Here is the listing of Best reference books on Process Data Analysis and Design of Experiments.
|1. “Applied Statistics and Probability for Engineers” by Douglas C. Montgomery, G. C. Runger
Book Review: This book presents a practical approach to chemical sciences, physical sciences and engineering. This is very good textbook for courses in probability and statistics. The book stresses on real engineering applications and real engineering solutions and also includes material on bootstrap, P value usage, equivalence testing, p values combination and contains numerous examples on the same.
|2. “Introduction to Linear Regression Analysis” by Douglas C. Montgomery, E. A. Peck, and G. G. Vining
Book Review: This book provides introduction to the basics of regression analysis. The book presents both theoretical concepts and applications thereby helping the readers to understand the principles that are needed to apply for regression model building techniques in different streams like engineering, management and health sciences. The book includes a discussion on transformations and weighted least squares that are used to resolve problems of model inadequacy and influential observations. This is a very good book for statistics and engineering courses on regression at the graduate and undergraduate levels.
|3. “Applied Regression Analysis” by Norman R. Draper, and Harry Smith
Book Review: This book gives very good introduction to the basics of regression analysis. The book also discusses the statistical tools that are used for establishing relationships between variables. The book also stresses on the presentation of concepts and applications and provides solid introduction to the fundamentals of regression analysis. The book also checks both linear and non-linear regression models and contains chapters on generalized linear models, regression geometry, robust regression and resampling procedures. This book is useful for analysts, researchers and university students for courses on regression.
|4. “Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building” by George E. P. Box, W. G. Hunter, and J. S. Hunter
Book Review: The various topics covered in the book are innovation catalysis, problem solving and discovery. The book also describes scientific experimentation and the tools needed to increase the knowledge gained from research data. The book explains the usage of these tools during all the stages of investigation process. The author first describes the problem that has to be solved and later examines the statistical methods of design and analysis. The topics in the book are variance graphical analysis, complex design computer analysis, transformation simplification, process control basics and many more.
|5. “Computational Complexity and Feasibility of Data Processing and Interval Computations” by Vladik Kreinovich|
|6. “Applied Design Of Experiments And Taguchi Methods” by P. Shahabudeen, K. Krishnaiah|
|7. “Linear Estimation And Design Of Experiments” by D. Joshi|
|8. “Design of Experiments” by Virgil L Anderson|
|9. “Design of a reliable processing system for satellite data” by Francesco Lazzarotto|
|10. “Introduction to Orthogonal Transforms: With Applications in Data Processing and Analysis” by Ruye Wang|
Sanfoundry Global Education & Learning Series – Best Reference Books!