数据科学的高级线性模型 2:统计线性模型

Advanced Linear Models for Data Science 2: Statistical Linear Models

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约翰霍普金斯大学
Coursera
  • 完成时间大约为 10 个小时
  • 高级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:

– A basic understanding of linear algebra and multivariate calculus.
– A basic understanding of statistics and regression models.
– At least a little familiarity with proof based mathematics.
– Basic knowledge of the R programming language.

After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists’ general understanding of regression models.

课程大纲

Introduction and expected values

In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.

The multivariate normal distribution

In this module, we build up the multivariate and singular normal distribution by starting with iid normals.

Distributional results

In this module, we build the basic distributional results that we see in multivariable regression.

Residuals

In this module we will revisit residuals and consider their distributional results. We also consider the so-called PRESS residuals and show how they can be calculated without re-fitting the model.

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