Quality Engineers Network Meetings For September 2020

A Message from  Paul Mathews
Here are the QMN and QEN meeting announcements for the September meetings. The links provided are registration links. After you register for the meeting (so that I can keep track of attendance) you will be e-mailed the actual meeting link.

Quality Managers Network Meeting (QMN)
Configuring MINITAB’s Stat> ANOVA> General Linear Model Menu
7:30-9:00AM, 4 September 2020, by Zoom ( register for the meeting)
One of the most powerful tools that MINITAB provides for data analysis is its Stat> ANOVA> General Linear Model menu. This menu can be used to analyze a quantitative response as a function of one or more predictor variables in a wide variety of experiment designs. MINITAB does offer some menus to analyze some simple experiments like Stat> ANOVA> One-Way; however, the Stat> ANOVA> General Linear Model menu is not that much more difficult to configure and its capabilities encompass those of all of the other methods and much, much more. My take on this is that I’d rather learn to use one very powerful tool to do the vast majority of my work than have to learn the intricacies of many simple tools. So this month we will discuss how to configure Stat> ANOVA> General Linear Model for the following issues:
  • Selecting one or more responses
  • Specifying ANOVA for one or more qualitative variables
  • Specifying regression for one or more quantitative variables
  • Adding two-factor and higher order interactions to the model
  • Adding quadratic and higher order terms to the model
  • Specifying random (versus fixed) variables in the model
  • Obtaining standard deviation estimates for random effects
  • Specifying nested variables
  • Testing assumptions of the analysis method
  • Specifying a transformation to the response
  • Studying the results of the analysis
  • Making predictions from the fitted model
QEN Meeting hosted by Geauga Growth Partnership
Variable Transformations for Nonnormal Data
7:30-9:00AM, 11 September 2020, by Zoom (register for the meeting)
I recently heard from a customer who was struggling to analyze and present data which had a number of outliers with large values in the data set. She was trying to make the case that those observations were different from the others so that they could be omitted from the analysis; however, after omitting them and reanalyzing the data there were even more observations that looked like outliers. This turned out to be the classic case of the need for a variable transform – specifically a log transform. This is a common occurrence in statistical analysis – that a response requires a transformation – so the method is used in inferential methods, gage studies, process capability studies, designed experiments, statistical process control, and many other situations. And with some practice and experience you can even learn to recognize when a transformation will be required and which transformation will do the job given the fundamental first principles of the process that produced the response.
Geauga News
Author: Geauga News