Process Improvement using Data.

Format:

Book

Author/Creator:

Dunn, Kevin, author.

Language:

English

Subjects (All):

Engineering and Technology--Textbooks.

Engineering and Technology.

Mathematics--Textbooks.

Mathematics.

Physical Description:

1 online resource

Place of Publication:

[Place of publication not identified] Kevin Dunn [2026]

Language Note:

In English.

Summary:

There is no other free, coherent text that covers what engineers and scientists actually do with process data (visualization, regression, designed experiments, process monitoring, and multivariate / latent-variable methods) in one volume. Most textbooks pick one of those topics and go deep. Practitioners need all of them, and need to see how they fit together, because real industrial problems don't respect chapter boundaries. Process Improvement using Data was written to fill that gap, and has been continuously refined in industry-facing classrooms and in industrial practice since 2010. It is suitable for upper-undergraduate or introductory-graduate courses, and for self-study by working engineers and data scientists with a basic statistics background.

Contents:

Preface

1. Data visualization

2. Univariate review

3. Process monitoring

4. Least-squares modelling

5. Design and analysis of experiments

6. Latent variable modelling

7. Product development and product improvement

Index

Notes:

Description based on online resource