Introduction to Statistics and Probability in Six Sigma

Since Six Sigma is well-recognized as a methodology which is more concerned about measuring quality, it is no longer surprising to see professionals and experts in implementing this methodology who are more focused towards using statistics and probability. Both statistics and probability are extremely important in the field of implementing Six Sigma since these are mainly used in effectively analyzing and interpreting data. If you plan to become an expert Six Sigma practitioner, then it is advisable for you to look for a Six Sigma certification program which covers substantial information about the most effective statistical techniques at present as well as the most basic and complex probability concepts and their individual meanings. This will allow you to know exactly how you can implement the methodology. Highly reputable online certification companies like Brainmeasures can be expected to provide you with a higher level of learning about the field so you have an assurance that you can easily transform yourself into a real expert in this industry.

What are the Most Effective Statistical and Probability Techniques in the Field of Six Sigma?

If you are planning to get to know more about statistics and probability and their major roles in the field of Six Sigma, then be aware that there are a few tools and techniques that you can use to perform tests that are capable of showing results effective in interpreting the present performance of a business organization. The following are just few of these:

- Probability Plotting. This can be defined as a graphical technique which you can use in making comparisons for 2 sets of data. The comparison may be conducted based on two sets of empirical observations through which one set is against a single theoretical set. Conducting the comparison based on two theoretical sets that tend to operate against one another is also possible. One of the many things that you should remember about making probability plots in the field of Six Sigma is that these are extremely useful when it comes to analyzing and displaying lifetime data. An inverse distribution scale is also utilized in this technique.
- Control Chart. The use of this tool in Six Sigma statistics and probability requires you to understand that certain deviations that are part of acceptable lower and upper limits are triggered by numerous factors. Anything which tends to fall beyond a specific limit can actually be attributed to a specific cause. When using control charts for statistics, be aware that there is such thing as common cause. This refers to the inherent variation which causes reasonable results to be produced within specific limits. Special causes are also in existence and these are triggered by forced errors. With the help of a control chart, you get the chance to analyze these causes through its mathematical mean line usually found on 2 limit lines and at the center. It should also be noted that one of the major components of the control chart for Six Sigma is performance data or information which you need to plot over time.
- Affinity Diagram. It was first designed by Kawakita Jiro and works by emphasizing the extreme need to sort and provide titles to data. The most useful affinity diagram is capable of organizing certain ideas that result from brainstorming on the left part of its panel. On the right part, affinity sets that represent ideas are grouped neatly. While there are no specific reasons why specific ideas belong to particular sets, the diagram is still useful in clarifying all these. You can expect ideas that have similarities to be set in one group.
- General Linear Model (GNL). This technique also uses a variety of models that are now used in statistics and probability within the field of Six Sigma including ordinary linear regression, F-test, t-test, ANOVA and MANCOVA. It can be defined as the generalization of multiple models designed for linear regression. This model is extremely useful when trying to acquire statistical information in the Six Sigma field since it gives you the chance to successfully complete your Six Sigma projects.
- Regression Analysis. This refers to a technique in the field of statistics which is a huge help in accurately estimating the relationships between variables. It takes advantage of various modeling techniques that are also useful in analyzing multiple variables especially if these are mainly focused on the established relationship between a single dependent variable and more than one independent variable.

Importance of Statistics and Probability in Six Sigma

Statistics and probability are two of the most powerful aspects that would lead all Sigma practitioners towards success. The reason behind this is that the use of the best probability and statistical tools and techniques will be their ticket towards understanding even the tiniest and the most complex details linked to a specific business organization. Both statistics and probability will allow you to gather information that are not only useful in assessing the performance of an organization but also in ensuring that your desired quality in the aspects of manufacturing and production are achieved.