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In the recent decades, the emerging new molecular measurement techniques and their subsequent availability in chemical database has allowed easier retrieval of the associated data by the chemical analyst. Before the data revolution, most books focused either on mathematical modeling of chemical processes or exploratory chemometrics. Computational and Statistical Methods for Chemical Engineering aims to combine these two approaches and provide aspiring chemical engineers a single, comprehensive account of computational and statistical methods.The book consists of four parts: Part I discusses the necessary calculus, linear algebra, and probability background that the student may or may not have encountered before. Part II provides an overview on standard computational methods and approximation techniques useful for chemical engineering systems. Part III covers the most important statistical models, starting from simple measurement models, via linear models all the way to multivariate, non-linear stochiometric models. Part IV focuses on the importance of designed experiments and robust analyses. Each chapter is accompanied by an extensive selection of theoretical and practical exercises. The book can be used in combination with any modern computational environment, such as R, Python and MATLAB. Given its easy and free availability, the book includes a bonus chapter giving a simple introduction to R programming.This book is particularly suited for undergraduate students in Chemical Engineering who require a semester course in computational and statistical methods. The background chapters on calculus, linear algebra and probability make the book entirely self-contained. The book takes its examples from the field of chemistry and chemical engineering. In this way, it motivates the student to engage actively with the material and to master the techniques that have become crucial for the modern chemical engineer.

Highly recommended by JASA, Technometrics, and other leading statistical journals, the first two editions of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Third Edition continues to lead readers step-by-step through the process of fitting LMMs. The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. All examples have been updated, with a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included, and there is a new chapter on power analysis for mixed-effects models.Features:•Dedicates an entire chapter to the key theories underlying LMMs for clustered, longitudinal, and repeated measures data•Provides descriptions, explanations, and examples of software code necessary to fit LMMs in SAS, SPSS, R, Stata, and HLM•Contains detailed tables of estimates and results, allowing for easy comparisons across software procedures•Presents step-by-step analyses of real-world data sets that arise from a variety of research settings and study designs, including hypothesis testing, interpretation of results, and model diagnostics•Integrates software code in each chapter to compare the relative advantages and disadvantages of each package•Supplemented by a website with software code, datasets, additional documents, and updatesIdeal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.