Applied Multivariate Statistical Analysis in Medicine

Author: Jiang, Jingmei

Edition: 1

ISBN: 9780443235870

Release Date: 23-08-2024

Package Dimensions: 9.3 x 7.5 x 1.2 inches

Languages: English

Binding: paperback

Number Of Pages: 522

Details: Applied Multivariate Statistical Analysis in Medicine provides a multivariate conceptual framework that allows readers to understand the interconnectivity and interrelations among variables, which maintains the intrinsic precision of statistical theories. With a strong focus on the fundamental concepts of multivariate statistical analysis, the book also gives insight into the applications of multivariate distribution in biomedical fields.

In 14 chapters, Applied Multivariate Statistical Analysis in Medicine covers the main topics of multivariate analysis methods widely used in health science research. The content is organized progressively from fundamental concepts to sophisticated methods. It begins with basic descriptive statistics in multivariate analysis and follows with parameter estimation, in addition to the hypothesis testing of a multivariate normal distribution, which has heavy applications in biomedical fields where the relationships among approximately normal variables are of great interest. Keeping mathematics to a minimum, considerable emphasis is placed on explanations and real-world applications of core principles to maintain a good balance between introducing theory and cultivating problem-solving skills. This book is a very valuable reference text for clinicians, medical researchers, and other researchers across medical and biomedical disciplines, all of whom confront increasingly complex statistical methods during the analysis and presentation of their results. Gives understanding and mastering of the multivariate analysis techniques in the medical sciences Maintains a balance between the introduction of statistical analysis theory and the cultivation of practical skills Exposes a variety of well-designed real-life cases that integrate concepts and analytical techniques Includes substantive exercises, online coding sources, and case discussions to solidify a conceptual understanding