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：
シラバス - 本コースの学習内容
週
122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!
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.
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
週
22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!
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
週
33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!
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
週
42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.
1 reading, 1 quiz
1件の学習用教材
Project Files and Rubric10 分
4.7
179件のレビュー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
Linear Regression and Modeling からの人気レビュー
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.
講師
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
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.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題（該当する場合）にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。
この専門講座をサブスクライブすると何を行うことができるようになりますか？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
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.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。