Jhareswar Maiti

Multivariate Statistical Modeling in Engineering and Management (eBook, PDF)

• Format: PDF

Produktbeschreibung

The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution.

Key features:

• Links data generation process with statistical distributions in multivariate domain
• Provides step by step procedure for estimating parameters of developed models
• Provides blueprint for data driven decision making
• Includes practical examples and case studies relevant for intended audiences

The book will help everyone involved in data driven problem solving, modeling and decision making.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 636
• Erscheinungstermin: 25. Oktober 2022
• Englisch
• ISBN-13: 9781000618396
• Artikelnr.: 65649761

Autorenporträt

Jhareswar Maiti (PhD, FIE), the Founder Chairman of the Centre of Excellence in Safety Engineering & Analytics (CoE-SEA) and Professor of the Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur has over 20 years of teaching, research and consulting work in the fields of applied analytics and multivariate statistical modeling. He is pioneer in making Safety Analytics as core area of research in the broad domain of Safety Science. He has established a unique world class laboratory called "Safety Analytics and Virtual Reality Laboratory" at IIT Kharagpur.

Inhaltsangabe

1. Introduction. 2. Basic Univariate Statistics. 3. Basic Computations. 4.
Multivariate Descriptive Statistics. 5. Multivariate Normal Distribution.
6. Multivariate Inferencial Statistics. 7. Multivariate Analysis of
Variance. 8. Multiple Linear Regression. 9. Multivariate Multiple Linear
Regression. 10. Path Model. 11. Principal Component Analysis. 12.
Exploratory Factor Analysis. 13. Confirmatory Factor Analysis. 14.
Structural Equation Modeling.