MATLAB Data Analysis Methods

Name: MATLAB Data Analysis Methods
ISBN: 9787111362876

Li Bainian / Wu LibinChina Machine Press2012

4.8

Word count: 352K words
Chapters: 27
Series: MATLAB Data Analysis Methods is one of the "Computer Course Planning Textbooks for Regular Higher Education Institutions" from China Machine Press, written by Li Bainian, Wu Libin, and others of the School of Statistics and Applied Mathematics at Anhui University of Finance and Economics. Using MATLAB as its practical platform, the book integrates the theory, algorithms, and programming implementation of data analysis, so that readers can both understand the principles of statistical methods and write runnable analysis programs themselves. The book has seven chapters: Chapter 1 introduces MATLAB basics—operations, variables and functions, matrix and array computation, M-files, and flow control—laying the groundwork for later methods; Chapter 2 covers descriptive data analysis, including basic statistics, sample data visualization, distribution tests, and data transformation methods such as the Box-Cox transformation, along with comprehensive evaluation models; Chapter 3 covers regression analysis—simple linear and nonlinear regression, multiple linear regression, and stepwise regression; Chapter 4 covers discriminant analysis—distance discrimination, Bayesian discrimination, and evaluation of discriminant criteria; Chapter 5 covers principal component analysis and canonical correlation analysis; Chapter 6 covers cluster analysis; and Chapter 7 covers numerical simulation analysis. Each chapter includes exercises and comprehensive, analytical experiments tied to real applications, emphasizing the teaching approach of "concepts and theory first, MATLAB solutions grounded in practice." The book is aimed primarily at undergraduates in computer science and technology, information and computing science, statistics, and related fields; it can also serve as a textbook for elective courses in related majors and as a reference for master's students and scientific and technical workers.
ISBN: 9787111362876

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Summary

This is an introductory data analysis textbook that is "sufficient without being heavy." It neither makes statistical theory abstruse nor turns MATLAB into a command manual; instead it breaks seven commonly used data analysis methods—descriptive analysis, regression, discrimination, principal components and canonical correlation, clustering, and numerical simulation—into four stages: principle, algorithm, code, and example, so readers can run results by following along. Several strengths: first, the structure is clear, with chapter order broadly matching a semester's course schedule—the first two chapters lay the foundation and later ones progress by method, convenient for both teaching and self-study; second, code and cases are equally emphasized, with comprehensive experiments at the end of each chapter so readers can verify each method immediately; third, the authors have long taught statistics and MATLAB, and the examples are close to real data problems rather than pure formula drills divorced from context. The first edition was adopted as a textbook or teaching reference by many universities and has a good reputation among readers. One caution: the examples are based on an earlier version of MATLAB (the MATLAB 7.0 interface and corresponding functions), and some function behavior and recommended practices have changed in newer versions; readers should note version differences and consult official documentation when necessary. For learners who want to approach statistical methods and programming implementation together, this book is a cost-effective starting point.

Editor's review

这是一本“够用而不厚重”的数据分析入门教材。它没有把统计理论写得玄奥，也没有把 MATLAB 讲成命令手册，而是把七类常用数据分析方法——描述性分析、回归、判别、主成分与典型相关、聚类和数值模拟——逐一拆成“原理—算法—代码—实例”四段，读者照着做就能跑出结果。本书的几个好处：一是结构清晰，章节顺序基本对应一门学期的课程安排，前两章打底，后面按方法递进，方便教学也方便自学；二是代码与案例并重，每章末尾都配有综合实验，读完一个方法马上能动手验证；三是作者长期从事统计与 MATLAB 教学，例题设置贴近实际数据问题，不搞脱离场景的纯公式演练。第 1 版曾被多所高校选用为教材或教学参考书，在读者中口碑良好。需要提醒的是，书中示例基于较早版本的 MATLAB（MATLAB 7.0 界面及相应函数），新版 MATLAB 的部分函数行为与推荐写法已有变化，建议读者在使用时留意版本差异，必要时查阅官方文档。对于想从统计方法和编程实现两条线同时入手的学习者，这本书是一个性价比很高的起点。

Who it's for

A good fit for

计算机、统计、信息与计算科学等专业本科生，作为数据分析与 MATLAB 课程教材或配套读物
需要把回归、判别、聚类等经典统计方法用 MATLAB 实现的研究生与科研人员
有基本数学基础、希望系统补齐数据分析方法并动手编程的自学者
做课程设计、毕业设计或数学建模、需要现成方法与代码模板的读者

