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Jingdong book

R语言与统计分析

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Jingdong book

R语言与统计分析

Name: R语言与统计分析
Brand: Jingdong book
SKU: 3118023761
Price: 41.41 USD
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通过《R语言与统计分析》，读者不仅可以快速学会R的基本原理与核心内容，而且可以根据书中的例子与例子中的R程序学会解决问题的统计计算方法与基本的编程技术，为解决更为复杂的统计问题奠定扎实的基础。

Content Description

本书以数据的常用统计分析方法为基础，在简明扼要地阐述统计学基本概念、基本思想与基本方法的基础上，讲述与之相对应的日函数的实现，并通过具体的例子说明统计问题求解的过程。
本书注重统计的思想性、方法的实用性和计算的可操作性。在内容的安排上不仅包含了基础统计分析中的探索性数据分析、参数估计与假设检验，还包括了非参数统计分析的常用方法、多元统计分析方法及贝叶斯统计分析方法。每一部分都通过具体例子重点讲述解决问题的思想、方法和在日中的实现过程。通过本书，读者不仅可以快速学会日的基本原理与核心内容，而且可以根据书中的例子与例子中的日程序学会解决问题的统计计算方法与基本的编程技术，为解决更为复杂的统计问题奠定扎实的基础。

Catalogue

第一章 R介绍
1.1 S语言与R
1.2 R的特点
1.3 R的资源
1.4 R的安装与运行
1.4.1 R软件的安装、启动与关闭
1.4.2 R程序包的安装与使用
第一章习题

第二章 R的基本原理与核心
2.1 R的基本原理
2.2 R的在线帮助
2.3 一个简短的R会话
2.4 R的数据结构
2.4.1 R的对象与属性
2.4.2 浏览对象的信息
2.4.3 向量的建立
2.4.4 数组与矩阵的建立
2.4.5 数据框（dataframe）的建立
2.4.6 列表（1ist）的建立
2.4.7 时间序列（ts）的建立
2.5 数据的存储与读取
2.5.1 数据的存储
2.5.2 数据的读取
2.6 R的图形功能
2.6.1 绘图函数
2.6.2 低级绘图命令
2.6.3 绘图参数
2.6.4 一个实例
2.7 R.编程
2.7.1 循环和向量化
2.7.2 用R写程序
2.7.3 编写你自己的函数
2.7.4 养成良好的编程习惯
第二章习题

第三章概率与分布
3.1 随机抽样
3.2 排列组合与概率的计算
3.3 概率分布
3.3.1 离散分布的分布律
3.3.2 连续分布的密度函数
3.4 R中内嵌的分布
3.5 应用：中心极限定理
3.5.1 中心极限定理
3.5.2 渐近正态性的图形检验
3.5.3 举例
第三章习题

第四章探索性数据分析
4.1 常用分布的概率函数图
4.2 直方图与密度函数的估计
4.2.1 直方图
4.2.2 核密度估计
4.3 单组数据的描述性统计分析
4.3.1 单组数据的图形描述
4.3.2 单组数据的描述性统计
4.4 多组数据的描述性统计分析
4.4.1 两组数据的图形概括
4.4.2 多组数据的图形描述
4.4.3 多组数据的描述性统计
4.4.4 分组数据的图形概括
4.5 分类数据的描述性统计分析
4.5.1 列联表的制作
4.5.2 列联表的图形描述
第四章习题

第五章参数估计
5.1 矩法估计和极大似然估计
5.1.1 矩法估计
5.1.2 极大似然估计
5.2 单正态总体参数的区间估计
5.2.1 均值μ的区间估计
5.2.2 方差σ2的区间估计
5.3 两正态总体参数的区间估计
5.3.1 均值差μ1-μ2的置信区间
5.3.2 两方差比σ12／22的置信区间
5.4 单总体比率p的区间估计
5.5 两总体比率差p1-p2的区间估计
5.6 样本容量的确定
5.6.1 估计正态总体均值时样本容量的确定
5.6.2 估计比例p时样本容量的确定
第五章习题

第六章参数的假设检验
6.1 假设检验与检验的p值
6.1.1 假设检验的概念与步骤
6.1.2 检验的p值
6.2 单正态总体参数的检验
6.2.1 均值μ的假设检验
6.2.2 方差盯σ2的检验：x2检验
6.3 两正态总体参数的检验
6.3.1 均值的比较：t检验
6.3.2 方差的比较：F检验
6.4 成对数据的t检验
6.5 单样本比率的检验
6.5.1 比率p的精确检验
6.5.2 比率p的近似检验
6.6 两样本比率的检验
第六章习题

第七章非参数的假设检验
7.1 单总体位置参数的检验
7.1.1 中位数的符号检验
7.1.2 Wilcoxon符号秩检验
7.2 分布的一致性检验：x2检验
7.3 两总体的比较与检验
7.3.1 X2独立性检验
7.3.2 Fisher精确检验
7.3.3 Wilcoxon秩和检验法和Mann-whitneyU检验
7.3.4 Mood检验
7.4 多总体的比较与检验
7.4.1 位置参数的Kruskal-Wallis秩和检验
7.4.2 尺度参数的Ansari-Bradley检验
7.4.3 尺度参数的Fligner-Killeen检验
第七章习题

第八章方差分析
8.1 单因子方差分析
8.1.1 数学模型
8.1.2 均值的多重比较
8.1.3 同时置信区间：Tukey法
8.1.4 方差齐性检验
……
第九章回归分析与相关分析
第十章多元统计分析介绍
第十一章贝叶斯统计分析

Introduction

统计学的任务是研究有关收集、整理、分析数据，从而对所考察的问题作出统计推断。作为一门科学，统计学有其坚实的理论基础，研究统计学方法的理论基础问题的那一部分，构成了所谓数理统计学的内容。其次，统计学就其本质来讲，是一门实用性很强的科学，它在人类活动的各个领域有着广泛的应用。因此数理统计的理论与方法应该与实际相结合，解决社会、经济、工农业生产、生物制药、航空航天、质量管理、环境资源等领域中的各种问题。最后，统计学又是一门技术性很强的科学，由于所研究的问题越来越复杂、变量之间关联性越来越强、数据的规模越来越大，使得原有的计算方法无法顺利实现。现在，随着计算机的不断发展与普及，特别是近20年来统计计算的突破性进展及统计软件的不断完善和成熟，使得解决这些问题不仅成为可能，而且越来越容易、快速。
目前许多大学中几乎所有理工科、甚至文科的许多专业都开设了《数理统计》或《应用统计》之类的课程，有的还编写了相应的教材，这是可喜的。这些课程与教材的共同特点是以较大的篇幅介绍数理统计的理论、方法与实际背景，并配有一定数量的例子和习题。部分学校还为有统计专业和应用数学专业的学生开设sAs或MATLAB统计软件课程，为经济统计专业的学生开设SPSS或Eviews统计软件课程，但这还远远不够。
作者长期从事概率论与数理统计、统计计算及统计软件的教学工作，我们发现目前的统计教学普遍存在的问题有：一、关于教学内容：在有限的课时中，对于非统计专业的学生采用针对统计专业学生的教学方式，过多强调理论的重要性，从而忽视了统计思想和数据处理能力的培养。

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