Linear Models with Python

Praise for Linear Models with R:

This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway's wealth of experience in teaching and using linear models. ... It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal

Throughout, it gives plenty of insight ... with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen...I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society

Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python.

Features:

• Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented.
• This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning.
• A reader coming to this book from an ML background will learn new statistical perspectives on learning from data.
• Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data.
• Includes an Appendix on Python for beginners.

Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

《使用R進行線性模型》的好評如潮：

這本書是任何對了解和應用線性模型感興趣的人必備的工具。章節的邏輯排序非常合理，展現了Faraway在教授和使用線性模型方面的豐富經驗...它以一種邏輯而複雜的方式呈現材料，使線性建模對幾乎所有領域的研究人員都具有吸引力。-《生物統計學雜誌》

整本書充滿了洞察力...即使是經驗豐富的從業者也會欣賞其中的評論。穿插其中的R代碼和輸出結果中，可以找到許多我認為是可靠的統計建議的小寶石，這些建議在所選的例子中得到了很好的體現...我愉快地閱讀了這本書，並認為對於任何從事線性模型使用或教學的人來說，也會有同樣的感受。-《皇家統計學會雜誌》

與廣受好評的暢銷書《使用R進行線性模型》一樣，《使用Python進行線性模型》將R替換為Python，無縫地呈現了線性建模的實踐。《使用Python進行線性模型》提供了關於重要數據分析主題的最新見解，從估計、推斷和預測到缺失數據、因子模型和區塊設計。許多例子演示了如何使用Python應用不同的方法。

特點：
- Python是一種功能強大的開源編程語言，越來越多地用於數據科學、機器學習和計算機科學。Python和R相似，但R是專為統計設計的，而Python則是多才多藝的。
- 這個版本將R替換為Python，使更多非統計學背景的用戶能夠使用。
- 從機器學習背景來看，讀者將學習到關於從數據中學習的新的統計觀點。
- 主題包括模型選擇、收縮、區塊實驗和缺失數據。
- 包含一個針對初學者的Python附錄。

《使用Python進行線性模型》解釋了如何在物理科學、工程、社會科學和商業應用中使用線性模型。它非常適合作為線性模型或線性回歸課程的教材。

Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.

Julian J. Faraway是巴斯大學數學科學系的統計學教授。他的研究專注於功能和形狀數據的分析，特別應用於人體運動建模。他在加州大學伯克利分校獲得統計學博士學位。