Answer:
Now we can calculate the p value with this probability:
If we use a significance level os 0.05 we see that the p value is lower than the significance level so then we can conclude that the true proportion of students with jobs is higher than 0.35 for this case. If we decrease the significance level to 1% the result changes otherwise not.
Step-by-step explanation:
Information given
n=78 represent the random sample taken
X=36 represent the students with jobs
estimated proportion of students with jobs
is the value that we want to test
z would represent the statistic
represent the p value
Hypothesis to test
We want to test if the proportion of students with jobs is higher than 0.35, the system of hypothesis are:
Null hypothesis:
Alternative hypothesis:
The statistic is given by:
(1)
Replacing the info we got:
Now we can calculate the p value with this probability:
If we use a significance level os 0.05 we see that the p value is lower than the significance level so then we can conclude that the true proportion of students with jobs is higher than 0.35 for this case. If we decrease the significance level to 1% the result changes otherwise not.
Answer:
The answer to your question is the third histogram
Step-by-step explanation:
What we must check in a histogram is that the x-axis is represented the intervals and in the y-axis is represented the frequency.
The first histogram is incorrect just by observing the first bar, we notice that the correct frequency from 0 to 4 is 3, not 14. This histogram is incorrect.
Also, the second histogram is incorrect, the frequency of the first category is 3, not 12. This histogram is wrong.
The third histogram is correct because all the bars are in agreement with their frequencies.
The last histogram is incorrect, for example, the last frequency is 12, not 3.
You can’t see it l don’t know
It is 45... Because you add all the numbers together then divide it by the number of numbers their are
9.296 × 10 1, 1 is the exponet