Measurement Systems Analysis from Coursera

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.

What's inside

Syllabus

Correlation and Association

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.

The One Way Analysis of Variance (ANOVA) for Fixed and Random Effects

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.

Introduction to Measurement Systems Analysis for Continuous Data, Potential Studies for Continuous Data

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.

Short Term and Long Term Studies for Continuous Data

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.

Measurement Systems Analysis for Discrete Data

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).

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores concepts of quality that are quintessential to advanced manufacturing and industrial processes

Uses R as a way to introduce the concepts, making it applicable to real-world scenarios

Offers performance-based admissions, removing barriers for diverse candidates

Suitable for those already working in data science who seek formal academic credit

Candidates who lack foundational knowledge in computer science, information science, or statistics may face challenges

Incorporates principles underlying measurement systems that are applicable to a wide range of industries

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Measurement Systems Analysis with these activities:

Understand the concepts of statistical correlation and association.

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Review the concepts of correlation and association to strengthen your statistical foundation for this course.

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Review the concept of correlation and its different types.
Explore the relationship between correlation and causation.
Understand the difference between linear and nonlinear correlation.

Read the book: Statistical Methods for Quality Assurance

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Gain a comprehensive understanding of statistical methods used in quality assurance, which will support your learning in this course.

View Statistics for Engineering and the Sciences on Amazon

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Read chapters 1-5 to grasp the fundamentals of statistical quality control.
Review chapters 6-10 to delve into specific statistical techniques.
Complete the end-of-chapter exercises to test your understanding.

Complete the online tutorials on Measurement Systems Analysis

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Enhance your practical skills by following guided tutorials on Measurement Systems Analysis, a key topic in this course.

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Follow the tutorials on potential, short-term, and long-term studies for continuous data.
Complete the tutorials on measurement systems analysis for discrete data.
Apply the techniques learned in the tutorials to analyze real-world measurement systems.

Three other activities

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Participate in a peer study group to discuss Measurement Systems Analysis

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Engage with fellow students to share insights and deepen your understanding of Measurement Systems Analysis through discussions.

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Join or form a study group with other students in this course.
Choose a topic related to Measurement Systems Analysis to discuss.
Prepare for the discussion by reviewing course materials and doing additional research.

Create a data visualization of the ANOVA results for a continuous dataset

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Solidify your understanding of ANOVA by creating a data visualization that represents the results of an analysis on a continuous dataset.

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Use appropriate software to perform an ANOVA on a continuous dataset.
Create a visual representation of the ANOVA results, such as a graph or chart.
Interpret the visualization to identify significant differences between groups.

Develop a measurement systems analysis plan for a real-world application

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Apply your knowledge to a practical setting by creating a comprehensive plan for analyzing a measurement system in a real-world scenario.

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Identify the measurement system to be analyzed and define the objectives of the analysis.
Determine the appropriate measurement system analysis techniques to be used.
Develop a detailed plan outlining the steps involved in the analysis.

Career center

Learners who complete Measurement Systems Analysis will develop knowledge and skills that may be useful to these careers:

Reliability Engineer

Reliability Engineers analyze and improve the reliability of products and systems. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to identify and eliminate sources of failure.

See salaries and explore the career path for Reliability Engineer

Process Engineer

Process Engineers design, develop, and improve manufacturing processes to increase efficiency and quality. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to improve processes.

See salaries and explore the career path for Process Engineer

Quality Assurance Manager

Quality Assurance Managers oversee the development and implementation of quality assurance programs to ensure that products and services meet customer requirements. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to evaluate the effectiveness of quality assurance programs.

See salaries and explore the career path for Quality Assurance Manager

Industrial Engineer

Industrial Engineers design, improve, and implement systems that integrate people, materials, and equipment to optimize efficiency. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to improve processes.

See salaries and explore the career path for Industrial Engineer

Manufacturing Engineer

Manufacturing Engineers design, build, and maintain the machines and systems used to produce goods. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to ensure that manufacturing processes are operating within acceptable tolerances.

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Quality Control Analyst

Quality Control Analysts ensure high quality standards in products and services by inspecting, testing, and evaluating components. This course helps build a foundation for this role by teaching measurement error analysis, which is essential for understanding how to adjust measurement systems to improve accuracy and quality.

See salaries and explore the career path for Quality Control Analyst

Statistician

Statisticians collect, analyze, interpret, and present data to help businesses and organizations make informed decisions. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to ensure its accuracy and reliability.

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Data Analyst

Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to ensure its accuracy and reliability.

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Market Researcher

Market Researchers collect, analyze, and interpret data to help businesses understand their customers and make informed decisions about product development and marketing strategies. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to ensure its accuracy and reliability.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to help businesses improve their operations. This course helps build a foundation for this role by teaching measurement systems analysis, which is essential for understanding how to collect and analyze data to identify and eliminate inefficiencies.

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Data Scientist

Data Scientists use mathematical and statistical techniques to extract knowledge from data.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty.

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Economist

Economists study the production, distribution, and consumption of goods and services.

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Biostatistician

Biostatisticians use statistical methods to design, analyze, and interpret biomedical research studies.