Statistics for Chemical and Process Engineers - A modern approach

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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

Author(s): Yuri A.W. Shardt
Edition: 1
Publisher: Springer International Publishing
Year: 0

Language: English
Pages: 436
Tags: Industrial Chemistry/Chemical Engineering; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Control; Industrial and Production Engineering

Front Matter....Pages i-xxvi
Introduction to Statistics and Data Visualisation....Pages 1-30
Theoretical Foundation for Statistical Analysis....Pages 31-85
Regression....Pages 87-140
Design of Experiments....Pages 141-209
Modelling Stochastic Processes with Time Series Analysis....Pages 211-282
Modelling Dynamic Processes Using System Identification Methods....Pages 283-336
Using MATLAB® for Statistical Analysis....Pages 337-362
Using Excel® to Do Statistical Analysis....Pages 363-398
Back Matter....Pages 399-414