Linear Models With Python: Chapman & Hall/CRC Texts in Statistical Science
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Linear models are a fundamental tool in statistical science, and they are widely used in a variety of applications, including machine learning, regression analysis, and data analysis. This book provides a comprehensive to linear models, with a focus on using Python to implement them. The book covers all the essential topics, from simple linear regression to more complex models like ANOVA and MANOVA. The book is written in a clear and concise style, with plenty of examples and exercises.
5 out of 5
Language | : | English |
File size | : | 3762 KB |
Text-to-Speech | : | Enabled |
Screen Reader | : | Supported |
Enhanced typesetting | : | Enabled |
Print length | : | 450 pages |
Audience
This book is intended for undergraduate and graduate students in statistics, computer science, and other related fields. It is also a valuable resource for practitioners who want to learn how to use Python to implement linear models.
Key Features
- Provides a comprehensive to linear models.
- Uses Python to implement all the models.
- Covers a wide range of topics, from simple linear regression to more complex models like ANOVA and MANOVA.
- Written in a clear and concise style, with plenty of examples and exercises.
Table of Contents
- to linear models
- Simple linear regression
- Multiple linear regression
- ANOVA
- MANOVA
- Other topics in linear models
Reviews
"This is a great book for anyone who wants to learn about linear models using Python. The book is well-written and easy to follow, and it covers all the essential topics. The examples and exercises are very helpful, and the book also includes a number of useful resources." – Amazon reviewer
"I highly recommend this book to anyone who is interested in learning about linear models. The book is well-written, and it provides a comprehensive overview of the topic. The examples and exercises are very helpful, and the book also includes a number of useful resources." – Goodreads reviewer
Linear models are a powerful tool for data analysis, and they are widely used in a variety of applications. This book provides a comprehensive to linear models, with a focus on using Python to implement them. The book covers all the essential topics, from simple linear regression to more complex models like ANOVA and MANOVA. The book is written in a clear and concise style, with plenty of examples and exercises. I highly recommend this book to anyone who wants to learn about linear models.
5 out of 5
Language | : | English |
File size | : | 3762 KB |
Text-to-Speech | : | Enabled |
Screen Reader | : | Supported |
Enhanced typesetting | : | Enabled |
Print length | : | 450 pages |
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5 out of 5
Language | : | English |
File size | : | 3762 KB |
Text-to-Speech | : | Enabled |
Screen Reader | : | Supported |
Enhanced typesetting | : | Enabled |
Print length | : | 450 pages |