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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回归与预测
｜
143
上与
R
很相似，可以使用一个公式接口来定义模型：
model = smf.ols(formula='AdjSalePrice ~ SqFtTotLiving*ZipGroup + SqFtLot + ' +
'Bathrooms + Bedrooms + BldgGrade + PropertyType', data=house)
results = model.fit()
results.summary()
statsmodels
包可以处理分类变量（如
ZipGroup[T.1]
、
PropertyType[T.Single Family]
）
和交互项（如
SqFtTotLiving:ZipGroup[T.1]
）。
房屋位置与房屋面积似乎有很强的交互作用。对于
ZipGroup
最低的房屋，它的斜率
与主效应
SqFtTotLiving
的斜率相同，都是每平方英尺
118
美元（这是因为
R
为因
子变量使用了参照编码，参见
4.4
节
）。对于
ZipGroup
最高的房屋，斜率是主效应与
SqFtTotLiving:ZipGroup5
之和，即每平方英尺
$115 + $227
=
$342
。换言之，与增加一平
方英尺的平均提升效果相比，在价格最高的邮政编码分组中增加一平方英尺，对预测销售
价格的提升几乎是前者的三倍。
带交互项的模型选择
在涉及多个变量的问题中，要确定在模型中包含哪种交互项是非常困难的。
通常有以下几种做法。
•
在有些问题中，先验知识和直觉可以作为指导，来确定选择哪种交互项放
在模型中。
•
可以使用逐步选择方法（参见 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.