problems. (Answer)
Answer:
Step-by-step explanation:
First find the place that is the thousandth place. That would be the 3 then you will look beside the 3 to see if their is a number bigger than five. If there is then you would add whatever number to make the number 10. In this case their is not so you would instead look at the three and replace all the numbers to the right of three ( don’t change three though) and make them zeros. So that will leave you with 299.2130 rounded to the nearest thousandth.
Answer:
See the proof below.
Step-by-step explanation:
For this case we just need to apply properties of expected value. We know that the estimator is given by:
And we want to proof that 
So we can begin with this:
And we can distribute the expected value into the temrs like this:
And we know that the expected value for the estimator of the variance s is 
, or in other way 
so if we apply this property here we have:
And we know that 
so using this we can take common factor like this:
And then we see that the pooled variance is an unbiased estimator for the population variance when we have two population with the same variance.
