The book describes the theoretical principles of nonstatistical methods of data analysis but without going deep into complex mathematics. The emphasis is laid on presentation of solved examples of real data either from authors' laboratories or from open literature. The examples cover wide range of applications such as quality assurance and quality control, critical analysis of experimental data, comparison of data samples from various sources, robust linear and nonlinear regression as well as various tasks from financial analysis. The examples are useful primarily for chemical engineers including analytical/quality laboratories in industry, designers of chemical and biological processes.
Features:
The book is aimed for Senior Undergraduate Students, Researchers, and Professionals in Chemical/Process Engineering, Engineering Physics, Stats, Mathematics, Materials, Geotechnical, Civil Engineering, Mining, Sales, Marketing and Service, and Finance.
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Pavel Kovanic (Born 1928) 1950-1955: Studied high voltage technology on the Technical University in Sverdlovsk (recently Ekaterinburg, Russia). 1956-1970: Researcher, head of a scientific department at the Nuclear Research Institute of the Czechoslovak Academy of Sciences. 1970-1995: Scientist at the Institute of the Automation and Theory of Information of the Czech Academy of Sciences. 1995-2018: As a retired scientist participated as a scientific consultant on several research projects including grants of European Union.
The focus of the book will be concentrated to the step-by-step explanation of applications of the algorithms to real data which are met in daily practice by all data analysts, with particulr focus on chemcial engineering. The book will lead readers through all relevant tasks on one-dimensional, two-dimensional, and multi-dimensional data analysis.
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