Basel M. Al-Eideh Knihy

Focusing on statistical inference in Markov processes, this book aims to equip graduate students and researchers with essential skills in estimation theory and hypothesis testing for discrete Markov chains. It offers a thorough survey of key results, including complete proofs of significant findings like the asymptotic properties of maximum likelihood estimators, ensuring clarity without cross-referencing. Additionally, the text reviews hypothesis tests for absorbing chains and presents minor contributions through the generalization and extension of existing results.

Focusing on the use of SPSS Software, this book provides a comprehensive introduction to statistical techniques for analyzing business data, tailored for students without prior experience. It adopts a step-by-step approach, enhancing students' confidence in their analytical skills and demonstrating SPSS's capabilities for data analysis and presentation. Suitable for undergraduates and postgraduates in Business and Economics, it serves as a valuable reference for courses in statistical methods and is beneficial for researchers in various social science fields.

Focusing on modeling and forecasting techniques, this book equips students and researchers with essential skills to analyze past, present, and future data for effective planning. It serves as an introductory guide to applied time series modeling, specifically designed for those with limited experience in model building. Utilizing statistical packages like Minitab and SPSS, readers will learn to formulate and interpret statistical models. This comprehensive resource is ideal for undergraduate courses and researchers in business, economics, and natural sciences.

Focusing on finite Markov chains, this book provides a comprehensive introduction to their transition probabilities, matrices, and graphical representations. It classifies state spaces, explores absorbing Markov chains and their probabilities, and discusses stationary and quasi-stationary distributions. Practical applications are also included, making it a valuable resource for undergraduate courses and researchers in statistics, stochastic processes, and related fields. The content is designed to enhance understanding and knowledge of this specific type of Markov process.