Try a one-way ANOVA test.
You will need to calculate Sum of Squares Within and Sum of Squares Between. You should have formulas to calculate these values.
Sum of Squares Total = Sum of Squares Within + Sum of Squares Between
To calculate Degrees of Freedom Between:
k - 1
Note: k = number of levels.
To calculate Degrees of Freedom Within:
N - k
Note: N = total number of values in all levels.
Degrees of Freedom Total = Degrees of Freedom Between + Degrees of Freedom Within
To calculate Mean Squares Between:
Sum of Squares Between divided by Degrees of Freedom Between
To calculate Mean Squares Within:
Sum of Squares Within divided by Degrees of Freedom Within
To calculate F-ratio:
Mean Squares Between divided by Mean Squares Within
You can set up a table with these values if asked to do so.
If the F-ratio exceeds the value from an F-table, then the null is rejected and you can conclude a difference.
If the F-ratio does not exceed the value from an F-table, then the null cannot be rejected and you cannot conclude a difference.
I hope this will help get you started.
A consumer research organization is attempting to determine whether there is any difference in mpg for fully loaded 22-foot trucks leased from three companies, A-Haul, Bertz, and Glyder. Five of these trucks are rented from each company. Each truck is driven with the same weight cargo over the same 200 mile route and the mpg recorded. The results of the test are: A-Haul 3.4 4.2 5.1 4.9 3.1 Bertz 5.1 2.0 8.7 6.7 6.1 Glyder 7.9 8.5 5.2 8.0 8.1 Perform the hypothesis test: H0 : 1 = 2 = 3 (Average mpg is the same for all three rental companies) H1 : Not all of the mean mpg is the same for the three companies An = 0.05 is used by the consumer research organization. Is there any difference in mean mpg? Find the F statistic value and state whether you reject the Null or fail to reject the Null hypothesis.
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