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The U.S. Bureau of Labor Statistics reports that the average annual expenditure on food and drink for all families is $5700. Ass
1 answer:
Answer:
80.78% of families spend less than $7000 annually on food and drink
Step-by-step explanation:
Problems of normally distributed samples can be solved using the z-score formula.
In a set with mean and standard deviation
, the zscore of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
In this problem, we have that:
What percentage of families spend less than $7000 annually on food and drink?
This is the pvalue of Z when X = 7000. So
has a pvalue of 0.8078
80.78% of families spend less than $7000 annually on food and drink
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Answer:
a) Are dependent since we are mesuring at the same individuals but on different times and with a different method
b) If we see the qq plot we don't have any significant deviation for the values and we don't have any heavy tail so we can conclude that we can approximate the differences with the normal distribution.
c)
So the p value is higher than the significance level given, so then we can conclude that we FAIL to reject the null hypothesis. So we can conclude that the mean differences is NOT significantly different from 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.
The Q-Q plot, or quantile-quantile plot, "is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a Normal or exponential".
Let put some notation
x=value for A , y = value for B
A: 379, 378, 328, 372, 325, 304, 356, 309, 354, 318, 355, 392
B: 372, 376, 328, 368, 283, 252, 369, 321, 379, 303, 328, 411
(a) Are the two samples paired or independent? Explain your answer.
Are dependent since we are mesuring at the same individuals but on different times and with a different method
(b) Make a normal QQ plot of the differences within each pair. Is it reasonable to assume a normal population of differences?
The first step is calculate the difference and we obtain this:
d: 7,2,0,4,42,52,-13,-12,-25,15,27,-19
In order to do the qqplot we can use the following R code:
d<-c(7,2,0,4,42,52,-13,-12,-25,15,27,-19)
qqnorm(d)
And the graph obtained is attached.
If we see the qq plot we don't have any significant deviation for the values and we don't have any heavy tail so we can conclude that we can approximate the differences with the normal distribution.
(c) Choose a test appropriate for the hypotheses above and justify your choice based on your answers to parts (a) and (b). Perform the test by computing a p-value, make a test decision, and state your conclusion in the context of the problem
The system of hypothesis for this case are:
Null hypothesis:
Alternative hypothesis:
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 two tailed test the p value is given by:
So the p value is higher than the significance level given, so then we can conclude that we FAIL to reject the null hypothesis. So we can conclude that the mean differences is NOT significantly different from 0 .
