Design of
Experiments.
The method itself, from the vocabulary up to response surfaces. Start at the top if the subject is new to you, or jump to the design you are about to run.
- DoE 01
What is Design of Experiments? (DoE Basics)
What a designed experiment is, and why it gets you more information from fewer runs than changing one factor at a time.
- DoE 02
Key DoE Terms
The words you need before you can read a design: factor, level, response, run, effect, and the rest.
- DoE 03
Principles of DoE: Randomization, Replication, Blocking
The three habits that make experimental data worth trusting: randomize the order, repeat the runs, block out what you cannot control.
- DoE 04
Main Effects & Interaction Effects Explained
How a factor moves the result on its own, and how two factors together can do something neither of them does alone.
- DoE 05
The Basics of Designing Better Experiments
Why changing one factor at a time hides the interactions, and how covering the design space finds them in fewer runs.
- DoE 06
A step by step example of a full factorial design
A full factorial worked through on real filtration data, from the run sheet to the main effect and interaction plots.
- DoE 06A
Create a Full Factorial Design in Python
Build a 2^k design with pyDOE3: name your factors, map coded levels to real units, randomize the order, export the run sheet.
- DoE 07
Introducing Fractional & Central Composite Designs
When a full factorial gets too big: fractional designs to screen many factors, central composite designs to optimize a few.
- DoE 08
Example of a Fractional Factorial Design
A fractional factorial worked through end to end: which runs you drop, what you give up, and what you can still conclude.
- DoE 08A
Create a Fractional Factorial Design in Python
Build a fractional design in pyDOE3, choose the resolution, and see which effects end up confounded with which.
- DoE 09
Mathematical Models in DOE
Turning a table of runs into an equation that predicts the response, and knowing how far you can trust it.
- DoE 09A
How to perform ANOVA
The test that tells you which factors really moved the response, and which ones only look like they did.
- DoE 09AB
Understanding the ANOVA Table Output
What every column in the ANOVA table means, and which numbers you actually read to decide what matters.
- DoE 09B
Testing ANOVA Assumptions
ANOVA only holds if your residuals behave. What to check, what it looks like when it fails, and what to do then.
- DoE 09BA
QQ-Plots Explained
How to read a QQ-plot, and what the common shapes tell you about whether your residuals are normal enough.
- DoE 09BB
Residual Analysis
What is left over once the model has done its work, and why those leftovers tell you whether to believe it.
- DoE 09C
How to Perform ANOVA with Python
Run ANOVA in Python with statsmodels: fit the model, drop the dead terms, add curvature, check the diagnostics.
- DoE 10
A step by step example of a Central Composite Design (CCD)
A central composite design worked through step by step, including how it picks up the curvature a factorial misses.
- DoE 10A
Create a Central Composite Design in Python
Build a central composite design in Python, set the axial distance, and get a run sheet you can take to the bench.
- DoE 11
Central Composite Design vs. Box-Behnken Design
Two response surface designs side by side. What each one costs in runs, and when to pick one over the other.
- DoE 11A
How to create a Box-Behnken Design in Python
Build a Box-Behnken design in pyDOE3, and see how it finds curvature with fewer runs than a central composite.
- DoE 12
A full factorial design in Python from beginning to end
One dataset, start to finish in Python: build the design, plot the effects, run ANOVA, check the model, draw the surface.
- DoE 13
Understanding Systematic and Random Errors
The difference between error that shifts every result the same way and error that just scatters them, and what each costs you.
- DoE 14
Why Blocking Matters
An epoxy coating experiment where batch-to-batch variation buried the real effect, and what blocking would have saved.
- DoE 15
Response Surface Methodology (RSM)
How a run of small designs walks you toward better process conditions instead of guessing where the optimum sits.
- DoE 16
The Path of Steepest Ascent
Once a model points uphill, this is how far you walk along it and where you stop to run the next set of experiments.
- DoE 17
Why You Should Always Code Your Variables
Coefficients in raw units cannot be compared. Coding puts every factor on the same scale so the model can be read.
- DoE 18
Blocking in Practice: How to Actually Do It
How a block is actually built into a design through confounding, with blocked and unblocked versions of one experiment.
- DoE 19
Active Effects: Not Everything Matters Equally
Most factors do nothing. Effect sparsity and effect hierarchy explain why, and how to tell the live ones from the dead.
- DoE 20
Foldover Designs
A second, mirrored set of runs that breaks the confounding in a fractional design, shown on an effect that misled.
- DoE 21
All-Subset Regression
Rather than dropping terms one at a time, fit every possible combination and pick the best model. How it compares to ANOVA.