CQAS Seminars

 

Advanced Regression Methods for Six Sigma

 

If you already use regression methods, this seminar is for you.

In this hands-on, three-day seminar you will learn about pitfalls and challenges in applying linear and non-linear regression. You will learn how to use advanced regression techniques in a wide variety of contexts from designed experiments to observational studies, especially those performed in Six Sigma projects. You will then practice those skills in problem solving sessions and learn how to apply them using MINITAB® computer software. In addition, you will learn about statistical resources available on the Internet. This course provides 2.1 CEUs.

Prior knowledge of basic regression techniques (see Implementation of Six Sigma with Regression Methods), and basic familiarity with statistical software (i.e., MINITAB®) is recommended.

 

Topics include:

• Dummy variables in regression
• Best model selection
• Ill-conditioning in regression data
• Ridge regression
• The geometry of least squares
• Non-linear regression
• Case studies in the use of advanced regression methods

 

How you will benefit:

• Improving your performance in the "Analyze" and "Improve" phases of your Six Sigma projects by using advanced regression methods
• Understanding ill-conditioning of your data and ways to overcome it
• Getting intimate insight into regression by understanding its geometry
• Learning how you can use non-linear regression
• Utilizing statistical resources available on the Internet

 

Cost

 

$895. This price includes a copy of The Manual for Statistical Resources on the Internet, written by the instructor. Continental breakfast and lunch is included each day of the seminar.

 

Meet your instructor

Peter Bajorski is a Ph.D. statistician with 20 years of experience in consulting, research, and teaching. He is currently a faculty member at CQAS. Prior to joining RIT, he held positions at Cornell University, the University of British Columbia, and Simon Fraser University. He was also an Associate Statistician at the Engineering Research and Development Bureau, New York State Department of Transportation.

Peter is a Six Sigma Black Belt and is familiar with the Lean Enterprise approach to process improvement. In addition to his expertise in process improvement methods, Peter's statistical focus is in regression techniques, multivariate analysis, design of experiments, and nonparametric methods. He also has done statistical work in reliability, imaging science, transportation, health services, quality assurance and material engineering, civil engineering, and industrial engineering. Peter has authored five short-course workbooks.