Six Sigma Exploratory Data Analysis

Six Sigma Green Belt
  • 13 Videos | 53m 59s
  • Includes Assessment
  • Earns a Badge
  • Certification CPE
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In the Analyze stage of the Six Sigma DMAIC process, project teams carefully analyze process output and input variables. The goal of this data analysis is to narrow down the many possible inputs identified during the Measure stage, and lead to business process improvement. The analysis is carried out using tools that help identify a few probable issues and drive process improvement, process design, and process control. In this course, you'll learn about some of the key tools used in exploratory data analysis in Lean and Lean Six Sigma, such as Multi-varied studies, Correlation analysis, and Regression models. The course is aligned with ASQ’s 2015 Six Sigma Green Belt Body of Knowledge.

WHAT YOU WILL LEARN

  • identify characteristics of a multi-vari analysis
    identify guidelines for creating sampling plans
    distinguish between types of variation
    interpret variation results in a given scenario
    identify uses of correlation analysis in Six Sigma
    make inferences about data based on a given scatter diagram
  • recognize considerations for interpreting the correlation coefficient
    identify characteristics of causation
    identify purpose of determining the statistical significance of a correlation coefficient
    recognize how linear regression is used during data analysis
    recognize appropriate conditions for hypothesis testing
    calculate an outcome using the simple least-squares linear regression formula

IN THIS COURSE

  • Playable
    1. 
    Six Sigma Exploratory Data Analysis
    49s
    UP NEXT
  • Playable
    2. 
    Multi-vari Analysis
    4m 11s
  • Locked
    3. 
    Sampling Plans for Multi-vari Analysis
    4m
  • Locked
    4. 
    Types of Variation
    3m 32s
  • Locked
    5. 
    Interpreting Variation Results
    2m 58s
  • Locked
    6. 
    Correlation Analysis
    2m 29s
  • Locked
    7. 
    Using Scatter Diagrams for Correlation Analysis
    2m 56s
  • Locked
    8. 
    Correlation Coefficient
    5m 19s
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    9. 
    Causation
    2m 52s
  • Locked
    10. 
    Testing Statistical Significance
    2m 24s
  • Locked
    11. 
    Using Linear Regression
    2m 25s
  • Locked
    12. 
    Hypothesis Testing for Regression Statistics
    1m 39s
  • Locked
    13. 
    Using Regression Analysis to Predict Outcomes
    1m 23s

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