91影视

The annual beer consumptions reported on the Weiss Stats site were based on a random sampling of \(300\) Missouri residents.

MINITAB process:

Step 1: Select Graph> Histogram.

Step 2: Select Easy, then click Beer

Step 3: In the dynamic graphs, enter the corresponding SALARY column.

Step 4: Click OK.

The histogram plainly demonstrates that the distribution isn't bell-shaped. That is, the data distribution is biassed to the right, with an outlier at the extreme.

Furthermore, neither test should be employed because the sample is large and the data distribution is not normally distributed. Some statisticians, however, will utilise the t-test because the population standard deviation is unknown.

Yes, the histogram in section (a) shows the exception. That is, Observation \(110\) is considered an external entity.

The null hypothesis is that each person in the United States consumes \(28.2\) gallons of beer per year on average.

The second hypothesis is:

$H.:渭\#28.2$

That is, in the United States, the average annual beer consumption is not \(28.2\) gallons.

MINITAB procedure:

First, go to Stat > Basic Statistics > \(1\)-From the drop-down menu, select \(t\).

Step 2: From the Samples in Column menu, choose Beer.

Step 3: Type \(28.2\) as the test mean in the Perform hypothesis test box.

Step 4: Change the Confidence Level to \(99\) in Options.

Step 5: Choose not equal instead of equal.

Step 6: Click OK in all dialogue boxes.

The null hypothesis must be rejected if \(Pa\) is true.

Conclusion

The significance level is \(0.003\), thus the \(P\)-value is smaller than that. To put it another way, \(P(=0.003)a (=0.01)\)

As a result, the null hypothesis is rejected at a \(1\%\) level.

As a result, at the \(1\%\) level of significance, the test results might be declared statistically significant.

The data give adequate information to determine that Missouri's mean annual beer consumption per person differs by \(1\%\) from the national average..

MINITAB procedure:

Step 1: Select Stat > Basic Statistics > \(1-\)Sample t from the drop-down menu.

Step 2: Select Beer from the Samples in Column menu.

Step 3: In the Perform hypothesis test box, enter \(28.2\) as the test mean.

Step 4: Go to Options and change the Confidence Level to \(99\).

Step 5: Instead of equal, choose not equal.

Step 6: In all dialogue boxes, click OK.

The significance level is \(0.006\), thus the \(P-\)value is smaller than that. To put it another way, \(P(-0.006)a (=0.01)\)

As a result, the null hypothesis is rejected at a \(1\%\) level.

As a result, at the \(1\%\) level of significance, the test results might be declared statistically significant.

As a result, the data are adequate to establish that Missouri's average yearly beer consumption differs by \(1\%\) from the national average.