There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities, and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.

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全书共分7章,包括引论、线性方程组求解、线性最小二乘问题、非对称特征值问题、对称特征问题和奇异值分解、线性方程组迭代方法及特征值问题迭代方法，本书不仅给出了数值线性代数的常用算法，而且也介绍了多重网格法和区域分解法等新算法，并指导读者如何编写数值软件以及从何处找到适用的优秀数值软件。 　　本书可作为计算数学和相关理工科专业一年级研究生的教材,也可作为从事科学计算的广大科技工作者的参考书。 第1章 引论 　1.1 基本符号 　1.2 数值线性代数的标准问题 　1.3 一般的方法 　　1.3.1 矩阵分解 　　1.3.2 扰动理论和条件数 　　1.3.3 舍入误差对算法的影响 　　1.3.4 分析算法的速度 　　1.3.5 数值计算软件 　1.4 例：多项式求值 　1.5 浮点算术运算 　1.6 再议多项式求值 　1.7 向量和矩阵范数 　1.8 第1章的参考书目和其他话题 　1.9 第1章问题 第2章 线性方程组求解 　2.1 概述 　2.2 扰动理论 　2.3 高斯消元法 　2.4 误差分析 　　2.4.1 选主元的必要性 　　2.4.2 高斯消元法正式的误差分析 　　2.4.3 估计条件数 　　2.4.4 实际的误差界 　2.5 改进解的精度 　　2.5.1 单精度迭代精化 　　2.5.2 平衡 　2.6 高性能分块算法 　　2.6.1 基本线性代数子程序（blas） 　　2.6.2 如何优化矩阵乘法 　　2.6.3 使用3级blas改组高斯消元法 　　2.6.4 更多的并行性和其他性能问题 　2.7 特殊的线性方程组 　　2.7.1 实对称正定矩阵 　　2.7.2 对称不定矩阵 　　2.7.3 带状矩阵 　　2.7.4 一般的稀疏阵 　　2.7.5 不超过o（n2）个参数的稠密矩阵 　2.8 第2章的参考书目和其他的话题 　2.9 第2章问题 第3章 线性最小二乘问题 　3.1 概述 　3.2 解线性最小二乘问题的矩阵分解 　　3.2.1 正规方程 　　3.2.2 qr分解 　　3.2.3 奇异值分解 　3.3 最小二乘问题的扰动理论 　3.4 正交矩阵 　　3.4.1 豪斯霍尔德变换 　　3.4.2 吉文斯旋转 　　3.4.3 正交矩阵的舍入误差分析 　　3.4.4 为什么用正交矩阵 　3.5 秩亏最小二乘问题 　　3.5.1 用svd解秩亏最小二乘问题 　　3.5.2 用选主元的qr分解解秩亏最小二乘问题 　3.6 最小二乘问题解法的性能比较 　3.7 第3章的参考书目和其他话题 　3.8 第3章问题 第4章 非对称特征值问题 　4.1 概述 　4.2 典范型 　4.3 扰动理论 　4.4 非对称特征问题的算法 　　4.4.1 幂法 　　4.4.2 逆迭代 　　4.4.3 正交迭代 　　4.4.4 qr迭代 　　4.4.5 使qr迭代有实效 　　4.4.6 海森伯格约化 　　4.4.7 三对角和双对角约化 　　4.4.8 隐式位移的qr迭代 　4.5 其他的非对称特征值问题 　　4.5.1 正则矩阵束和魏尔斯特拉斯典范型 　　4.5.2 奇异矩阵束和克罗内克典范型 　　4.5.3 非线性特征值问题 　4.6 小结 　4.7 第4章参考书目和其他话题 　4.8 第4章问题 第5章 对称特征问题和奇异值分解 　5.1 概述 　5.2 扰动理论 　5.3 对称特征问题的算法 　　5.3.1 三对角qr迭代 　　5.3.2 瑞利商迭代 　　5.3.3 分而治之 　　5.3.4 对分法和逆迭代 　　5.3.5 雅可比法 　　5.3.6 性能比较 　5.4 奇异值分解算法 　　5.4.1 双对角svd的qr迭代及其变形 　　5.4.2 计算双对角svd达到高的相对精度 　　5.4.3 svd的雅可比法 　5.5 微分方程和特征值问题 　　5.5.1 toda格子 　　5.5.2 与偏微分方程的关系 　5.6 第5章参考书目和其他话题 　5.7 第5章问题 第6章 线性方程组迭代方法 　6.1 概述 　6.2 迭代法的在线（on-line）帮助 　6.3 泊松方程 　　6.3.1 一维泊松方程 　　6.3.2 二维泊松方程 6.3.3 用克罗内克积表达泊松方程 6.4 解泊松方程方法小结 　6.5 基本迭代法 　　6.5.1 雅可比法 　　6.5.2 高斯-塞德尔法 6.5.3 逐次超松弛法 6.5.4 模型问题的雅可比、高斯－塞德尔和sor（ω）的收敛性 6.5.5 雅可比、高斯－塞德尔和sor（ω）法明细的收敛准则 　　6.5.6 切比雪夫加速和对称sor（ssor） 　6.6 克雷洛夫子空间方法 　　6.6.1 通过矩阵-向量乘法得到关于a的信息 　　6.6.2 利用克雷洛夫子空间kk解ax=b 　　6.6.3 共轭梯度法 　　6.6.4 共轭梯度法的收敛性分析 　　6.6.5 预条件 　　6.6.6 解ax=b的其他克雷洛夫子空间算法 　6.7 快速傅里叶变换 　　6.7.1 离散傅里叶变换 　　6.7.2 用傅里叶级数解连续模型问题 　　6.7.3 卷积 　　6.7.4 计算快速傅里叶变换 　6.8 块循环约化 　6.9 多重网格法 　　6.9.1 二维泊松方程多重网格法概述 　　6.9.2 一维泊松方程的多重网格法详述 　6.10 区域分解法 　　6.10.1 无交叠方法 　　6.10.2 交叠方法 　6.11 第6章的参考书目和其他话题 　6.12 第6章问题 第7章 特征值问题的迭代方法 　7.1 概述 　7.2 瑞利-里茨方法 　7.3 精确算术运算的兰乔斯算法 　7.4 浮点算术运算的兰乔斯算法 　7.5 选择正交化的兰乔斯算法 　7.6 选择正交化之外的方法 　7.7 非对称特征值问题的迭代算法 　7.8 第7章的参考书目和其他话题 　7.9 第7章问题 参考文献（图灵网站下载） 索引

