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
41,581 次近期查看
Learner Career Outcomes
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
您将获得的技能
StatisticsLinear RegressionR ProgrammingRegression Analysis
Learner Career Outcomes
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
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
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初级
完成时间大约为14 小时
建议：4 weeks of study, 5-7 hours/week
英语（English）
字幕：英语（English）, 韩语
教学大纲 - 您将从这门课程中学到什么
周
1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!
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.
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
周
2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!
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
周
3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!
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
周
4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.
1 个阅读材料, 1 个测验
1 个阅读材料
Project Files and Rubric10分钟
4.7
189 条评论来自Linear Regression and Modeling 的热门评论
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.
I feel I'm running out of complement words for this course series. In conclusion, clear teaching, helpful project, and knowledgeable classmates that I can learn from through final project.
讲师
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
关于 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.
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
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注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
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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