Statistical Modelling in Medical and Economic Data
Edited by Nur Arina Bazilah Kamisan & Noraslinda Mohamed Ismail
ISBN: 978-983-52-2051-7
Price: RM60
A new and invaluable resource titled “Statistical Modelling in Medical and Economic Data” has been released, offering comprehensive insights into the process of statistical modelling. Edited by Nur Arina Bazilah Kamisan and Noraslinda Mohamed Ismail, this book delves into a variety of statistical models and techniques essential for analyzing complex datasets, making it a perfect resource for students, lecturers, and data analysts.
The book highlights the importance of choosing the correct statistical model depending on the goal of the analysis and showcases examples from both medical and economic fields. Some of the critical analyses covered in the book include:
- Lung cancer patient studies
- Correlation between aplastic anemia and bone marrow transplant
- Air pollution forecasting
- Smoking cessation analysis
- Economic data analyses such as inflation rate modelling, insurance policy analysis, and crude oil price forecasting.
It covers various statistical techniques such as Bayesian models, Monte Carlo simulations, regression analysis, ARIMA models, and fuzzy models, among others. The editors have ensured a robust approach to modelling by applying more than one statistical model to each case study, providing an enriched learning experience.
This book serves as an excellent tool for lecturers as a teaching example, data analysts in need of practical guidance, and students eager to master statistical data analysis. The blend of theoretical background and applied examples makes it a must-have resource in the academic and professional realms of medical and economic data analysis.
Where to Buy:
The book is available for purchase at:
Kedai Buku Universiti, UTM Johor Bahru
Penerbit UTM Press
penerbit.utm.my/booksonline
Shopee: shopee.com.my/utmpress
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.