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Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de. The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg. Table of Contents Part I Descriptive Techniques Chapter 1 Comparison of Batches Part II Multivariate Random Variables Chapter 2 A Short Excursion into Matrix Algebra Chapter 3 Moving to Higher Dimensions Chapter 4 Multivariate Distributions Chapter 5 Theory of the Multinormal Chapter 6 Theory of Estimation Chapter 7 Hypothesis Testing Part III Multivariate Techniques Chapter 8 Regression Models Chapter 9 Variable Selection Chapter 10 Decomposition of Data Matrices by Factors Chapter 11 Principal Components Analysis Chapter 12 Factor Analysis Chapter 13 Cluster Analysis Chapter 14 Discriminant Analysis Chapter 15 Correspondence Analysis Chapter 16 Canonical Correlation Analysis Chapter 17 Multidimensional Scaling Chapter 18 Conjoint Measurement Analysis Chapter 19 Applications in Finance Chapter 20 Computationally Intensive Techniques Part IV Appendix Chapter 21 Symbols and Notations Chapter 22 Data

Applied Numerical Methods with MATLAB for Engineers & Scientists, Chapra, MG, 2011. 很好的matlab学习教材，英文版的

量子计算：一种应用方法 Jack Hidary教科书“量子计算：一种应用方法”的解决方案。 通过提出拉取请求，随时指出解决方案中的任何错误或错误。

Empirical Modeling and Data Analysis for Engineers and Applied Scientists English | 25 July 2016 | ISBN: 3319327674 | 264 Pages This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and “applied science” is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as “Statistics for Engineers and Scientists” without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models – predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.