It will take 22.5 min cause 25 divided by 100 wuld be 4 wich then divide 90 with
Answer:
0.2377 or about a 23.77% chance
Step-by-step explanation:
P(123<X<130) = normalcdf(123,130,130,11) = 0.2377303466 ≈ 0.2377
Therefore, the probability that the next game Victoria bowls, her score will be between 123 and 130 is 0.2377 or about a 23.77% chance
Answer:
There are no solutions.
Step-by-step explanation:
First simplify the equation.
<u>6(x - 2)</u> = 8 + 6x
Distribute.
6x - 12 = 8 + 6x
Adding 8 to a number and subtracting 12 from the same number to get the same answer doesn't make sense so there are no solutions.
Answer:
a) 

b) From the central limit theorem we know that the distribution for the sample mean
is given by:
c)
Step-by-step explanation:
Let X the random variable the represent the scores for the test analyzed. We know that:

And we select a sample size of 64.
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Part a
For this case the mean and standard error for the sample mean would be given by:


Part b
From the central limit theorem we know that the distribution for the sample mean
is given by:
Part c
For this case we want this probability:

And we can use the z score defined as:

And using this we got:
And using a calculator, excel or the normal standard table we have that:
Q1: 7×6 = 42
Q2: 42×1/8 = 42/8 = 5.25 (5 1/4) in^3
Q3: (0.5)^3 = 0.5×0.5×0.5 = 0.125 m^3
Q4: 4(5/2)^2 = 4×5/2×5/2 = 4×25/4 = 25 in^3
Q5: 3(13/2)(3/2) = 117/4 = 29.25 (29 1/4) in^3
Q6: (0.9)^3 = 0.729 cm^3