### The Sigma Shift Explained | Quality Digest

Design for Six Sigma (DFSS) Sigma or Z-value to me is a composite capability estimate useful for management updates Try to draw what represent Z and what cpk/ppk and find the relations. Therefore, should this measuring system really be something that we use to make sound business decisions?. Some Six Sigma practitioners are concerned about the current method used to Because a 50 percent error-free yield does not equal a Z-score of zero, the range In addition to the asymmetry in the measurement system, there are questions . Design for Six Sigma ResearchWhen, where and why companies use DFSS. The lessons are designed to serve as an "organizer" for the coming material. Standard scores express the difference between a value and the mean of the control scores is the z-score, which is used on some recent QC analytical systems. the mean (µ or mu) and standard deviation (s or sigma) of a large population.

Average - the most common statistic The simplest term is the average or mean, which is commonly used by almost everyone in our society. Given a series of numbers that show some fluctuation, or variation, the best description of what has been observed is the average of those numbers.

An example in the laboratory is the calculation of the average for a group of measurements on a control material. The average is the best estimate of the value that is to be expected on continued measurements. It is also your best bet, if you were asked to guess a number! Standard deviation - a common laboratory statistic The next term is the standard deviation, or SD, which is familiar to most laboratorians. The SD is determined by analyzing many aliquots of a control material at least 20calculating the mean by adding the values of all of these and dividing by N here Then the mean value is subtracted from each of these values control value - mean and this latter value is squared and summed?

This value is divided by or Then the square root is taken. This "value - mean" value minus the mean term will be a recurring theme in statistics.

The calculation of SD will be more fully explained in the coming lessons. See also QC-The Calculations. Probabilities or p's - where statistical discomfort often begins The third term is probabilities. Said another way, there is a 0.

Said another way, there is only a 0. Probabilities as they relate to the normal curve distribution will be covered in a future lesson.

## Quantitative Application of Sigma Metrics in Medical Biochemistry

In its original definition, SD was described in concentration units, i. Standard scores express the difference between a value and the mean of the control material as a multiple of the standard deviation, i. The most universal of all the standard scores is the z-score, which is used on some recent QC analytical systems.

This z-score may cause confusion, especially for analysts who have been around the laboratory for a long time. One of the very practical uses of a z-score is to compare different distributions, so you do not have to be concerned about any particular mean or SD value. Suppose you were analyzing two different control materials and wanted to compare results on consecutive measurements.

Now instead of comparing each control value with the mean of that control, you can take the difference of each control from its own mean and then divide by its standard deviation to tell how far each value is from its respective mean. A z-value of 2. Notice here that the z produced is a decimal fraction. And also notice that a z of 2. If the z-value on the next observation on the other control material were 2. The cost, in terms of the amount of inspection and the amount of computation, of sampling four or five is less than that of sampling larger numbers.

The smaller sample averages will mask the effect of causes less than larger samples will. Of the two, a sample of five makes it easier to calculate sample average, while a sample size of four makes it easier to calculate.

### Z An Organizer Of Statistical Terms (Part I) - Westgard

X-bar, R charts are the distribution-independent because of the application of the central limit theorem, which states that sample averages tend to follow a normal distribution irrespective of the distribution of the population.

Measure of dispersion among means is sometimes known as the "standard error of estimate. In other words, sample-to-sample variation will be less. Without computers standard deviations were prone to calculation errors, so the tolerance limits were determined using sample range values.

The typical formula for determining control limits is: Ken Geckho Participant Ok, one thing that I know for sure is that if I want to measure something to make good decisions about it, one of the first steps is to validate my ability to measure it, right? Then, we distribute the collection of data to a randomly selected group of Black Belts heck, we could even select them from the same large corporation, if you like and ask them to calculate the Sigma values from it.

My only point is that I would be willing to bet that this little MSA would score quite poorly. Not by any stretch. Even when practitioners have the same rules, they often have them for different reasons.

How is a new Black or Green Belt not to mention manager supposed to make any sense of it?

**Z-Scores and Percentiles: Crash Course Statistics #18**

The answer is the best that they can, which usually results in even more creative interpretations added to the pile of confusion. The Sigma measure supports each opportunity in a unit of work, not the entire product or service system.