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回归分析(修订版) 教材;社会学;数理社会学

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[美]谢宇   社会科学文献出版社  2013-03 出版
ISBN:978-7-5097-4289-1

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本书源于作者多年教授回归分析的课程讲义。从基本的统计概念讲起,对线性回归分析的基本假定、回归中的统计推论和回归诊断做了详尽的介绍,同时还含盖了对很多在社会科学中实际研究非常有用的内容,包括虚拟变量、交互作用、辅助回归、多项式回归、样条函数(spline function)回归和阶跃函数(step function)回归等。此外,本书还涉及到通径分析、纵贯数据模型、多层线性模型和logit模型等作为基本线性回归分析扩展和延伸。
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  • 序言
  • 第1章 基本统计概念
    1. 1.1 统计思想对于社会科学研究的重要性
    2. 1.2 本书的特点
    3. 1.3 基本统计概念
    4. 1.4 随机变量的和与差
    5. 1.5 期望与协方差的性质
    6. 1.6 本章小结
  • 第2章 统计推断基础
    1. 2.1 分布
    2. 2.2 估计
    3. 2.3 假设检验
    4. 2.4 本章小结
  • 第3章 一元线性回归
    1. 3.1 理解回归概念的三种视角
    2. 3.2 回归模型
    3. 3.3 回归直线的拟合优度
    4. 3.4 假设检验
    5. 3.5 对特定X下Y均值的估计
    6. 3.6 对特定X下Y单一值的预测
    7. 3.7 简单线性回归中的非线性变换
    8. 3.8 实例分析
    9. 3.9 本章小结
  • 第4章 线性代数基础
    1. 4.1 定义
    2. 4.2 矩阵的运算
    3. 4.3 特殊矩阵
    4. 4.4 矩阵的秩
    5. 4.5 矩阵的逆
    6. 4.6 行列式
    7. 4.7 矩阵的运算法则
    8. 4.8 向量的期望和协方差阵的介绍
    9. 4.9 矩阵在社会科学中的应用
    10. 4.10 本章小结
  • 第5章 多元线性回归
    1. 5.1 多元线性回归模型的矩阵形式
    2. 5.2 多元回归的基本假定
    3. 5.3 多元回归参数的估计
    4. 5.4 OLS回归方程的解读
    5. 5.5 多元回归模型误差方差的估计
    6. 5.6 多元回归参数估计量方差的估计
    7. 5.7 模型设定中的一些问题
    8. 5.8 标准化回归模型
    9. 5.9 CHIP88实例分析
    10. 5.10 本章小结
  • 第6章 多元回归中的统计推断与假设检验
    1. 6.1 统计推断基本原理简要回顾
    2. 6.2 统计显著性的相对性,以及效应幅度
    3. 6.3 单个回归系数βk=0的检验
    4. 6.4 多个回归系数的联合检验
    5. 6.5 回归系数线性组合的检验
    6. 6.6 本章小结
  • 第7章 方差分析和F检验
    1. 7.1 一元线性回归中的方差分析
    2. 7.2 多元线性回归中的方差分析
    3. 7.3 方差分析的假定条件
    4. 7.4 F检验
    5. 7.5 判定系数增量
    6. 7.6 拟合优度的测量
    7. 7.7 实例分析
    8. 7.8 本章小结
  • 第8章 辅助回归和偏回归图
    1. 8.1 回归分析中的两个常见问题
    2. 8.2 辅助回归
    3. 8.3 变量的对中
    4. 8.4 偏回归图
    5. 8.5 排除忽略变量偏误的方法
    6. 8.6 应用举例
    7. 8.7 本章小结
  • 第9章 因果推断和路径分析
    1. 9.1 相关关系
    2. 9.2 因果推断
    3. 9.3 因果推断的问题
    4. 9.4 因果推断的假设
    5. 9.5 因果推断中的原因
    6. 9.6 路径分析
    7. 9.7 本章小结
  • 第10章 多重共线性问题
    1. 10.1 多重共线性问题的引入
    2. 10.2 完全多重共线性
    3. 10.3 近似多重共线性
    4. 10.4 多重共线性的度量
    5. 10.5 多重共线性问题的处理
    6. 10.6 本章小结
  • 第11章 多项式回归、样条函数回归和阶跃函数回归
    1. 11.1 多项式回归
    2. 11.2 样条函数回归
    3. 11.3 阶跃函数回归
    4. 11.4 本章小结
  • 第12章 虚拟变量与名义自变量
    1. 12.1 名义变量的定义与特性
    2. 12.2 虚拟变量的设置
    3. 12.3 虚拟变量的应用
    4. 12.4 本章小结
  • 第13章 交互项
    1. 13.1 交互项
    2. 13.2 由不同类型解释变量构造的交互项
    3. 13.3 利用嵌套模型检验交互项的存在
    4. 13.4 是否可以删去交互项中的低次项?
    5. 13.5 构造交互项时需要注意的问题
    6. 13.6 本章小结
  • 第14章 异方差与广义最小二乘法
    1. 14.1 异方差
    2. 14.2 异方差现象举例
    3. 14.3 异方差情况下的常规最小二乘估计
    4. 14.4 广义最小二乘法
    5. 14.5 加权最小二乘法
    6. 14.6 本章小结
    7. 附录:加权最小二乘回归使用的Stata代码
  • 第15章 纵贯数据的分析
    1. 15.1 追踪数据的分析
    2. 15.2 趋势分析
    3. 15.3 本章小结
  • 第16章 多层线性模型介绍
    1. 16.1 多层线性模型发展的背景
    2. 16.2 多层线性模型的基本原理
    3. 16.3 模型的优势与局限
    4. 16.4 多层线性模型的若干子模型
    5. 16.5 自变量对中的问题
    6. 16.6 应用举例
    7. 16.7 本章小结
  • 第17章 回归诊断
    1. 17.1 因变量是否服从正态分布
    2. 17.2 残差是否服从正态分布
    3. 17.3 异常观测案例
    4. 17.4 本章小结
  • 第18章 二分因变量的logit模型
    1. 18.1 线性回归面对二分因变量的困境
    2. 18.2 转换的方式
    3. 18.3 潜变量方式
    4. 18.4 模型估计、评价与比较
    5. 18.5 模型回归系数解释
    6. 18.6 统计检验与推断
    7. 18.7 本章小结
  • 词汇表
  • 后记
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