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.
提供：
このコースについて
83,166 最近の表示
受講生の就業成果
33%
コース終了後に新しいキャリアをスタートした
45%
コースが具体的なキャリアアップにつながった
12%
昇給や昇進につながった
習得するスキル
StatisticsLinear RegressionR ProgrammingRegression Analysis
受講生の就業成果
33%
コース終了後に新しいキャリアをスタートした
45%
コースが具体的なキャリアアップにつながった
12%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約14時間で修了
推奨：4 weeks of study, 5-7 hours/week
英語
字幕：英語, 韓国語
シラバス - 本コースの学習内容
22分で修了
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!
22分で修了
1件のビデオ （合計2分）, 2 readings
1件のビデオ
2件の学習用教材
About Statistics with R Specialization10 分
More about Linear Regression and Modeling10 分
2時間で修了
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.
2時間で修了
8件のビデオ （合計47分）, 3 readings, 2 quizzes
8件のビデオ
Correlation9 分
Residuals1 分
Least Squares Line11 分
Prediction and Extrapolation3 分
Conditions for Linear Regression10 分
R Squared4 分
Regression with Categorical Explanatory Variables5 分
3件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 1 Suggested Readings and Practice10 分
2の練習問題
Week 1 Practice Quiz8 分
Week 1 Quiz18 分
2時間で修了
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!
2時間で修了
3件のビデオ （合計24分）, 5 readings, 3 quizzes
5件の学習用教材
Lesson Learning Objectives10 分
Week 2 Suggested Readings and Exercises10 分
About Lab Choices10 分
Week 1 & 2 Lab Instructions (RStudio)10 分
Week 1 & 2 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 2 Practice Quiz6 分
Week 2 Quiz16 分
Week 1 & 2 Lab20 分
3時間で修了
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!
3時間で修了
7件のビデオ （合計57分）, 5 readings, 3 quizzes
7件のビデオ
Introduction2 分
Multiple Predictors11 分
Adjusted R Squared10 分
Collinearity and Parsimony3 分
Inference for MLR11 分
Model Selection11 分
Diagnostics for MLR7 分
5件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 3 Suggested Readings and Exercises10 分
Week 3 Lab Instructions (RStudio)10 分
Week 3 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 3 Practice Quiz16 分
Week 3 Quiz20 分
Week 3 Lab20 分
2時間で修了
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.
2時間で修了
1 reading
1件の学習用教材
Project Files and Rubric10 分
レビュー
4.7
LINEAR REGRESSION AND MODELING からの人気レビュー
PKにより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.
DSにより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.
EBにより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.
VSにより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.
ANにより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.
AAにより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.
OKにより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.
CGにより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.
YOによりJan 1, 2017
Again like the first two courses, this course was great. The Lectures were excellent and the assignments very helpful in solidifying understanding. Thanks a lot Dr Mine
JMによりJan 12, 2017
This course was good. However, compared to the other courses in the specialisation had less content. I would have liked to have videos on logistic regression as well.
JSによりNov 3, 2018
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.
MSによりJun 20, 2018
This was the first course where I started noticing that I'm really learning and was able to apply some of the earned knowledge at work.Totally recommended.
ABによりNov 27, 2017
Great course! I've already taken a similar stats course using SPSS and this course was an excellent refresher, while increasing my familiarity with R.
HHによりJul 3, 2017
wonderful module! easy to understand. the labs are extremely helpful which enables you to have a good command of practicing linear regression with R
ACによりOct 29, 2016
Excellent Course. Mine, the teacher is a great great teacher. The mentors help a lot.\n\nTechnical parts, coursera platform should work better
HZによりOct 1, 2016
The structure of this course is really good. The slides demonstrates everything clearly. The speed that the instructor talks is good too.
BSによりJan 19, 2018
One of the most useful of the series, can be valuable as a standalone course on Regression and Correlation. It is also very accessible.
NHによりDec 18, 2018
Very informative for an introduction. Wish it was longer and more mathematical, but there are other courses on Coursera for that.
TGによりJan 28, 2017
Good overview of regression modeling. Would have liked to see more on logistic regression. But that's ok, can read it on my own.
DWによりJun 5, 2017
The Professor is a clear communicator and has a flair for finding interesting and engaging examples to illustrate the concepts.
デューク大学（Duke University）について
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.
Statistics with R専門講座について
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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返金ポリシーについて教えてください。
学資援助はありますか？
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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