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.