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Springer Applied Statistical Learning with Stata Case Studies

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Description

Master modern statistical learning methods with this comprehensive textbook from Springer. This guide provides an accessible overview of essential techniques, moving from foundational concepts to advanced algorithms. Each chapter focuses on specific statistical learning methods or groups of related techniques to build your expertise systematically. Designed for practical application, the book includes detailed case studies using Stata software. You will explore a wide range of topics including logistic regression, regularized linear models like Lasso, nearest neighbors, and the Naive Bayes classifier. The text also covers classification trees, random forests, boosting, support vector machines, feature engineering, and neural networks. This is an essential resource for students and professionals in higher education seeking to bridge the gap between statistical theory and practical software implementation.

Key Features

Includes practical case studies using Stata software to help apply statistical learning methods to real-world data.

Covers a wide range of algorithms including logistic regression, Lasso, and nearest neighbors.

Provides detailed instruction on classification techniques such as Naive Bayes, classification trees, and random forests.

Explores advanced modeling topics like boosting, support vector machines, and neural networks.

Addresses essential data preparation through dedicated sections on feature engineering.

Published by Springer as part of the Statistics and Computing series for higher education.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
03 August 2024
Listed Since
04 August 2024

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