Data Analysis and Modelling with Machine Learning Techniques

This book can best be described as the student's guide and the researcher's quick reference. It proposes that students, researchers, and practitioners adopt sound statistical practices in research data analysis and modeling, a concern frequently expressed by reviewers and thesis supervisors. It presents eight chapters cutting across common research design and problem areas, including software applications, parametric and nonparametric inferential methods, various data modelling methods, and machine learning techniques. Chapter 1 provides step-by-step guides to using three software applications – SPSS, R and SmartPLS – for data analysis and modelling, allowing for flexibility in choosing software applications. Chapters 2 and 3 lead the way to generating common research data along with various data diagnostic practices, data summaries, and data visualization techniques. Chapter 4 provides illustrations in applying parametric and nonparametric methods. Chapters 5 and 6 detail robust practices in data modelling. Chapter 7 deploys common machine learning techniques to validate models proposed in Chapter 6, while Chapter 8 provides a guide in achieving the foremost goal in time series analysis, along with an automated ARIMA (Autoregressive Integrated Moving Average) procedure.