Quality Quiz from Professor Cleary

Congratulations:
"No" is the correct answer!

Click here for a more complete video explanation

Walker Runn was flat-out 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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