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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130
｜
第
4
章
4.2.5
加权回归
统计学家使用加权回归的目的多种多样。加权回归对于复杂任务的分析尤其重要。数据科
学家会发现加权回归在两种情况下非常有用。
•
对以不同精度进行测量的观测进行逆方差加权，方差大的观测得到较低的权重。
•
分析一行中有多个个案的数据，权重变量表示每行中有多少原始观测。
以房屋数据为例，与近期的销售数据相比，较早的销售数据的可靠性较低。要使用
DocumentDate
变量确定销售的年份，可以使用
2005
（数据开始记录的年份）之后的年份的
数量作为
Weight
。
R
代码：
library(lubridate)
house$Year = year(house$DocumentDate)
house$Weight = house$Year - 2005
Python
代码：
house['Year'] = [int(date.split('-')[0]) for date in house.DocumentDate]
house['Weight'] = house.Year - 2005
可以通过使用了
weight
参数的
lm
函数来计算加权回归。
house_wt <- lm(AdjSalePrice ~ SqFtTotLiving + SqFtLot + Bathrooms +
Bedrooms + BldgGrade,
data=house, weight=Weight)
round(cbind(house_lm=house_lm$coefficients,
house_wt=house_wt$coefficients), ...
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