Running with Scissors

Presenter: Paul Sheehy, Principal, Paul Sheehy Consulting, Brewer, ME, USA

Keywords: Statistics, Assumptions, Common Errors

Industry: Education/Training, Manufacturing, Service

Level: Intermediate


ABSTRACT

Give people a powerful tool (statistics) with insufficient training, and you likely have a case of somebody “running with scissors.” This talk will cover some of the more common statistical mistakes including:

  • Misapplication of the normality assumption and normality testing with a discussion of which statistical tools or methods are sensitive to the assumption, which are moderately affected and which are robust to it. Additionally, a method to validate normality with large samples will be demonstrated.
  • Questions regarding the equal variance assumption for the 2-Sample t test and One-Way ANOVA. This will include the use of the Welch’s test.
  • Criticality of rational subgroups in control charting and capability.
  • In measurement systems, the issues arising from the use of the small number of parts in a typical Gage R&R study to estimate the variation of the process which, if unverified, may result in a seriously incorrect denominator when calculating the % gage R&R. There will be a discussion on the value of using a historical standard deviation as a check.
  • Issues that can seriously impact the ability of a Capability Analysis to predict the future performance of the process. Discussion will include the effects due to insufficient data, insufficient time, subgroup problems, and lack of stability. The concept of a Capability Snapshot will be presented.


Conference & Exhibitor Registration will start on
October 9, 2018

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Conference Chair's Message

Laura Martin-Samala

Continuous Process Improvement is not just something a Lean Six Sigma (LSS) practitioner helps organizations achieve, but is also a fundamental core competency of their personal make up. That is why  innovative companies are looking for people that use the LSS way of thinking at every level. In this age of “Big Data”, “IOT” and “AI”, ... read more