This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
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课程信息
170,907 次近期查看
学生职业成果
33%
完成这些课程后已开始新的职业生涯
45%
通过此课程获得实实在在的工作福利
12%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为9 小时
英语（English）
字幕：英语（English）, 韩语
您将获得的技能
StatisticsLinear RegressionR ProgrammingRegression Analysis
学生职业成果
33%
完成这些课程后已开始新的职业生涯
45%
通过此课程获得实实在在的工作福利
12%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为9 小时
英语（English）
字幕：英语（English）, 韩语
提供方
杜克大学
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 22 分钟
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!
完成时间为 22 分钟
1 个视频 （总计 2 分钟）, 2 个阅读材料
2 个阅读材料
About Statistics with R Specialization10分钟
More about Linear Regression and Modeling10分钟
完成时间为 2 小时
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.
完成时间为 2 小时
8 个视频 （总计 47 分钟）, 3 个阅读材料, 2 个测验
8 个视频
Correlation9分钟
Residuals1分钟
Least Squares Line11分钟
Prediction and Extrapolation3分钟
Conditions for Linear Regression10分钟
R Squared4分钟
Regression with Categorical Explanatory Variables5分钟
3 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 1 Suggested Readings and Practice10分钟
2 个练习
Week 1 Practice Quiz8分钟
Week 1 Quiz18分钟
完成时间为 2 小时
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!
完成时间为 2 小时
3 个视频 （总计 24 分钟）, 5 个阅读材料, 3 个测验
5 个阅读材料
Lesson Learning Objectives10分钟
Week 2 Suggested Readings and Exercises10分钟
About Lab Choices10分钟
Week 1 & 2 Lab Instructions (RStudio)10分钟
Week 1 & 2 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 2 Practice Quiz6分钟
Week 2 Quiz16分钟
Week 1 & 2 Lab20分钟
完成时间为 3 小时
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!
完成时间为 3 小时
7 个视频 （总计 57 分钟）, 5 个阅读材料, 3 个测验
7 个视频
Introduction2分钟
Multiple Predictors11分钟
Adjusted R Squared10分钟
Collinearity and Parsimony3分钟
Inference for MLR11分钟
Model Selection11分钟
Diagnostics for MLR7分钟
5 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 3 Suggested Readings and Exercises10分钟
Week 3 Lab Instructions (RStudio)10分钟
Week 3 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 3 Practice Quiz16分钟
Week 3 Quiz20分钟
Week 3 Lab20分钟
完成时间为 2 小时
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.
完成时间为 2 小时
1 个阅读材料
1 个阅读材料
Project Files and Rubric10分钟
审阅
来自LINEAR REGRESSION AND MODELING 的热门评论
由 PK 提供May 23, 2017
Very good course taught by Dr. Mine who is as always a very good teacher. The videos are very eloquent and easy to understand. Highly recommend it if you are looking for a basic refresher course.
由 DS 提供Mar 29, 2017
This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.
由 EB 提供Feb 25, 2017
Good, but a little "smaller" than the Inferential statistics course (which is very complete). I would have liked to also learn Logistics regression, which I now have to learn elsewhere.
由 LH 提供May 14, 2020
It has been a great adventure so far. I still greatly appreciate how final projects are constructed that gives us freedom to choose our approach to the problems within the data set.
关于 Statistics with R 专项课程
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
常见问题
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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