Applied Statistics with Python - Kaganovskiy, Leon

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Auteur(s) : Kaganovskiy, Leon

Editeur : Taylor & Francis Ltd

Langue : Anglais

Parution : 01/12/2025

Format : Moyen, de 350g à 1kg

Nombre de pages : 646.0

ISBN : 1041191707

Résumé :

VOLUME ONE: INTRODUCTORY STATISTICS AND REGRESSION

Preface 1. Introduction 2. Descriptive Data Analysis 3. Probability 4. Probability Distributions 5. Inferential Statistics and Tests for Proportions 6. Goodness of Fit and Contingency Tables 7. Inference for Means 8. Correlation and Regression

VOLUME TWO: MULTIVARIATE MODELS

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

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Biographie:

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.

Sommaire:

Based on Dr. Leon Kaganovskiy's 15 years of experience teaching statistics courses at Touro University and Brooklyn College, , Two-Volume Set focuses on applied and computational aspects of statistics, 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.

Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning and the books heavily rely 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.

Applied Statistics with Python has been expanded from eight chapters to thirteen chapters in two volumes, and is intended for undergraduate students in business, economics, biology, social sciences, and natural science, while also being useful as a supplementary text for more advanced students and professionals. 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:

• Covers both introductory topics such as descriptive statistics, probability, probability distributions, proportion and means hypothesis testing, one-variable regression, as well as advanced machine-learning topics
• 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
• Uses tools from Standardized sklearn Python package