Mathematical Gnostics: Advanced Data Analysis for Research and Engineering Practice

The book provides a theoretical introduction to nonstatistical methods of data analysis, with a focus on chemical engineering applications. It covers a wide range of examples, including quality assurance, critical analysis, and robust regression, and emphasizes the use of data metrics and probability distributions. It is suitable for senior undergraduate students, researchers, and professionals in chemical/process engineering, engineering physics, statistics, mathematics, materials, geotechnical, civil engineering, mining, sales, marketing, and finance.

Format: Hardback
Length: 322 pages
Publication date: 08 February 2023
Publisher: Taylor & Francis Ltd

The book delves into the theoretical principles of nonstatistical methods of data analysis, providing a comprehensive understanding without delving into complex mathematics. It emphasizes the presentation of solved examples of real data, sourced from authors' laboratories or open literature, covering a 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, and various tasks from financial analysis. These examples are particularly valuable for chemical engineers, including analytical/quality laboratories in industry, designers of chemical and biological processes.

Key Features:

Exclusive Title: Mathematical Gnostics offers a unique perspective on nonstatistical methods with multidisciplinary applications, particularly focusing on chemical engineering.

Clarification of Data Space Metrics: The book elucidates the role of data space metrics, including the aggregation of uncertain data, providing insights into data uncertainty analysis.

New Look on Data Probability, Information, Entropy, and Thermodynamics: It offers a fresh perspective on data probability, information, entropy, and thermodynamics, shedding light on the underlying principles of data uncertainty.

Enables Design of Probability Distributions for All Real Data Samples: The book empowers readers to design probability distributions for all real data samples, including smaller ones, facilitating effective data analysis and interpretation.

Includes Data for Examples with Solutions with Exercises in R or Python: The book provides data for examples accompanied by solutions, accompanied by exercises in R or Python, enabling readers to apply the theoretical concepts and practice their skills.

Target Audience: The book is designed for senior undergraduate students, researchers, and professionals in chemical/process engineering, engineering physics, statistics, mathematics, materials, geotechnical, civil engineering, mining, sales, marketing, and service, as well as finance.

By exploring the theoretical foundations and practical applications of nonstatistical methods, this book serves as a valuable resource for advancing data analysis and interpretation in various fields.

Weight: 790g
Dimension: 234 x 156 (mm)
ISBN-13: 9781138339231