Applied Statistics with Python

Volume II: Multivariate Models
1. Auflage 2025
ISBN: 978-1-041-00625-1
Verlag: CRC Press

Buch, Englisch, 310 Seiten, Format (B × H): 156 mm x 234 mm

Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.

As in Volume I, the Python programming language is used throughout due to its flexibility
and widespread adoption in data science and machine learning. The book relies heavily on
tools from the standard sklearn package, which are integrated directly into the discussion.
Unlike many other resources, Python is not treated as an add-on, but as an organic part of the
learning process.

This book is based on the author’s 15 years of experience teaching statistics and is designed
for undergraduate and first-year graduate students in fields such as business, economics,
biology, social sciences, and natural sciences. However, more advanced students and
professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required—core concepts are introduced and explained along the way, making the material accessible to a wide range of learners.

Key Features:

· Employs Python as an organic part of the learning process
· Removes the tedium of hand/calculator computations
· Weaves code into the text at every step in a clear and accessible way
· Covers advanced machine-learning topics
· Uses tools from Standardized sklearn Python package

Zielgruppe

Undergraduate Advanced and Undergraduate Core

Autoren/Hrsg.

Kaganovskiy, Leon

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Preface  1 Analysis of Variance (ANOVA)  2 Multivariate Data Models  3 Nonlinear Models 4 Tree-Based Methods 5 Unsupervised Models (Principal Values and Clusters)  Bibliography  Index

Über Autor(innen)

Leon Kaganovskiy is an Associate Professor at the Mathematics Department of Touro College. He received a M.S. in Theoretical Physics from Kharkov State University, and M.S. and PhD in Applied Mathematics from the University of Michigan. His most recent interest is in a broad field of Applied Statistics, and he has developed new courses in Bio-Statistics with R, Statistics for Actuaries with R, and Business Analytics with R. He teaches Statistics research courses at the Graduate Program in Speech-Language Pathology at Touro College.