Maiti J. Multivariate Statistical Modeling in Engineering...2022

Textbook in PDF format

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.
Foreword.
Preface.
Acknowledgments.
Author.
Introduction.
Basic Univariate Statistics.
Basic Computations.
Multivariate Descriptive Statistics.
Multivariate Normal Distribution.
Multivariate Inferential Statistics.
Multivariate Analysis of Variance.
Multiple Linear Regression.
Multivariate Multiple Linear Regression.
Path Model.
Principal Component Analysis.
Exploratory Factor Analysis.
Confirmatory Factor Analysis.
Structural Equation Modeling.
Bibliography.
App. A1: Cumulative Standard Normal Distribution.
App. A2: Percentage Points of Student’s t- distribution.
App. A3: Percentage Points of the Chi- square Distribution.
App. A4: Percentage Points of the F- distribution (α = 0.10).
App. A5: Percentage Points of the F- distribution (α = 0.05).
App. A6: Percentage Points of the F- distribution (α = 0.01).
Index