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
89,209 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
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
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
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
4.7
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 VS 提供Sep 14, 2017
fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.\n\nthough you need to complete the prior courses to understand this.
由 AN 提供Dec 11, 2018
Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.
由 AA 提供Dec 19, 2017
Nice course. The downside is that it only explains interpretation of linear regression, but not enough details about how linear regression is performed from math point of view.
由 CG 提供Jan 19, 2018
Good but I felt some gaps in the material made it difficult to learn. Also, the quiz questions are focused on attention to detail "gotcha" questions. This can be frustrating.
由 OK 提供Sep 21, 2016
Very good course. while it does not cover everything. the teacher does a great job explaining things in a simple manner. My feed back would be to move ANOVA into this module.
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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.
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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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