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  • Specialization:ÌýData Science Methods for Quality Improvement
  • Instructor:ÌýWendy Martin,ÌýInstructor,ÌýW. Edwards Deming Professor of Management
  • Prior knowledge needed:ÌýDTSA 574, DTSA 5705

Learning OutcomesÌý

  • Assess a discrete measurement.Ìý
  • Perform analyzes for potential and long term control and capability.Ìý
  • Make decisions on measurement systems process improvement.

Course Content

Duration: 2h

In this module, we will learn to identify, characterize and analyze relationships between two variables. We will first learn about the correlation between two continuous variables and tests for significance. Next, we will learn about correlation for ordinal variables, and association for one nominal and one continuous variable. Finally, we will learn to assess the relationship for two nominal variables.

Duration: 5h

In this module, we will perform an Analysis of Variance for Fixed and Random Effects for a single factor and interpret results. We will first examine within versus between-group variation, and interpret the ANOVA source table. We will learn how to perform the ANOVA with Fixed Effects for means and dispersion, considering normality and equal/unequal variance. We'll create data visualizations of results, calculate statistical importance and perform post hoc analysis. Finally, we'll perform the ANOVA with Random Effects.

Duration: 3h

In this module, we will understand the terms and concepts associated with measurement systems analysis and analyze measurement error to determine the potential capability of a measurement system. We will explore the guidelines for measurement systems analyses and the equations for measurement error and capability. We will then calculate the sources of variation from the ANOVA determine the largest sources of variation, and determine capability in comparison to both process variation and specification tolerance. Finally, we'll create data visualizations, and interpret the results of the analysis.

Duration: 4h

In this module, we will analyze measurement error to determine the short and long-term capability of a measurement system. We will build on what we have learned in the previous module, adding the evaluation of the underlying assumptions of normality, independence of part size/magnitude and measurement error, and stability of measurement error. We'll perform an ANOVA to determine sources of variation along with the determination of gauge discrimination. Finally, we'll create data visualizations, and interpret the results of the analysis.

Duration: 1h 30m

You will complete a multiple choice exam worth 20% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

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