Please update your browser's security settings.
To maintain the highest security standards, we will be disabling support for browsers using TLS 1.0. Learn more and upgrade your browser here.


Diagnostic Practices Program

The Diagnostic Practices program will endow the participant with the knowledge and insights necessary to judiciously plan and successfully execute a diagnostic study. Participants will learn how to fully characterize the statistical performance of a process and identify the dominant families of variation.

In many instances, the simple application of a few diagnostic tools can often preclude the need for exhaustive experimentation. Of course, such an action has the potential to shorten the total time it takes to execute an improvement project.

Students will discover a selected array of powerful analytical and statistical tools that are essential for isolating critical sources of variation related to process centering and spread. Major emphasis is given to the methods and techniques for statistically analyzing, describing, and displaying performance data – for virtually all types of products, processes, services and transactions.

In particular, the participant will learn how to select the right variables and parameters for inclusion in a factorial experiment. Participants will learn how to establish operating tolerances for almost any type of product, process or service.

Of special interest, the participant will learn the theory and application of common sampling methods as well as how to draw valid conclusions and make statistical inferences from a sampling distribution. In support of this, the participant will also learn how to draw such conclusions with known degrees of statistical risk and confidence.

Of course, the critical tools and concepts associated with statistical hypothesis testing is thoroughly discussed and then related to the use of diagnostic tools, design-of-experiments, and statistical process control methods. Related to this instructional goal, the participant will also be taught how to construct statistical hypotheses and then how to test those hypotheses using well established methods, such as the common t-test, analysis-of-variance, and regression, just to mention a few.

However, when the assumptions underlying the use of parametric tools can not be reasonably satisfied, the practitioner sometimes finds it necessary to employ nonparametric methods, or so called "distribution free" methods. To this end, the participant will learn how to employ such tools as the median test (and sign test) to evaluate a relatively diverse range of statistical hypotheses.

The knowledge gained from this portion of the curriculum is paramount to the effective use of performance metrics and indices of process capability. Reinforcement of the major techniques and applications is realized through exercises, scenarios, and case studies.

Diagnostic Practices
$50.00 USD

Request This Program