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Home  /  Six Sigma Study Guide Articles  /  Author: Ted Hessing

Author: Ted Hessing

I originally created SixSigmaStudyGuide.com to help me prepare for my own Black belt exams. Overtime I've grown the site to help tens of thousands of Six Sigma belt candidates prepare for their Green Belt & Black Belt exams. Go here to learn how to pass your Six Sigma exam the 1st time through!
Ted Hessing

Multiple Linear Regression

Posted by Ted Hessing

Multiple linear regression is an extension to methodology of simple linear regression. It is used to study more than two variables.

This entry was posted in Analyze, Multiple Regression Analysis and tagged ASQ, Black Belt, IASSC, QuestionSet. Bookmark the permalink.
Ted Hessing

Non-Linear Regression, aka Attributes Data Analysis

Posted by Ted Hessing

A Non-Linear Regression is a statistical analysis tool used to model complex datasets and express them as mathematical functions.

This entry was posted in Analyze, Multiple Regression Analysis and tagged ASQ, Black Belt, Green Belt, IASSC. Bookmark the permalink.
Ted Hessing

Data Sampling Techniques & Uses

Posted by Ted Hessing

Data Sampling is the selection of statistical samples from the population to estimate the characteristics of entire population.

This entry was posted in Data Collection, Measure and tagged ASQ, Black Belt, Green Belt, IASSC, QuestionSet, Villanova. Bookmark the permalink.
Ted Hessing

Graphical Analysis

Posted by Ted Hessing

Graphical analysis is one of the critical aspects of Six Sigma projects. It is an effective way to visualize data patterns.

This entry was posted in Analyze, Graphical Analysis, Measure and tagged ASQ, Black Belt, Green Belt, IASSC, Villanova, Yellow Belt. Bookmark the permalink.
Ted Hessing

Kano Model

Posted by Ted Hessing

The Kano model is a framework designed to prioritize features based on the degree to which they will satisfy or delighted the users.

This entry was posted in Customer Requirements, Define and tagged ASQ, Black Belt, Villanova. Bookmark the permalink.
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