A=(1/2)(h)(B+b)
(6)=(1/2)(h)((2)+(1))
12=h(3)
h=4
Answer: h=4
Answer:
D. 29.75
Step-by-step explanation:
To determine the mean, you must add up all the numbers and then divide that number by how many numbers you added.
Here,
21 + 23 + 35 + 40
= 119
You added 4 numbers (21, 23, 35, 40),
so, 119 / 4
= 29.75
Hope this helps and a thank won't hurt! :)
Answer:
The probability that the sample proportion is between 0.35 and 0.5 is 0.7895
Step-by-step explanation:
To calculate the probability that the sample proportion is between 0.35 and 0.5 we need to know the z-scores of the sample proportions 0.35 and 0.5.
z-score of the sample proportion is calculated as
z=
where
- p(s) is the sample proportion of first time customers
- p is the proportion of first time customers based on historical data
For the sample proportion 0.35:
z(0.35)=
≈ -1.035
For the sample proportion 0.5:
z(0.5)=
≈ 1.553
The probabilities for z of being smaller than these z-scores are:
P(z<z(0.35))= 0.1503
P(z<z(0.5))= 0.9398
Then the probability that the sample proportion is between 0.35 and 0.5 is
P(z(0.35)<z<z(0.5))= 0.9398 - 0.1503 =0.7895
Answer:
a
Step-by-step explanation:
to be a function, every x has to have exactly one y, so there can't be the same number x twice wirh different y's
Answer:
> a<-rnorm(20,50,6)
> a
[1] 51.72213 53.09989 59.89221 32.44023 47.59386 33.59892 47.26718 55.61510 47.95505 48.19296 54.46905
[12] 45.78072 57.30045 57.91624 50.83297 52.61790 62.07713 53.75661 49.34651 53.01501
Then we can find the mean and the standard deviation with the following formulas:
> mean(a)
[1] 50.72451
> sqrt(var(a))
[1] 7.470221
Step-by-step explanation:
For this case first we need to create the sample of size 20 for the following distribution:

And we can use the following code: rnorm(20,50,6) and we got this output:
> a<-rnorm(20,50,6)
> a
[1] 51.72213 53.09989 59.89221 32.44023 47.59386 33.59892 47.26718 55.61510 47.95505 48.19296 54.46905
[12] 45.78072 57.30045 57.91624 50.83297 52.61790 62.07713 53.75661 49.34651 53.01501
Then we can find the mean and the standard deviation with the following formulas:
> mean(a)
[1] 50.72451
> sqrt(var(a))
[1] 7.470221