Review
Dr Faraway uses many examples and graphical procedures to illustrate the methods. This is a great strength of the book. …Linear Models with R is one of several books appearing to make R more accessible by bringing together functions from a number of packages and illustrating their use. From this perspective alone it is an important contribution. …I feel this book does a nice job of describing the methods available in linear modeling and illustrating the realistic implementation of these methods in a careful data analysis.
-Statistics in Medicine, 2006
One danger with applied books such as this is that they become recipe lists of the kind 'press this key to get that result.' This is not so with Faraway's book. Throughout, it gives plenty of insight on what is going on, 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…I find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught.
-Journal of the Royal Statistical Society
One danger with applied books such as this is that they become recipe lists of the kind press this key to get that result. This is not so with Faraways book. Throughout, it gives plenty of insight on what is going on, 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…I find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught.
-Journal of the Royal Statistical Society
Overall, Linear Models with R is well written and, given the increasing popularity of R, it is an important contribution.
-Technometrics, Vol. 47, No. 3, August 2005
The book is very comprehensibly written and can therefore be recommended for beginners in linear models. It is clearly and simply explained how to use R and which packages are necessary to analyze linear models. …All in all, this book is recommendable as a textbook for computational linear regression courses and therefore for students and lecturers, but also for applied statisticians who want to get started on regression analysis using the software R.
-Biometrics
The book is very comprehensibly written and can therefore be recommended for beginners in linear models. It is clearly and simply explained how to use R and which packages are necessary to analyze linear models. …All in all, this book is recommendable as a textbook for computational linear regression courses and therefore for students and lecturers, but also for applied statisticians who want to get started on regression analysis using the software R.
-Biometrics
There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysis…The text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations.
-Short Book Reviews of the International Statistical
Institute
There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysis…The text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations.
-Short Book Reviews of the International Statistical
Institute
…Dr. Faraway uses many examples and graphical procedures to illustrate the methods. This is a great strength of the book. … Linear Models with R is one of several books appearing to make R more accessible by bringing together functions from a number of packages and illustrating their use. From this perspective alone it is an important contribution. …I feel this book does a nice job of describing the methods available in linear modeling and illustrating the realistic implementation of these methods in a careful data analysis. …
