Multivariate Statistics and Machine Learning in R For Beginners

With Applications in Biology and Medicine
Erscheinungsjahr 2026
ISBN: 978-3-032-01850-2
Verlag: Springer

Buch, Englisch, 398 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 785 g

This book is more than just a book – it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential.
Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. 
The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification.
By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Tilevik, Andreas

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung

Inhaltsverzeichnis

A brief introduction to machine learning and multivariate statistics.- Matrix algebra.- Managing data in R.- Graphical illustration of multivariate data.- Multivariate relationships.- PCA and PCoA.- Linear discriminant analysis.- Distances in space.- Multivariate statistical tests.- Classification and performance metrics.- Supervised Machine Learning.- Clustering.- PCR, PLS and Lasso regression.- Case studies.- Anwers to exercises.

Über Autor(innen)

Andreas Tilevik is an associate professor in Systems Biology at the University of Skövde, Sweden. He has more than 15 years of teaching and research experience in data analysis and machine learning. Andreas holds a PhD from the University of New South Wales, Australia, in computational biology and has a bachelor's degree in statistics from Karlstad University, Sweden. He is also the creator of the YouTube channel TileStats, which includes more than 100 videos in statistics and machine learning.