Not a good fit for

完全没有统计与线性代数基础、希望跳过理论直接套代码的零基础读者
关注深度学习、大数据分布式计算等现代工程实践、希望学习最新工具链的从业者
需要紧跟新版 MATLAB 语法与函数接口、把本书当作命令速查手册的读者

Reading tips

先通读第 1 章并动手敲一遍示例，确保矩阵运算与 M 文件流程控制过关，再进入后续方法章节。
每章按“读原理—抄代码—换自己的数据重跑—解释结果”的顺序推进，实验题不要跳过。
留意书中基于较早版本 MATLAB 的函数写法，遇到报错或警告先查官方文档，理解新版本的替代写法。
可按需抽取章节阅读：做预测重点看第 3、4、7 章，做降维与分类重点看第 5、6 章。

About the publisher

China Machine Press China · Founded in 1950

In addition to science and technology and professional publishing, the books in our catalog from China Machine Press also reflect a sustained focus on psychology, computing, management, finance and economics, and social sciences. In psychology, the catalog includes works by C. G. Jung and introductions to analytical psychology, as well as applied topics such as stress and health, and addiction and healing. In computing, it features Structure and Interpretation of Computer Programs (JavaScript Edition). Management and finance titles include Administrative Behavior (4th Edition), The Price of Inequality (Collector's Edition), and This Time Is Different: Eight Centuries of Financial Folly (Commemorative Edition). In the philosophy and history of science, there is The Worldview: The Third Edition. Relevant authors include C. G. Jung, William McGuire, Sonu Shamdasani, and Gabor Maté. Public information is limited; the above summarizes the publisher's profile based solely on the books in our catalog.

Preface
Teaching Suggestions
Chapter 1 MATLAB Basics
1.1 Data Analysis and MATLAB
1.2 Introduction to MATLAB
1.3 Variables and Functions
1.4 Matrices and Their Operations
1.5 M-Files and Programming
1.6 Examples of Common MATLAB Operations
Chapter 2 Descriptive Data Analysis
2.1 Basic Statistics and Data Visualization
2.2 Data Distribution and Tests
2.3 Data Transformation
Experiment 1 Data Statistics and Distribution Tests
Chapter 3 Regression Analysis
3.1 Simple Regression Models
3.2 Multiple Linear Regression Models
3.3 Stepwise Regression
Experiment 2 Multiple Linear Regression and Stepwise Regression
Chapter 4 Discriminant Analysis
4.1 Distance Discriminant Analysis
4.2 Evaluation of Discriminant Criteria
4.3 Bayesian Discriminant Analysis
Experiment 3 Distance and Bayesian Discriminant Analysis
Chapter 5 Principal Component Analysis and Canonical Correlation Analysis
Chapter 6 Cluster Analysis
Chapter 7 Numerical Simulation Analysis

FAQ

这本书第 1 版和第 2 版有什么区别？

第 1 版 2012 年出版，ISBN 9787111362876，共 7 章、约 35.2 万字。第 2 版（2017 年，ISBN 9787111558507）在保持原框架的基础上更新了例题数据、补充了神经网络模式识别与预测的内容，并按新版 MATLAB 优化了部分程序。选购时建议先看清版次。

书中的程序能在新版 MATLAB 上运行吗？

书中示例主要基于较早版本的 MATLAB 编写与验证，大部分基础统计函数仍然可用，但界面说明、部分函数参数与推荐写法已有变化。运行报错时，建议对照官方文档调整参数或改用新版推荐函数，核心方法逻辑不受影响。

阅读本书需要什么数学基础？

需要了解基本的概率统计（均值、方差、分布检验）和线性代数（矩阵运算、特征值）知识，再加上一点微积分常识即可。书中对方法原理有讲解，但不会从零补数学课，基础薄弱者可配合一本统计学教材同步阅读。

这本书适合作为考研或课程教材使用吗？

可以。全书 7 章结构完整、每章配有习题和综合实验，与多所高校的数据分析、MATLAB 与数学实验类课程安排较为契合，也曾被不少院校选为教材或教学参考书。是否适配具体课程，建议对照任课教师的考纲与章节安排。

书中包含神经网络内容吗？

第 1 版以描述性分析、回归、判别、主成分与典型相关、聚类、数值模拟为主；第 2 版新增了应用神经网络进行模式识别与预测的内容。若你关注神经网络部分，请确认自己拿到的是第 2 版。

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