PLZ HELP NOOOOW!!
1 answer:
Hey there! The answer is 1 1/8 mile.
To find the amount of miles Sarah ran, we need to multipy the length of the track by the times she run that distance.
Rembember: multiplying a fraction means you have to multiply the <span>numerator by the numerator (of the other fraction) and the denominator by the denominator (of the other fraction)
</span>The answer is 1 1/8 mile.
~ Hope this helps you!
To find the amount of miles Sarah ran, we need to multipy the length of the track by the times she run that distance.
Rembember: multiplying a fraction means you have to multiply the <span>numerator by the numerator (of the other fraction) and the denominator by the denominator (of the other fraction)
</span>The answer is 1 1/8 mile.
~ Hope this helps you!
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Answer:
The p value on this case is given by the problem.
If we compare the p value with a significance level assumed , we see that
and we can conclude that we FAIL to reject the null hypothesis that the difference mean between after and before is less or equal than 0.
Step-by-step explanation:
A paired t-test is used to compare two population means where you have two samples in which observations in one sample can be paired with observations in the other sample. For example if we have Before-and-after observations (This problem) we can use it.
Let put some notation
x=test value before , y = test value after
The system of hypothesis for this case are:
Null hypothesis:
Alternative hypothesis:
The first step is calculate the difference
The second step is calculate the mean difference
The third step would be calculate the standard deviation for the differences, and we got:
The 4 step is calculate the statistic given by :
The next step is calculate the degrees of freedom given by:
Now we can calculate the p value, since we have a right tailed test the p value is given by:
The p value on this case is given by the problem.
If we compare the p value with a significance level assumed , we see that
and we can conclude that we FAIL to reject the null hypothesis that the difference mean between after and before is less or equal than 0.