Design of Experiments & Advanced Statistics for Engineers

Modern experimental design and advanced statistics — factorial, fractional, and response-surface designs, ANOVA and regression, variance components, advanced SPC.

Private cohorts & on-site
Format: 3-day (8:30 a.m.–4:30 p.m.)
Level: Advanced
Location: Scheduled on demand · on-site at your facility or a regional venue
Date(s): Not yet scheduled for open enrollment. Get notified when it is, or book it privately for your team.
Includes: Certificate of Completion · printed slide binder · take-home reference text

Get notified when this course is scheduled

One email when dates are set. Or skip the wait: run it as a private cohort, on-site at your plant.

Talk to us about a private cohort
  • One email, no sequence
  • Never shared
  • Reply within one business day

Faculty

Faculty details for this seminar will be announced with the full schedule.

Fees

Early: $1,895 (payment 4+ weeks ahead)
Standard: $2,095 (check/ACH) · $2,165 (card)

Group discount: $200 off per attendee for 3+ from the same organization.

Also Available

  • Corporate on-site delivery at your facility
  • Private cohort sessions
  • Digital curriculum licensing

Seminar Overview

Engineers collect mountains of data and learn little from it — because the experiments and analyses are ad hoc. Building on ETS's applied-statistics course and the quantitative depth of the MIT OpenCourseWare statistics corpus, this seminar teaches modern experimental design and advanced statistical methods: fractional factorial and response-surface designs, regression and ANOVA, variance components, and statistical process control in depth. The result is engineers who prove their claims with designed experiments instead of anecdotes.

Ideal Learner

  • Process and manufacturing engineers optimizing processes
  • R&D engineers designing product and material experiments
  • Quality engineers driving DOE and SPC programs
  • Reliability and validation engineers analyzing test data
  • Engineers needing to defend quantitative claims

Learning Objectives

  • Design experiments that isolate factor effects with minimal runs
  • Analyze factorial and response-surface designs with ANOVA and regression
  • Quantify variance components and measurement-system error
  • Apply advanced SPC for process stability and improvement
  • Draw defensible, statistically sound engineering conclusions

Consulting Sessions

Seminar attendees can sign up for individual consulting sessions with the instructor. Sessions are free for registered attendees, first-come first-served — sign up when registering by calling 248-539-0473 or during the seminar.

Seminar Outline

  • Correlation vs. causation, confounding, and lurking variables
  • Power, sample size, and effect size
  • The cost of a bad experiment
  • Full vs. fractional factorial design
  • Aliasing and resolution
  • Screening designs for many factors
  • CCD and Box-Behnken designs
  • Fitting and interpreting response surfaces
  • Finding optimum regions with confidence
  • Analysis of variance, contrasts, and interactions
  • Regression model building and validation
  • Multiple comparisons done correctly
  • Measuring measurement-system variability
  • Variance component estimation
  • Stable process and measurement quality
  • Control chart selection and interpretation
  • Capability analysis in depth
  • From control to continuous improvement
  • Attendees design and analyze a real experiment
  • Isolate factors, build a surface, draw conclusions
  • Present the statistically defensible result

More in Track G — Computational & Quantitative Engineering

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