Experimental design
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Design principles
- Randomization, replication, and blocking
- Completely randomized designs
- Randomized complete block designs
- Latin squares and crossover designs (introduction)
- Factorial experiments: main effects and interactions
Analysis of variance
- One-way ANOVA: decomposition and F-tests
- Two-way ANOVA with and without interaction
- Multiple comparisons: Tukey, Bonferroni
- Fixed vs random effects (introduction)
- Split-plot and nested designs (overview)
Advanced design topics
- Response surface methodology (introduction)
- Fractional factorial designs
- Confounding and aliasing in 2^k designs
- Optimal design criteria (D- and A-optimality intro)
STEM / applied
Industrial and lab applications
- Taguchi methods overview (optional)
- Design of experiments in manufacturing quality
- Analysis with JMP, Minitab, or R
- Pilot studies and adaptive designs (introduction)
- Reproducibility and protocol documentation
- Case studies from engineering and agriculture
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Covers classical DOE topics found in statistics and agriculture/engineering programs. Applied sections include industrial and lab-based case studies.