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Measurement Systems Analysis

Measurement Systems Analysis

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Gain insight into a topic and learn the fundamentals.
4.2

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Intermediate level

Recommended experience

Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
Build toward a degree

Gain insight into a topic and learn the fundamentals.
4.2

10 reviews

Intermediate level

Recommended experience

Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
Build toward a degree

What you'll learn

  • Understand the terms and concepts associated with measurement systems analysis

  • Analyze measurement error to determine the potential capability of a measurement system

  • Analyze measurement error to determine the short-term and long-term capability of a measurement system

  • Analyze a measurement system for discrete data using potential, short-term, and long-term studies

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Assessments

7 assignments

Taught in English

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This course is part of the Data Science Methods for Quality Improvement Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability. Additionally, you will learn how to assess a discrete measurement and perform analyses for internal consistency, concordance between assessors, and concordance with a standard. Finally, you will learn how to make decisions on measurement systems process improvement.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

In this module, we will learn to identify, characterize and analyze relationships between two variables. We will first learn about 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 relationship for two nominal variables.

What's included

9 videos3 readings1 assignment2 discussion prompts

9 videosβ€’Total 39 minutes
  • Introduction to Correlation and Associationβ€’2 minutes
  • The Coefficient of Correlationβ€’3 minutes
  • Calculating the Coefficient of Correlationβ€’3 minutes
  • The One Sample t Test for Correlation β€’6 minutes
  • Fisher’s Z Test for Correlationβ€’4 minutes
  • Spearman’s Rank-Order Correlationβ€’4 minutes
  • The Point Biserial Correlationβ€’5 minutes
  • Measures of Associationβ€’6 minutes
  • Calculating Measures of Associationβ€’5 minutes
3 readingsβ€’Total 21 minutes
  • Course Updates and Accessibility Supportβ€’1 minute
  • Earn Academic Credit for your Work!β€’10 minutes
  • Course Supportβ€’10 minutes
1 assignmentβ€’Total 60 minutes
  • Correlation and Associationβ€’60 minutes
2 discussion promptsβ€’Total 20 minutes
  • Introduce Yourself!β€’10 minutes
  • Correlation and Associationβ€’10 minutes

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.

What's included

15 videos2 assignments1 discussion prompt

15 videosβ€’Total 76 minutes
  • Introduction to the Analysis of Varianceβ€’4 minutes
  • ANOVA Principlesβ€’4 minutes
  • One Way ANOVA for Meansβ€’6 minutes
  • The ANOVA Source Tableβ€’5 minutes
  • Perform an ANOVA in RStudioβ€’4 minutes
  • Welch's ANOVA for Unequal Varianceβ€’2 minutes
  • Data Visualization for ANOVAβ€’5 minutes
  • Statistical Importanceβ€’6 minutes
  • One Way ANOVA for Dispersionβ€’6 minutes
  • Post Hoc Analysis - Tukey HSDβ€’6 minutes
  • Post Hoc Analysis - Games and Howellβ€’4 minutes
  • Roadmap for the One Way ANOVAβ€’6 minutes
  • Roadmap for the One Way ANOVAβ€’8 minutes
  • One Way ANOVA - Random Effectsβ€’5 minutes
  • One Way ANOVA - Random Effectsβ€’5 minutes
2 assignmentsβ€’Total 240 minutes
  • One Way ANOVA for Fixed Effectsβ€’120 minutes
  • One Way ANOVA for Random Effectsβ€’120 minutes
1 discussion promptβ€’Total 10 minutes
  • One Way ANOVA for Fixed and Random Effectsβ€’10 minutes

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.

What's included

11 videos1 assignment1 discussion prompt

11 videosβ€’Total 60 minutes
  • Introduction to Continuous MSAβ€’5 minutes
  • Measurement Systems Capabilityβ€’4 minutes
  • Guidelines for Continuous MSAβ€’6 minutes
  • Sources of Variationβ€’5 minutes
  • Two Way Random Effects ANOVAβ€’5 minutes
  • Estimating Variance from Mean Squaresβ€’5 minutes
  • The Components of Variance β€’7 minutes
  • Study Variation and Discriminationβ€’6 minutes
  • Data Visualizationsβ€’7 minutes
  • Data Visualizationsβ€’5 minutes
  • Interpreting Results β€’4 minutes
1 assignmentβ€’Total 90 minutes
  • Measurement Systems Analysis and Potential Studiesβ€’90 minutes
1 discussion promptβ€’Total 10 minutes
  • Measurement Systems Analysis and Potential Studiesβ€’10 minutes

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.

What's included

7 videos2 assignments1 discussion prompt

7 videosβ€’Total 33 minutes
  • Short Term Measurement Systems Analysisβ€’4 minutes
  • Testing Assumptions, Evaluating Controlβ€’6 minutes
  • Measurement Error versus Part Sizeβ€’5 minutes
  • Short Term Analysisβ€’5 minutes
  • Long Term Measurement Systems Analysisβ€’4 minutes
  • Evaluating Controlβ€’5 minutes
  • Measurement Error versus Part Sizeβ€’4 minutes
2 assignmentsβ€’Total 180 minutes
  • Short Term Measurement Systems Analysisβ€’90 minutes
  • Long Term Measurement Systems Analysisβ€’90 minutes
1 discussion promptβ€’Total 10 minutes
  • Short and Long Term Measurement Systems Analysisβ€’10 minutes

In this module, we will analyze a discrete measurement system to determine agreement, consistency, and validity. We will first familiarize ourselves with the terms, definitions, and procedures associated with Discrete Measurement Systems Analysis. Next, we will explore the measurement of agreement using the Kappa statistic and the measure of disagreement using the test of symmetry. We will then learn to perform analyses for concordance with two appraisers and two categories, two appraisers more than two categories, and more than two appraisers. We will analyze appraisers for internal consistency. Finally, we'll assess validity (concordance with a standard).

What's included

14 videos1 assignment1 discussion prompt

14 videosβ€’Total 67 minutes
  • Discrete MSA Terms and Definitionsβ€’4 minutes
  • Agreement vs Disagreementβ€’4 minutes
  • The Kappa Statisticβ€’5 minutes
  • Kappa vs Kappa Maxβ€’4 minutes
  • Interpreting Kappa Maxβ€’6 minutes
  • Discrete MSA Procedure Part 1β€’4 minutes
  • Discrete MSA Procedure Part 2β€’4 minutes
  • Concordance - 2 Appraisers, 2 Categoriesβ€’6 minutes
  • Internal Consistencyβ€’5 minutes
  • Concordance - More Than Two Categoriesβ€’4 minutes
  • Post Hoc Analysis - More Than Two Categoriesβ€’6 minutes
  • Internal Consistency - More Than Two Categoriesβ€’3 minutes
  • Validity - Agrement with a Standardβ€’6 minutes
  • More Than 2 Appraisersβ€’5 minutes
1 assignmentβ€’Total 90 minutes
  • Discrete Measurement Systems Analysisβ€’90 minutes
1 discussion promptβ€’Total 10 minutes
  • Discrete Measurement Systems Analysisβ€’10 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.ΒΉ

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University of Colorado Boulder
6 Coursesβ€’12,736 learners

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