Statistics for Chemical and Process Engineers
A Modern Approach
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Beschreibung
This book shows the reader how to develop and test models, design experiments and analyse data in ways easily applicable through readily available software tools like MS Excel® and MATLAB®. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory, Statistics for Chemical and Process Engineers provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
A coherent, concise and comprehensive course in the statistics needed for a modern career in chemical engineering; covers all of the concepts required for the American Fundamentals of Engineering examination.
This book shows the reader how to develop and test models, design experiments and analyse data in ways easily applicable through readily available software tools like MS Excel® and MATLAB®. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory, Statistics for Chemical and Process Engineers provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
Covers all concepts required by the American Fundamentals of Engineering Examination Helps the reader perform correct data analysis by providing detailed guidance frameworks in addition to the conceptual presentation Emphasizes examples relevant to chemical and process engineers especially those new to statistical analysis Microsoft Excel Templates facilitate the use of the methods presented without requiring the practitioner to have access to specialized software Generalized exposition of results means they can be put to use in the widest range of applications possible Integrative approach to system identification, linear regression and statistical theory helps the reader to understand the similarities and differences in the methods used Includes supplementary material: sn.pub/extras
Autor*in
Yuri A.W. Shardt
Themen in »Statistics for Chemical and Process Engineers«
ANOVA Analysis Data Mining Chemistry Data Visualization Design of Experiments FE Review Manual Fundamentals of Engineering Exam Review Book Generalised Factorial Design Interpretation of Experiments Introductory Statistics Principal Component Analysis Regression Analysis System Identification