Levi-Strauss Co manufactures clothing. The quality control department measures weekly values of different suppliers for the percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up). The data is in table N, and there are some negative values because sometimes the supplier is able to layout the pattern better than the computer ("Waste run up," 2013). Do the data show that there is a difference between some of the suppliers? Test at the 10% level.
Table N: Run-ups for Different Plants Making Levi Strauss Clothing
Plant 1 |
Plant 2 |
Plant 3 |
Plant 4 |
Plant 5 |
1.2 |
16.4 |
12.1 |
11.5 |
24 |
10.1 |
-6 |
9.7 |
10.2 |
-3.7 |
-2 |
-11.6 |
7.4 |
3.8 |
8.2 |
1.5 |
-1.3 |
-2.1 |
8.3 |
9.2 |
-3 |
4 |
10.1 |
6.6 |
-9.3 |
-0.7 |
17 |
4.7 |
10.2 |
8 |
3.2 |
3.8 |
4.6 |
8.8 |
15.8 |
2.7 |
4.3 |
3.9 |
2.7 |
22.3 |
-3.2 |
10.4 |
3.6 |
5.1 |
3.1 |
-1.7 |
4.2 |
9.6 |
11.2 |
16.8 |
2.4 |
8.5 |
9.8 |
5.9 |
11.3 |
0.3 |
6.3 |
6.5 |
13 |
12.3 |
The null hypothesis HO: All five mean percentage differences are equal
Alternate hypothesis HA : At least two of the mean percentage differences are not equal
alpha = 0.1
F = 3.486919
p-value = 0.013116
Since p-value <0.1, we reject the null hypothesis.
The result is statistically significant at 0.1 level of significance. There is enough evidence to support the claim that there is a difference between some of the suppliers.
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