Mar 13, 2025  
2023-2024 Undergraduate Academic Catalog-June Update 
    
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2023-2024 Undergraduate Academic Catalog-June Update [ARCHIVED CATALOG]

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IND 3410 - Design of Experiments and Data Analysis for Engineers

3 lecture hours 0 lab hours 3 credits
Course Description
This course covers the basic approaches for statistically designed experiments and enhanced decision making via the use of analysis of variance, regression, and data aggregation techniques.  Students will utilize these techniques, as well as statistical software, in conjunction with the design of experiments process to solve open ended problems as part of hands-on exercises. (prereq: IND 2030 ; MTH 2480  for non-IE majors) (quarter system prereq: IE 2030)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • 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
  • Solid ability to graphically analyze data
  • Conduct hypothesis testing
  • Statistically analyze and evaluate data using probability distributions and make a strongly defendable statistical and practical decision based upon the testing
Course Topics
  • 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
  • Regression, inferences, and diagnostics (including multicollinearity)
Coordinator
Dr. Douglas Grabenstetter


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