-Statistics in Medicine, 2006
Product Description
This textbook focuses on the practice of regression and analysis of variance. Readers will learn which methods are available and the various situations in which they can be applied. Numerous examples clarify the use of the techniques and demonstrate what conclusions can be made. The author places less emphasis on mathematical theory, partly because some prior knowledge is assumed and partly because the issues are better tackled elsewhere. An interesting aspect of this book is the author's emphasis on statistical theory and qualitative aspects of the topic. He highlights the importance of data analysis and stresses its inportance through use of the inclusion of R software.
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对于我这种偏爱视觉化学习的读者来说,这本书的图表质量简直是教科书级别的典范。作者没有堆砌那些令人眼花缭乱的复杂三维图,而是专注于使用最能清晰传达信息的二维图示。特别是关于贝叶斯方法的引入部分,作者使用了非常巧妙的图例来解释先验分布、似然函数和后验分布之间的动态关系,即便是对于初次接触贝叶斯统计的人,也能通过这些图形直观地把握其核心逻辑。此外,作者在章节末尾设置的“思考题”环节设计得极具启发性,它们往往不是简单地要求计算某个数值,而是要求读者对特定模型的实际应用场景进行深入的哲学思考和权衡。这种互动式的学习体验,极大地增强了我对知识的内化吸收,让我感觉自己不仅仅是在“看”书,而是在积极地“参与”一场智力上的对话。
评分这本书的深度远超同类书籍,它真正做到了将“R语言”这个强大的工具与“线性模型”这一核心统计学思想进行无缝对接。我尤其欣赏作者在处理复杂模型时的那种庖丁解牛般的细致。例如,在讲解多重共线性问题时,作者不仅展示了如何使用VIF(方差膨胀因子)进行诊断,还详细对比了不同正则化方法(比如岭回归和Lasso)在解决此问题时的优劣和适用场景,并且所有论证都配有清晰的R代码示例。代码的注释详尽到令人发指,即便我是一个对R语言有一定基础的进阶用户,也能从中学习到不少更高效的编程技巧。当我尝试将书中的案例代码应用于我自己的数据集时,发现模型的解释性和预测性能都有了显著提升。这本书更像是一位经验丰富、知识渊博的导师在你身边实时指导,随时准备解答你可能遇到的每一个技术难题,而不是一本冷冰冰的参考手册。
评分这本书的叙事节奏把握得非常精准,它像一部精心编排的交响乐,从平缓的引子(基础回归)逐渐过渡到激昂的高潮(混合效应模型和时间序列分析)。最让我印象深刻的是它对模型诊断部分的讲解。很多教材在这里往往草草了事,但这本书花了整整一个章节来探讨残差分析、异方差性和正态性检验的各种陷阱和应对策略。作者甚至开辟了一个小节,专门讨论了在面对“不满足标准假设”的数据时,什么时候应该选择变换数据,什么时候应该考虑使用更稳健的模型,而不是盲目地进行数据转换。这种严谨的批判性思维训练,对于提升一个分析师的职业素养来说,价值无可估量。读完这部分内容,我不再是那个只会调用`lm()`函数的“代码执行者”,而是一个能够真正理解模型局限性的“统计思考者”。
评分这本书的封面设计简直是艺术品,那种深沉的蓝色调配上简洁的字体,立刻就给人一种专业而又不失雅致的感觉。我是在一家独立书店偶然翻到的,当时正为手头上的一个数据建模项目焦头烂额,急需一本能将理论与实践完美结合的工具书。这本书的排版非常人性化,字里行间留出的空白恰到好处,阅读起来毫不费力。更让我惊喜的是,它并没有一上来就抛出复杂的数学公式,而是用非常生活化的例子来引入概念,比如用天气数据预测水果的收成,这种方式极大地降低了初学者的门槛。我花了大概一个下午的时间研读了前三章,发现作者对于“为什么”这个问题的探讨远超我预期的深度。他没有仅仅停留在“如何运行代码”的层面,而是深入剖析了线性模型背后的统计学原理和假设条件,这一点对于真正想成为数据科学家的我来说至关重要。这本书的开篇给我营造了一种非常好的学习氛围,让我觉得接下来的学习过程会是一种享受而非煎熬。
评分这本书的综合性和前瞻性令人印象深刻。它没有将自己局限在传统的最小二乘法框架内,而是勇敢地将目光投向了现代数据科学的前沿领域。比如,它对广义可加模型(GAMs)的阐述,清晰地展示了如何在保持线性模型可解释性的同时,捕捉到数据中更复杂的非线性关系。更重要的是,作者在收尾部分对“模型选择的伦理”进行了深刻的反思,探讨了在商业决策中,过度拟合和模型简化之间的权衡艺术。这种超越技术细节的宏观视野,使得这本书的价值远超一本纯粹的技术手册。它像是一份给未来数据科学家的职业宣言,指导我们如何负责任、有洞察力地使用统计工具。我强烈推荐给所有希望将线性建模技能提升到新境界的从业者和学术研究者。
评分原理部分用了很多线性代数的内容,看起来略吃力,于是去补了几集Gilbert Strang的线性代数。撸完还是挺有收获的,但是仅限于基本的线性回归(连续的自变量)。后面到ANOVA以及ANCOVA比较跳跃,又不明白了。打算撸别的。
评分STAT 425
评分原理部分用了很多线性代数的内容,看起来略吃力,于是去补了几集Gilbert Strang的线性代数。撸完还是挺有收获的,但是仅限于基本的线性回归(连续的自变量)。后面到ANOVA以及ANCOVA比较跳跃,又不明白了。打算撸别的。
评分原理部分用了很多线性代数的内容,看起来略吃力,于是去补了几集Gilbert Strang的线性代数。撸完还是挺有收获的,但是仅限于基本的线性回归(连续的自变量)。后面到ANOVA以及ANCOVA比较跳跃,又不明白了。打算撸别的。
评分STAT 425
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