Vol. 10, No. 7

July 2008

PQ Systems
 
Contents

Waiting to hear from you

Quality Quiz: With a video!

Data in everyday life

Six Sigma

Bytes and pieces

FYI: Current releases

 

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Software

 

   

Quality Quiz from Professor Cleary

Congratulations:
"B" is correct.

Click here for a more complete video explanation

Clara Nett was flat-out wrong. She had confused the sum of X-bars with the alpha value.  To learn about alpha values and hypothesis testing, keep reading.

Data from the production lines was as follows:

Book example

Line 1   Line 2
 
 

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 if 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 (H o) is that Line 1 and Line 2 are not significantly different from one another.

Step 2:

Alpha value = .05

Interpretation: An alpha value of 5 percent 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:

Step 4:

Make decision:

a) Look up tabular t value in a statistics textbook. In this case, the tabular value is equal to 2.71.

b) Compare the value of 4.4 from Step 3 to 2.71. 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.

 

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