Apr 19, 2024  
2023-2024 Graduate Academic Catalog 
    
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2023-2024 Graduate Academic Catalog [ARCHIVED CATALOG]

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IND 6130 - Quality Engineering

3 lecture hours 0 lab hours 3 credits
Course Description
This course covers techniques that help firms improve both products and processes. The basic approaches for statistically designed experiments and enhanced decision-making via the use of analysis of variance, regressive and data aggregation techniques. Techniques such as Lean, Six Sigma, analysis of means, ANOVA, linear regression, simple comparative and factorial designs, and randomized blocking designs will be covered. (prereq: IND 2030 or equivalent)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Understand Lean and Six Sigma
  • Recognize the various experimental and analytical approaches discussed in this course
  • Differentiate between the various experimental and analytical methodologies discussed in this course and apply the correct approach for a given situation
  • Successfully design statistically valid experiments
  • Successfully evaluate and analyze the output data resulting from the experimental design and analytical techniques discussed in this course
  • Plan and conduct a designed experiment
  • Analyze experimental data, draw conclusions, and make recommendations regarding process improvements
Prerequisites by Topic
  • Probability and statistics
Course Topics
  • Overview of Lean and Six Sigma
  • Experimental process
  • Factor brainstorming and rank sorting
  • Randomization
  • Hypothesis testing (two sample use in experimentation)–includes pretesting
  • One way analysis of means–includes pre and post testing
  • Applications of factorial designs
  • Randomized complete block design (single factor)
  • 2k full factorial designs
  • General linear model (two-way ANOVA)
  • Main effects and interaction plots
  • Fractional factorial designs, aliasing, and confounding
  • Blocking and use of center points
  • Basic response optimization
  • Correlation
  • Simple linear regression, inferences, and diagnostics
  • Multiple linear regression, inferences, and diagnostics (including multicollinearity)
Coordinator
Dr. Douglas Grabenstetter


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