Applied DoE
===========

A worked-solution walkthrough of Kevin Dunn's 12-week Design of Experiments
short course, written for practitioners in industry. Each module is an answer
key for the corresponding weekly worksheet at
`yint.org <https://yint.org/resources>`_, and uses :mod:`process_improve` code
end-to-end.

The series is organized into four phases. Each module page weaves in
facilitator notes as inline **Tip**, **Guidance**, **Check yourself**, and
**Solution** callouts so you can pause, reflect, and test your own
understanding before reading the worked answer.

Before you start
----------------

A short planning template to fill in before any of the modules below.

.. toctree::
   :maxdepth: 1

   00_plan_your_experiment

Phase A. Foundations
--------------------

The two-factor mindset; how factors, levels, and responses connect to a
linear model.

.. toctree::
   :maxdepth: 1

   01_two_factor_mindset
   02_coding_linear_model

Phase B. Full factorial designs
-------------------------------

Scaling up to three and more factors, reading effects and interactions, and
diagnosing the model that comes out.

.. toctree::
   :maxdepth: 1

   03_full_factorials
   04_residuals_blocking

Phase C. Doing less, learning more
----------------------------------

Screening designs when you cannot afford a full factorial, and judging
whether a design is worth running before you spend the budget.

.. toctree::
   :maxdepth: 1

   05_fractional_factorials
   06_power_and_evaluation

Phase D. Optimization
---------------------

Moving from a screening study to an optimum, including response surfaces,
steepest ascent, and balancing multiple responses.

.. toctree::
   :maxdepth: 1

   07_response_surfaces
   08_multiresponse_confirmation

Sources and attribution
-----------------------

Each module is a worked solution to the corresponding weekly worksheet from
Kevin Dunn's *Design of Experiments* short course. The original worksheets
live at https://yint.org/resources. Worksheet questions are paraphrased here;
the canonical wording remains in the source documents. The companion
textbook is `Process Improvement using Data <https://learnche.org/pid>`_
(Chapter 5 covers DoE).