October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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158
｜
第
4
章
本节要点
•
回归中的离群点是残差较大的记录。
•
在拟合回归方程时，多重共线性会导致数值不稳定。
•
混淆变量是一种被模型忽略的重要预测变量，忽略它会使回归方程呈现虚假关系。
•
如果一个变量的效果依赖于另一个变量的水平或数值，就需要使用两个变量之间的
交互项。
•
多项式回归可以拟合预测变量与结果变量之间的非线性关系。
•
样条是一系列以节点连接在一起的多项式分段。
•
可以使用广义可加模型（
GAM
）自动地在样条回归中指定节点。
4.7.4
扩展阅读
•
如果想了解更多关于样条模型和
GAM
的内容，可以看一下
Trevor Hastie
、
Robert
Tibshirani
和
Jerome Friedman
合著的
《统计学习基础：数据挖掘、推理与预测（第
2
版 ）》，
以及其基于
R
的姊妹篇—
—
Gareth James
、
Daniela Witten
、
Trevor Hastie
和
Robert
Tibshirani
合著的《统计学习导论：基于
R
应用》
。
•
要想学习更多关于使用回归模型进行时间序列预测的知识，可以参考
Galit
Shmueli
和
Kenneth Lichtendahl
合著的
Practical Time Series Forecasting with R
。
4.8
小结
近年来，可能没有任何一种统计方法比回归应用得更广泛。回归是在多个预测变量和一个
结果变量之间建立关系的一种过程。回归的基本形式是线性回归：每个预测变量都有一个
系数，它描述了该预测变量与结果变量之间的线性关系。回归还有很多高级的形式 ...
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