关于此课程： 这门课程介绍一元和多元线性回归模型。 这些模型能够让你获得数据集和一个连续变量之间的关系。（比如说：）在教授的外表吸引程度和学生的评分之间有什么关联么？我们可以根据孩子母亲的特定特征来预测这个孩子的测试分数么？在这门课程当中，你将会学习线性回归的基本理论，运用免费统计软件R、RStudio分析一些数据例子来学习如何拟合、检验，以及如何利用回归模型去检验多元变量之间的关系。
制作方： Duke University
教学方： Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
| 基本信息 | 课程 3（共 5 门，Statistics with R Specialization ） |
| 级别 | 初级 |
| 承诺学习时间 | 学习时间4周，每周5-7个小时 |
| 语言 | 英语（English） |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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 this course!
1 个视频, 2 个阅读材料
- Reading: About Statistics with R Specialization
- Reading: More about Linear Regression and Modeling
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 relationship between two numerical variables.
8 个视频, 3 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Correlation
- 视频: Residuals
- 视频: Least Squares Line
- Reading: Lesson Learning Objectives
- 视频: Prediction and Extrapolation
- 视频: Conditions for Linear Regression
- 视频: R Squared
- 视频: Regression with Categorical Explanatory Variables
- Reading: Week 1 Suggested Readings and Practice
- Practice Quiz: Week 1 Practice Quiz
已评分: Week 1 Quiz
第 2 周
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 the discussion forum!
3 个视频, 3 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Inference for Linear Regression
- 视频: Variability Partitioning
- Reading: Week 2 Suggested Readings and Exercises
- Practice Quiz: Week 2 Practice Quiz
- Reading: Instructions for Week 1 & 2 Lab
已评分: Week 2 Quiz
已评分: Week 1 & 2 Lab
第 3 周
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. Hope you enjoy!
7 个视频, 4 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Multiple Predictors
- 视频: Adjusted R Squared
- 视频: Collinearity and Parsimony
- Reading: Lesson Learning Objectives
- 视频: Inference for MLR
- 视频: Model Selection
- 视频: Diagnostics for MLR
- Reading: Week 3 Suggested Readings and Exercises
- Practice Quiz: Week 3 Practice Quiz
- Reading: Instructions for Week 3 Lab
已评分: Week 3 Quiz
已评分: Week 3 Lab
第 4 周
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 assignment.
1 个阅读材料
- Reading: Project Files and Rubric
已评分: Data Analysis Project
常见问题解答
运作方式
Coursework
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Help from Your Peers
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Certificates
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制作方
Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
价格
| 购买课程 | |
|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
I enjoyed this course. It was quick, but I learned a lot! I thought the assignments were well-thought-out, and the custom R package for the course was a nice touch.
This course was very challenging. I learn a lot with the model we have to find and it is very interesting to note other students. None of us found the same results. For me, it is very strange not to know at the end what are the good results. It seems that you change the subject overtime. Do you send the correction?
It will be nice to know if we reasoned correctly.
Learnt a lot after doing the course project. Very good exposure.
Very useful and practical