Quality Quiz from Professor Cleary
Congratulations:
"No" is the correct answer!
Click here for a more complete video explanation
Walker Runn was flatout wrong. He had confused
the sum of the two sample sizes with a Type 1 error. To learn about
alpha values and hypothesis testing, keep reading.
Data from the production lines was as follows:
The production on Line 2 is clearly less than that
of Line 1. The question remains whether the difference is due to
natural variation or it can be ascribed to the two lines actually
operating differently. Using traditional hypothesis testing, one
can apply the "t test":
Step 1:
Interpretation: The null hypothesis (H0)
is that Line 1 and Line 2 are not significantly different.
Step 2:
Alpha value
Interpretation: An alpha value of .05
suggests a willingness to accept a 5 percent chance of rejecting
the null when it is actually true. This is known as a Type I error.
Step 3:
Calculate statistical t value.
t= 2.834
Step 4:
Make decision.
a) Look up tabular t value
in a statistics textbook. In this case, it is equal to 2.22814 (Note:
you have 10 degrees of freedom: +
2).
b) Compare the calculated t value of 2.834 from Step 3
to the tabular t value of 2.22814. If it is greater, reject.
If not, accept.
Interpretation: In this case, the mean
values are different enough from each other that one would conclude
that Lines 1 and 2 are indeed different from one another.
If this exercise brings back dark memories
of a statistics course and its innumerable calculations, welcome
to the new technology. Our DOEpack
will do the work for you.
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