This companion book to the textbook Statistics for Chemical and Process Engineers−A Modern Approach provides a complete overview of how to use Python to solve typical statistical problems in engineering. In addition to short sections on the required theory, the focus of the book is on detailed, Python code blocks for solving the specific problems. Furthermore, solutions are provided for standard problems that can then be re-used and modified as necessary. End-of-chapter questions allow the reader to independently test the knowledge acquired.

The content

Detailed solutions for problems in

• Data visualization
• Hypothesis testing
• Linear and nonlinear regression
• Design of experiments

The target groups

• Students and engineers that wish to solve statistical problems using Python
• Professors and instructors that wish to use Python in their courses

The author

Prof. Dr. Yuri A.W. Shardt is currently the chair of the Department of Automation Engineering (DE: Fachgebiet Automatisierungstechnik) at the Technical University of Ilmenau.

Prof. Dr. Yuri A. W. Shardt is currently the chair of the Department of Automation Engineering (DE: Fachgebiet Automatisierungstechnik) in the Faculty of Computer Science and Automation (DE: Fakultät Informatik und Automatisierung) at the Technical University of Ilmenau (DE: Technische Universität Ilmenau), working in the fields of big data, including process identification and monitoring with an emphasis on the development and industrial implementation of soft sensors; holistic control, including the development of advanced control strategies for complex industrial process; and the smart world, including such implementations as smart factories, smart home, Industry 4.0, and smart grids. Previously, he worked at the University of Waterloo in the Department of Chemical Engineering and at the University of Duisburg-Essen in the Institute of Control and Complex Systems (DE: Fachgebiet Automatisierungstechnik und komplexe Systeme, AKS) as an Alexander von Humboldt Fellow. He has written 40 papers appearing in such journals as Automatica, Journal of Process Control, IEEE Transactions on Industrial Electronics, and Industrial and Engineering Chemistry Research on topics ranging from system identification, soft sensor development, to process control. He has presented his research at numerous conferences and taught various courses in the intersection between statistics, chemical engineering, process control, EXCEL®, and MATLAB®. Prof. Dr. Shardt completed his doctoral degree under the supervision of Prof. Dr. Biao Huang at the University of Alberta. His thesis examined the methods for extracting valuable data for system identification from data historians for application to soft sensor design. In addition to his academic work, he has spent considerable time in industry working on implementing various process control solutions. He also has interests in linguistics, as well as software internationalisation and localisation.