(k + 3)by the power of 3
1 answer:
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Answer:
The question is incomplete, the complete question is Angela makes a pillow in the shape of a wedge to use for watching TV. The pillow is filled with 12 ft³ of fluffy material. The base is 3 ft, the height is 2 ft, what is the length of the pillow?
The length of the pillow is 4 feet
Step-by-step explanation:
The formula of the volume of the wedge is , where
- b is the base of it
- h is the height of it
- l is the length of it
∵ The pillow in the shape of a wedge
∵ The pillow is filled with 12 ft³ of fluffy material
∴ The volume of the wedge = 12 ft³
∵ The base = 3 feet
∵ The height = 2 feet
- Use the formula of the volume above to find its length
∵
∴ V = 3 l
∵ V = 12 ft³
- Equate 3 l by 12
∴ 3 l = 12
- Divide both sides by 3
∴ l = 4 feet
∴ The length of the pillow is 4 feet
Answer:
Step-by-step explanation:
Let A the random variable that represent "The arrival time of the plumber ". And we know that the distribution of A is given by:
And let B the random variable that represent "The time required to fix the broken faucet". And we know the distribution of B, given by:
Supposing that the two times are independent, find the expected value and the variance of the time at which the plumber completes the project.
So we are interested on the expected value of A+B, like this
Since the two random variables are assumed independent, then we have this
So we can find the individual expected values for each distribution and then we can add it.
For ths uniform distribution the expected value is given by where X is the random variable, and a,b represent the limits for the distribution. If we apply this for our case we got:
The expected value for the exponential distirbution is given by :
If we use the substitution we have this:
Where X represent the random variable and the parameter. If we apply this formula to our case we got:
We can convert this into hours and we got E(B) =0.5 hours, and then we can find:
And in order to find the variance for the random variable A+B we can find the individual variances:
We have the following property:
Since we have independnet variable the Cov(A,B)=0, so then: