Description
Statistical modelling is a comprehensive process that employs a variety of statistical models and techniques to examine datasets and give relevant information that helps in detecting correlations between variables and generating predictions. Many datasets may be analyzed with statistical models, but how to decide which model is appropriate with the aim of the analysis. This book will provide sufficient guidance for picking relevant statistical models depending on the purpose of the investigation. In this book, it focuses on statistical modelling using medical data such as analysis on lung cancer patients, correlation analysis on aplastic anemia with bone marrow transplant, air pollution forecasting, smoking cessation analysis and economic data such as modelling inflation rate, analysis on insurance policy, and forecasting the crude oil price using statistics models such as Bayesian, Monte Carlo, regression analysis, ARIMA, fuzzy models, and many more.
The modelling approach discussed in this book is quite extensive, and the authors used more than one model for each case, providing readers with a more in-depth understanding of the modelling process. Thus, this book is excellent for lecturers who want to utilize it as a teaching example, data analysts, and students who want to learn and use statistical data analysis techniques since it demonstrates a range of statistical models that could be used to analyze data.
