Figures for Process Improvement using Data

This repository holds the figures used in the book Process Improvement using Data by Kevin Dunn, along with the source scripts and editable artwork that produce them. It is a companion to the book's text repository, kgdunn/pid-book, and to the accompanying Python package, kgdunn/process-improve.

The figures live in their own repository, rather than inside the book, so that the same image library can be shared across the book, slide decks, and other teaching material without duplicating large binary files. The book checks this repository out as a symbolic link named figures/, and its reStructuredText source then refers to images by paths such as figures/doe/COST-contours.png.

What is in here

Roughly 2000 rendered images (PNG, JPG, and SVG), together with the code and design files that generate them:

• Rendered output: .png, .jpg, and .svg files. These are the images embedded in the book.
• Generating scripts: .py (matplotlib), .R, and .m (MATLAB) files that reproduce a plot from data. Where a figure is data-driven, its script sits next to it under the same name, for example doe/COST-contours.py produces doe/COST-contours.png.
• Editable artwork: .svg vector sources for hand-drawn diagrams, .pxm (Pixelmator) raster sources, and .xmind files for the mind maps.
• Small supporting data: a few .csv and .dat files used by the scripts.

How the repository is organised

Files are grouped into topic folders, not one folder per book chapter. A single chapter usually draws on several topic folders, and some folders (for example doe/ and least-squares/) also supply figures to other courses and talks. The main folders are:

Folder	Contents
univariate/	Univariate statistics: distributions, confidence intervals, control-chart precursors
visualization/	Data visualization: time-series, scatter and bubble plots, tables
least-squares/	Least squares and regression: fits, residuals, leverage, ANOVA
doe/	Design and analysis of experiments: factorials, fractional factorials, response surfaces, optimal / DSD / OMARS designs
pca/, pls/, multiblock/	Latent-variable modelling: PCA, PLS, and multiblock methods
batch/	Batch process data analysis
monitoring/, process-control/	Process monitoring and control charts
new-products/, image/, concepts/	Product development, image analysis, and general concept diagrams
examples/, teaching/, mindmaps/	Worked examples, teaching aids, and chapter mind maps

Other folders (separations/, reactors/, econ-prob-solve/, classification/, svm/, and so on) support related teaching material and are not all used by the book.

Naming

File names are descriptive and hyphenated, for example monitoring/CO2-phaseI-first-round.png. When a figure has several variants, the variant is appended to the stem, for example doe/COST-contours.png and doe/COST-contours-no-markers.png.

Where each book chapter's figures live

The table below lists the primary folder each chapter draws from, with one example figure from that chapter.

Univariate review

Primary folder: univariate/

Data visualization

Primary folder: visualization/

Least squares modelling

Primary folder: least-squares/

Design and analysis of experiments

Primary folder: doe/

Latent variable modelling

Primary folders: pca/, pls/, multiblock/

Process monitoring

Primary folders: monitoring/, process-control/

Product development using latent variables

Primary folders: image/, new-products/, concepts/

Regenerating a figure

Data-driven figures are regenerated by running the script next to the image. For example:

cd doe
python COST-contours.py         # writes COST-contours.png

The book itself shows Plotly code to the reader, while the committed PNG is produced by the matplotlib (or R, or MATLAB) script kept here. When the underlying analysis changes, update the script and regenerate the image in the same commit, so the code and the picture stay in step. Hand-drawn diagrams are edited in their .svg (or .pxm) source and re-exported.

Usage rights

These figures are part of Process Improvement using Data and are licensed, like the book, under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) licence.

You are free to copy, adapt, and redistribute the figures, including for courses you teach and for commercial use, provided that you:

• give attribution to the original author, Kevin Dunn, and
• share alike, licensing any adapted version under the same CC BY-SA 4.0 terms.

A small number of figures adapt third-party material (for example screenshots, or plots of publicly reported data such as stock prices). Where that is the case, the book credits the original source at the point of use; please carry that credit through if you reuse such a figure.

Citation

If you use these figures, please cite the book:

Dunn, K. G. (2010–2026). Process Improvement using Data (CC BY-SA 4.0). Zenodo. https://doi.org/10.5281/zenodo.20284934

• Book: https://learnche.org/pid
• Book source: https://github.com/kgdunn/pid-book
• Companion package: https://github.com/kgdunn/process-improve