Linear Regression and Modeling

Linear Regression and Modeling

课程
en
英语
20 时
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来源
  • 来自www.coursera.org
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  • 4 序列
  • 等级 介绍

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课程详情

教学大纲

  • Week 1 - About Linear Regression and Modeling
    This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in thi...
  • Week 1 - Linear Regression
    In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear ...
  • Week 2 - More about Linear Regression
    Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in...
  • Week 3 - Multiple Regression
    In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. H...
  • Week 4 - Final Project
    In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review a...

先决条件

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讲师

Mine Çetinkaya-Rundel
Associate Professor of the Practice
Department of Statistical Science

编辑

杜克大学是一所位于北卡罗来纳州达勒姆的北美私立研究型大学。该大学以杜克王朝的名字命名。

虽然该大学直到 1924 年才正式成立(其根源可追溯到 1838 年)。杜克大学经常被称为 "南方的哈佛",是美国南方选拔最严格的大学。

该大学是美国大学协会的成员,自 1900 年以来,该协会一直将北美的精英研究型大学聚集在一起。

平台

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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