Statistics for Chemical Engineers: From Data to Models to Decisions

by Victor M. Zavala

Overview

Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data–model–decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.

Product Details

ISBN-13:	9781009541893
Publisher:	Cambridge University Press
Publication date:	09/25/2025
Series:	Cambridge Series in Chemical Engineering
Pages:	468
Product dimensions:	0.00(w) x 10.00(h) x 0.00(d)

About the Author

Victor M. Zavala is the Baldovin-DaPra Professor of Chemical and Biological Engineering at the University of Wisconsin, Madison and a Senior Computational Mathematician at Argonne National Laboratory. He is the recipient of the Harvey Spangler Award for Innovative Teaching and Learning Practices from the College of Engineering at UW-Madison, and of the Presidential Early Career Award for Scientists and Engineers (PECASE).

Table of Contents

1. Introduction to statistics; 2. Univariate random variables; 3. Multivariate random variables; 4. Estimation for random variables; 5. Estimation for structural models; 6. Statistical learning; 7. Decision-making under uncertainty.

Editorial Reviews

‘… speaks our native language, reframing statistics not as an auxiliary tool but as a foundational modeling paradigm intrinsic to how we understand, design, and make decisions in complex systems familiar to chemical engineers.’ Michael Webb, Princeton University

‘This excellent book bridges the fundamentals of statistics with modern machine learning, providing a solid foundation in statistical thinking alongside important insights into data-driven decision-making.’ Antonio Del Rio Chanona, Imperial College London

‘A timely and much needed resource which presents clear, relevant examples tailored to our discipline. The clarity and purpose of this textbook are invaluable for both undergraduate and graduate students.’ Viviana Monje, University at Buffalo

‘A masterful integration of statistical thinking into the chemical engineering mindset … fills a critical gap and offers a fresh perspective on how engineers model, analyze, and make decisions.’ Joe Paulson, The Ohio State University

‘Masterfully integrates theory and concepts with real-world data analysis applications. This is a must-read for chemical engineering students, practitioners, researchers, and educators.’ Alexander Dowling, Notre Dame University

From the Publisher