IEMS 307: Quality Improvement by Experimental Design

Quarter Offered

Spring : TTH 9:30-10:50 ; Ankenman


IEMS 201 or IEMS 303 or equivalent


Methods for designing and analyzing industrial experiments.  Blocking; randomization; multiple regression; factorial and fractional factorial experiments; response surface methodology; Taguchi’s robust design; split plot experimentation. Homework, labs, exams, and a project.

  • This course is a major requirement for MaDE and an IE/OR Technical Elective for IEMS


  • Students will be able to design, conduct, and analyze comparison experiments and make engineering decisions based on the results.
  • Students will be able to design, conduct, and analyze factorial and fractional factorial experiments and make engineering decisions that account for estimation uncertainty and confounding of main effects and various interaction effects.
  • Students will be able to follow a process of experimental optimization to optimize a design or the settings of a process.
  • Students will be able to design and analyze experiments that have multiple levels of variability.


  • Some Quality History and Scientific Context (Chapter 1)
  • Brief Introduction to SPC (Not in Textbook)
  • Comparing Two Treatments (Blocking and Randomization) (Sections 3.1-3.4)
  • Empirical Models and Regression Analysis (Chapter 10 and Minitab)
  • Factorial Experiments, Main Effects and Interactions (Sections 5.1-5.15)
  • Model Building. (Chapter 10 and Minitab)
  • Aliasing and Confounding, Fractional Factorial Analysis (Sections 6.1-6.8)
  • Fold-Over Designs (Section 6.8-6.10)
  • 4- and 8- level factors (Blocking: Sections 5.16 and 6.16)
  • Response Surface Methods (Chapter 11)
  • Data Transformations and Multiple Responses (Chapter 8)
  • Robustness and Split Plot Experiments (Chapter 13)


Required Text: “Statistics for Experimenters: Design, Innovation and Discovery,” Second Edition:  by George E. P. Box, J. Stuart Hunter, and William G. Hunter, Wiley. ISBN: 978-0-471-71813-0.