《统计与金融》内容涉及金融学与统计学的诸多内容，与一般偏重于单纯介绍理论知识和模型的著作不同，它把统计模型和金融模型联系在一起，寓统计学知识于金融学之中，并且用各种软件做出了完美的应用程序。《统计与金融》的主要特点是： 运用金融中的例子，阐明概率与统计的主要原理。 帮助读者理解以经验为依据的研究方法在金融和运筹学中是如何运用的。 介绍了新的统计方法，例如时间序列、GARCH模型、再抽样以及非参数回归。 提供了一些运用MATLAB和SAS软件包的例子。

心理统计导论（第9版），ISBN：9787115219992，作者：（美） 理查德`鲁尼恩 等著，林丰勋 译

R is rapidly becoming the standard for developing statistical software, and R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R.

This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison''s Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, it''s easy to: * produce handsome, publication-quality plots, with automatic legends created from the plot specification * superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales * add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression * save any ggplot2 plot (or part thereof) for later modification or reuse * create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots * approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and you''ll learn everything you need in the book. After reading this book you''ll be able to produce graphics customized precisely for your problems, and you''ll find it easy to get graphics out of your head and on to the screen or page.

统计作为数据分析的一种通用语言，为使用者提供了一套获取数据、分析数据并从数据中得出结论的原则和方法。本书内容包括描述统计、推断统计、多元统计和非参数统计等主要方法。写法上完全立足于统计应用，力求通俗易懂，每种方法都从实际问题入手进行讨论，尽可能避免统计公式的推导。书中例题的解答结合使用了Excel和SPSS两个软件，并给出每种方法的详细操作步骤，使读者能轻松完成统计计算。本书可作为高等院校经济管理类专业本科生统计学课程的教材使用，也可作为研究生和MBA的教材或参考书，对广大实际工作者也极具参考价值。

《应用线性回归模型(第4版影印版)》从McGrawHill出版公司引进，共分三部分，内容包括：第一部分：简单线性回归：一元预测函数的线性回归，回归影响和相关分析，诊断及补救措施，即时推断和回归分析的其它几个专题，简单线性回归分析中的矩阵方法；第二部分：多元线性回归：多元回归Ⅰ，多元回归2，定性回归模型和定量预测，建立线性回归模型Ⅰ：模型选择及有效性，建立线性回归模型Ⅱ：诊断，建立线性回归模型Ⅲ：补救措施，时间序列数据中的自相关；第三部分：非线性回归：非线性回归和神经网络方法。《应用线性回归模型(第4版影印版)》篇幅适中，例子多涉及各个应用领域，在介绍统计思想方面比较突出，光盘数据丰富。《应用线性回归模型(第4版影印版)》适用于高等院校统计学专业和理工科各专业本科生和研究生作为教材使用。

《统计自然语言处理基础：国外计算机科学教材系列》是一本全面系统地介绍统计自然语言处理技术的专著，被国内外许多所著名大学选为计算语言学相关课程的教材。《统计自然语言处理基础：国外计算机科学教材系列》涵盖的内容十分广泛，分为四个部分，共16章，包括了构建自然语言处理软件工具将用到的几乎所有理论和算法。全书的论述过程由浅入深，从数学基础到精确的理论算法，从简单的词法分析到复杂的语法分析，适合不同水平的读者群的需求。同时，《统计自然语言处理基础：国外计算机科学教材系列》将理论与实践紧密联系在一起，在介绍理论知识的基础上给出了自然语言处理技术的高层应用（如信息检索等）。在《统计自然语言处理基础：国外计算机科学教材系列》的配套网站上提供了许多相关资源和工具，便于读者结合书中习题，在实践中获得提高。近年来，自然语言处理中的统计学方法已经逐渐成为主流。

贝叶斯方法是基于贝叶斯定理而发展起来用于系统地阐述和解决统计问题的方渚上用贝叶斯定理的方式依赖于一个统计学家如何看待“概率”的基本概念。这些统计学家“推崇”概率为不确定性的度量，对于任何实际客体它将会被认定；因此没有任何理由贝叶斯定理不在任何场合应用。本书的主要内容包括贝叶斯立场、先验分布，后验分布及贝叶斯推断、常用分布、可靠性问题、经验贝叶方法、贝叶斯统计地应用、参考文献。

The resources that statisticians use directly affect people, government and society. Opinion polls influence political opinion; statistical evidence supports medical advances. In the sixth edition of his groundbreaking text, David Moore emphasizes the concepts and applications of statistics from a wide range of fields - encouraging students to see the meaning behind statistical results whilst retaining their interest. Moore's emphasis on ideas and data with minimal computation is generally acknowledged as the most effective way to teach non-mathematical students.

统计学是论述收集. 分析并解释数

Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

《多元统计分析及R语言建模（第2版）》共分14章，主要内容有：多元数据的收集和整理、多元数据的直观显示、线性与非线性模型及广义线性模型、判别分析、聚类分析、主成分分析、因子分析、对应分析、典型相关分析等常见的主流方法。《多元统计分析及R语言建模（第2版）》还参考国内外大量文献，系统地介绍了这些年在经济管理等领域应用颇广的一些较新方法，可作为统计学专业本科生和研究生的多元分析课程教材。

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

《商务统计轻松学》系统地阐述了统计学的基本知识，通俗易懂，深入浅出，理论与实践相结合。所有的统计学基本概念都以简单明了的语言进行定义，并配以实例和说明进行阐释，使读者对概念一目了然，书中编排了丰富的实例和习题，其小“问题详解”不仅是统计理论的简单应用，还为读者指明了相应的解题思路和方法，并突出了如何使用Microsoft Excel工作表与统计计算器来解决统计问题，此外，《商务统计轻松学》还配有大量的网上资源和习题供读者下载练习使用。《商务统计轻松学》是那些畏惧统计学的人和在日常生活中将会用到统计学的人的最佳读本。 读者对象：大众。

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

《试验应用统计·设计、创新和发现(原书第2版)》从试验工作者的角度阐述了统计方法在试验设计中的应用，强调科学地利用统计工作从试验数据中获取最大的信息。内容主要包括：基础知识、比较两总体、两水平因析设计、部分因析设计、因析设计及数据变换、变差的多种来源、最小二乘与试验设计的必要性、响应曲面的某些应用等。 《试验应用统计·设计、创新和发现(原书第2版)》内容丰富，从实际问题出发，分析各种方法的利弊，然后采用最佳统计方法解决问题，《试验应用统计·设计、创新和发现(原书第2版)》适合作为理工科各专业本科生，研究生的统计学教材，也可作为相关领域研究人员的参考读物。

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

统计计算是数理统计、计算数学和计算机科学的交叉学科。《统计计算》系统地介绍了统计计算的基本方法，并给出各种算法的统计原理和数值计算的步骤，以及部分例子，使读者掌握用统计方法解决具体问题的全过程. 《统计计算》内容包括误差与数据处理、分布函数和分位数的计算、随机数的产生与检验、矩阵计算、无约束最优化方法、多元线性和非线性回归的算法及随机模拟方法等.各章内容丰富，并配有适量的习题和上机实习题. 《统计计算》可作为理工科院校概率统计、数学、应用数学、计算机科学等系大学生的教材，也可作为教师、研究生以及从事统计、信息处理工作的有关工程技术人员的参考书。