The Quality Tools course will provide the participant with an array of classical quality improvement methods and diagnostic tools commonly associated with such initiatives as Six Sigma, Lean Sigma, TQM 8D and other fine process improvement programs.

Specifically the participant will learn how to establish basic cause-and-effect relationships, solve on-going operational problems and discover how to enhance or otherwise improve daily operations.

Furthermore, the participant will learn how data can be collected and graphically analyzed to track down unwanted sources of product and process variation; thereby reducing product defects, process cycle time and operational costs.

These time-proven methods and tools can serve as the backbone for virtually any type if quality improvement effort. Naturally, the tools and methods presented in this course can serve as an excellent entry point for a Six Sigma X-Belt program or be linked to other initiatives such as lean manufacturing to form a Lean Sigma initiative. Such an integrated approach can often represent a viable alternative for a commercial enterprise or small business that is dedicated to increasing customer satisfaction and business performance.

The Basic Statistics course will provide the participant with the knowledge and skills necessary to statistically characterize virtually any set or array of data. This course represents the first step into the world of applied statistics and therefore the underpinning principles and practices contained within are vital to realizing a higher level of analytical power.

Students will organize a set of data for subsequent statistical analysis using descriptive statistics and match that data to a common distribution such as the normal curve. In addition, candidates will learn how to define the central value of that data distribution, characterize the inherent variability associated with that distribution and estimate the probability of any given value or point of interest. Participants will then be taught how to report the related statistics and descriptive findings in a simple and comprehensible manner.

Of course such knowledge and skills are essential for the successful execution of process capability studies and product characterization efforts. Furthermore, this course will prepare a participant to undertake the study and application of such tools as Applied Diagnostic Methods (ADM), Statistical Process Control (SPC) and Design of Experiments (DOE).

In this context, it is easy to see why Basic Statistics is the backbone of so many modern process improvement tools, methods and practices. For all intents and purposes, this course is essential to the career development goals of anyone involved in the field of business improvement.

In each of the above courses there is a reinforcement of major concepts, techniques and application is realized through exercises, scenarios and case studies. By way of these training methods the participant will gain tremendous insight into the logic and reasoning which underlies Six Sigma and the process of breakthrough improvement.

The Advanced Statistics course is aimed at providing the participant with the "next level" of statistical tools required to fully exploit the benefits offered by Statistical Process Control.

The following subjects are covered:

* Continuous Capability - Learn how to compute, interpret, interrelate and report the primary indices of capability that rely on continuous data, such as Cp, Cpk, Z.st, Z.lt, just to mention a few..

*Discrete Capability - Learn how to compute, interpret, interrelate and report the primary indices of capability that rely on discrete data, such Rolled Throughput Yield (Y.rt), Defects-Per-Million-Opportunities (DPMO) an Defects-Per-Unit (DPU)

*Hypothesis Testing - Will provide the participant with the knowledge and skills necessary to translate practical problems into statistical questions that are suitable for analytical investigation.

*Confidence Intervals - Learn how to compute, interpret, interrelate and report statistical confidence intervals for virtually any application involving the use of continuous or discrete data, such as the confidence intervals that would embody the true mean and variance of a continuous product performance characteristic or the confidence intervals around a given defect rate.

*Control Methods - Learn how to identify, plan, construct, implement and interpret SPC charts for continuous and discrete performance characteristics. Naturally such charts are related to industrial and commercial products, services and transactions.

*Parametric Methods - Parametric methods represent a class of statistical tools that are mathematical procedures for testing statistical hypothesizes, often related to the mean and variance.

*Chi-Square Methods - Any statistical hypothesis test in which the test statistic has a chi-square distribution, given that the null hypothesis is true. This test statistic is often employed to determine the underlying distribution of a product or service performance variable or estimate the extent of association between one categorical variable and another. It is also a foundational statistic when conducting survey-based investigations and research, such as customer satisfaction analyses.

*Survey Methods - Methods used to collect numerical information about people's opinions or otherwise assemble certain pieces of factual information (in quantitive form)

*Non-Parametric Methods - This branch of mathematical statistics is concerned with statistical models and tests that are not dependant upon the type or nature of the underlying distribution. Non Parametric models differ from Parametric models in that the model is not specified before-the-fact, but is instead determined from data. Non Parametric models are also called "distribution free statistics".

*Experimental Methods - Learn to "screen" a large group of variables so as to discover the "vital few" contributors, understand how to segregate sources of nonrandom error from random error, identify and analyze variable interactions, maximize the mean of a performance variable while concurrently reducing the variance and establish realistic performance specifications and apply tolerances for products and processes.

*Design for Six Sigma (DFSS) Methods - Learn the basics of Quality Function Deployment (QFD) and the role this methodology plays in the design process or quality improvement initiative.