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Jingdong book

清华管理学系列英文版教材:实用多元统计分析(第6版)

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清华管理学系列英文版教材:实用多元统计分析(第6版)

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本书是学习掌握多元统计分析的各种模型和方法的一本有价值的参考书:首先,它做到了“浅入深出”,既可供初学者入门,又能使有较深基础的人受益;其次,它既侧重于应用,又兼顾必要的推理论证,使学习者既能学到“如何”做,又能在一定程度上了解“为什么”这样做;最后,它内涵丰富、全面,不仅基本包括各种在实际中常用的多元统计分析方法,而且对现代统计学的最新思想和进展有所介绍。值得一提的是,本书中有大量来自实际问题的数据实例,通过对这些实例的分析,读者可以学到如何将一个实际问题转化为恰当的统计问题,进而选择恰当的方法来进行分析。
Content Description

多元统计分析是统计学中内容十分丰富、应用范围极为广泛的一个分支。在自然科学和社会科学的许多学科中,研究者都有可能需要分析处理有多个变量的数据的问题。能否从表面上看起来杂乱无章的数据中发现和提炼出规律性的结论,不仅需要对所研究的专业领域有很好的训练,而且要掌握必要的统计分析工具。
对研究者来说,本书是学习掌握多元统计分析的各种模型和方法的一本有价值的参考书:首先,它做到了“浅入深出”,既可供初学者入门,又能使有较深基础的人受益;其次,它既侧重于应用,又兼顾必要的推理论证,使学习者既能学到“如何”做,又能在一定程度上了解“为什么” 这样做;最后,它内涵丰富、全面,不仅基本包括各种在实际中常用的多元统计分析方法,而且对现代统计学的最新思想和进展有所介绍。值得一提的是,本书中有大量来自实际问题的数据实例,通过对这些实例的分析,读者可以学到如何将一个实际问题转化为恰当的统计问题,进而选择恰当的方法来进行分析。
Catalogue

第1章 多元分析概述
1.1 引言
1.2 多元方法的应用
1.3 数据的组织
1.4 数据的展示及图表示
1.5 距离
1.6 最终评注
第2章 矩阵代数与随机向量
2.1 引言
2.2 矩阵和向量代数基础
2.3 正定矩阵
2.4 平方根矩阵
2.5 随机向量和矩阵
2.6 均值向量和协方差矩阵
2.7 矩阵不等式和极大化
补充2A 向量与矩阵:基本概念
第3章 样本几何与随机抽样
3.1 引言
3.2 样本几何
3.3 随机样本以及样本均值和协方差矩阵的期望值
3.4 广义方差
3.5 作为矩阵运算的样本均值、协方差与相关系数
3.6 变量的线性组合的样本值
第4章 多元正态分布
4.1 引言
4.2 多元正态密度及其性质
4.3 从多元正态分布抽样与极大似然估计
4.4 X和s的抽样分布
4.5 X和S的大样本特性
4.6 评估正态性假设
4.7 搜寻离群值与“清洁”数据
4.8 变换到接近正态性
第5章 关于均值向量的推断
5.1 引言
5.2 U0作为正态总体均值的似真性
5.3 霍特林r的平方与似然比检验
5.4 置信域和均值分量的联合比较
5.5 总体均值向量的大样本推断
5.6 多元质量控制图
5.7 观测值缺损时均值向量的推断
5.8 多元观测中由时间相依性造成的困难
补充5A 作为p维椭球投影的联合置信区间与置信椭圆
第6章 多个多元均值向量的比较
6.1 引言
6.2 成对比较与重复测量设计
6.3 两总体均值向量的比较
6.4 多个多元总体均值向量的比较(单因子MANOVA)
6.5 处理效应的联合置信区间
6.6 协方差矩阵相等性的检验
6.7 双因子多元方差分析
6.8 轮廓分析
6.9 重复测量设计和生长曲线
6.10 对分析多元模型的透视和建议
第7章 多元线性回归模型
7.1 引言
7.2 经典线性回归模型
7.3 最小二乘估计
7.4 回归模型的推断
7.5 由估计的回归函数作推断
7.6 模型检查及回归中的其他问题
7.7 多元多重回归
7.8 线性回归的概念
7.9 比较回归模型的两种表述方式
7.10 有时间相关误差的多重回归模型
补充7A 多元多重回归模型的似然比的分布
第8章 主成分
8.1 引言
8.2 总体主成分
8.3 综合主成分的样本变差
8.4 主成分的图形表示
8.5 大样本推断
8.6 用主成分监控质量
补充8A 样本主成分近似的几何意义
第9章 因子分析与对协方差矩阵结构的推断
9.1 引言
9.2 正交因子模型
9.3 估计方法
9.4 因子旋转
9.5 因子得分
9.6 因子分析展望和建议
补充9A 最大似然估计的某些计算细节
第10章 典型相关分析
10.1 引言
10.2 典型变量和典型相关系数
10.3 总体典型变量的解释
10.4 样本典型变量和样本典型相关系数
10.5 其他样本描述性度量
10.6 大样本推断
第11章 判别与分类
11.1 引言
11.2 两个总体的分离与分类
11.3 两个多元正态总体的分类
11.4 评估分类函数
11.5 多个总体的分类
11.6 对多个总体进行判别的费希尔方法
11.7 Logistic回归与分类
11.8 最后的评述
第12章 聚类、距离方法与多维标度变换
12.1 引言
12.2 相似性量度
12.3 分层聚类方法
12.4 非分层聚类方法
12.5 基于统计模型的聚类
12.6 多维标度变换
12.7 对应分析
12.8 用于观察抽样单元和变量的双重信息图
12.9 普罗克鲁斯特斯分析:一种比较点结构的方法
附录
数据索引
主题索引

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