Confidence Level Question

try this website. scroll down to the bottom of the page and you will have the answer to your question.
http://www.sixsigmafirst.com/sampling.htm
Akram-Al Qiram.

To Darth’s point, there are some differences between discrete and continuous data but here is a little more information on population sampling that you may find useful.
Precision required in estimate?  This measures how accurate you want the estimate to be (ex. if we are measuring loan processing time, we may want our estimate to be within ± 1 day).
Amount of variation in characteristic?  This measures the current variation of the set of data (ex. we know from past measurements that the variation in the processing time, as measured by standard deviation, is 6 days).
Confidence level? This measures the confidence that we want the estimate to be in the specified accuracy (precision).  This is most often represented at a 95% confidence and is a constant number of 2 in the sampling formula. 
Sample Size? The sample size is calculated from the following formula: n = (2s/∆)2  n=sample size, 2=constant based on confidence, s=standard deviation, and ∆=degree of precision required.

In our example, the required sample size is 144
                        n = (2×6/1)2 = 144

Yeah…not sure what is going on with the font there.
Kris

Good points Darth and not too picky nor insulting at all.  I agree that it is 1.96, just chose to round it up as I don’t think it makes that much of a statistical difference. 
I did forget to mention that the formula was for continuous however, so good catch.
Kris Brazeal

When determining precision in sample size calculation for continuous data, are there any mathmatical rules of thumb?  I would think one would view this relative to the std dev in the process…any thoughts?  Thanks. 

First of all, forget about normal data if you are talking about most anything in a call center.  Time, which is the most common metric will be non normal because of the natural boundary of zero on one side.  No problem dealing with it but the statistics based on a normal distribution will likely not apply.  Furthermore, data dealing with categorized or discrete data will also not be normal since you are either measuring percentage or counts, both of which again, have a natural boundary of zero or 100% if percentages.
The “s.d.” for the discrete sample size calculations is totally different than for the continuous.  Since you are doing proportion of calls into categories, it is the s.d of a proportion (binomial) that is relevant.  The following formula is what is used.  A value of .5 is used for the p if you have no idea of the true proportion.  This gives you the largest, worst case sample size.
n = (z/delta)squared times p(1-p)

I work with a customer support call center, in a company that is just beginning (2 Yrs) to venture down Six Sigma, Lean has been around a while in our operations group.
Ops applications are much more similar to the typical training in Lean/Six Sigma, so we on the Serve side are attempting to make the transition to understand how the different techniques and tools work in a service environment.
Having access to people who have dealt with the transition in the past will be very helpful.
John

Abul,Thanks for your reference to my earlier response. To insure my guidance is accurately conveyed to this thread I would like to make a few adjustments to your suggestions.Requirements for Statistical Sampling Determination: You need the reference variation from the control group or the best estimate of such, you need the desired confidence for the claim, you need to know what practical difference you want to detect, you also need to know the desired power of the test, you will need to know whether the test is two or one-sided, and lastly you want to select samples from the population that are representative of that population.Larger samples are warranted as the desired power and confidence increase for fixed variation and practical difference, or for a fixed variation-power-confidence the diffrence to detect decreases.Some examples sampling using continuous measures:Given – One-Sided Test of Unpaird Means
Power = 90%
Confidence = 95%
Variation = 1 SDDifference to Detect N1 & N2
————————————–
0.5 86
1.0 22
1.5 11
2.0 7Given – One-Sided Test of Paired Means
Power = 90%
Confidence = 95%
Variation = 1 SDDifference to Detect N1 & N2
————————————–
0.5 37
1.0 11
1.5 6
2.0 4Given – Two-Sided Test of Unpaird Means
Power = 90%
Confidence = 95%
Variation = 1 SDDifference to Detect N1 & N2
————————————–
0.5 70
1.0 19
1.5 9
2.0 6Given – Two-Sided Test of Unpaird Means
Power = 90%
Confidence = 95%
Variation = 3 SDDifference to Detect N1 & N2
————————————–
0.5 758
1.0 191
1.5 86
2.0 49Given – Two-Sided Test of Unpaird Means
Power = 80%
Confidence = 95%
Variation = 3 SDDifference to Detect N1 & N2
————————————–
0.5 567
1.0 143
1.5 64
2.0 37Typical values for confidence and power for conservative studies are: Power = 90%, and Confidence = 95%.Typical values for confidence and power for exploratory studies are: Power >= 80%, and Confidence >=90%.= 80%, and Confidence >=90%.= 80%, and Confidence >=90%.=90%.=90%.Ken

Thanks Holly,
I’ve posted many responses over the past few weeks on the subject of sample selection, or what some call the “sampling frame.”  In his day, Deming was especially insistent on insuring the sampling frame properly matched the problem or question at hand.
As you suggest, selecting a representative sample is good insurance against biased results.  While selecting the right number of samples is key to insuring statistical validity, so is selecting a representative sample.
Rather than provide answers in this posting, I would like to see how others on the forum achieve this critical element that links sample selection to process understanding. 
What methods do you use to insure the samples you select are representative of the system at large?  What problems have you encountered in past work where improper sampling, (not including the improper size), provided you misunderstanding in the opreation of a process?
Regards,
Ken

Hi Holly,
I wasn’t sure from your last note if you wanted to have a discussion, or you had a specific problem.  I’m trying hard not to give the others all of the answers, and allow them to participate.
It’s difficult for me to help you with such limited data and information.  The best I can do is give you some suggestions and ideas.  No guarantee doing this from the US.  I suppose you are in Asia, possibly China?  If so, your English is very good!
Some Questions:
1.  Did the problem occur before the shut-down?
2. If it occurred after the shut-down, get the maintenance logs and find out what was changed, repaired, or corrected, during the shut-down.
3. If problem occurred before the shut-down, then go to #4.
4. If problem occurred before the shut-down, or the maintenance investigation did not yield anything interesting, then collect equal samples by line and/or machine, however your production system is configured.
5. Assess the number of defects per unit, or the number of defective units by line or machine–try to identify which lines or machine provide the greatest contribution.
6. Narrow your focus to the selected machines within the key cause line that have the greatest chance of producing the defect.
7. Determine through observation whether the likely cause of the defect is due to mistakes in setting up and operating the machine, or due to the machine drifting during operation, or the settings for the machine are wrong.
8. If mistakes in set-up and operation are the cause of problem, then try to redesign the set-up/operation process so the operator cannot produce the mistake. (Mistake Proofing)
9. If machine operation settings are wrong, run small tests to find the better settings.  Reset equipment and track operation via a control chart.
10.  If machine is drifting during operation, then have maintenance repair the machine.
Just a few ideas.  I will be heading to bed in 30 minutes, but available until then if you have additional questions.
Ken