For this case, the first thing we must do is define variables.
We have then:
x: number of cabins
y: number of campers
We now write the equation that models the problem:
We know that there are 148 campers.
Therefore, substituting y = 148 in the given equation we have:
From here, we clear the value of x:
Therefore, the number of full cabins is:
Answer:
The number of full cabins is:
Answer:
c) H0 : p = 5.8%
H1 : p > 5.8%
Step-by-step explanation:
At the null hypothesis, we test that the percentage is equal to a certain value. At the alternate hypothesis, we have a test about this percentage, if it is more, less, or different from the tested value.
A psychologist claims that more than 5.8 percent of the population suffers from professional problems due to extreme shyness
At the null hypothesis, we test if the percentage is 5.8%
At the alternate hypothesis, we test if this percentage is more than 5.8%. So
This means that the correct answer is given by option c.
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