About this course: 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.
Created by: Duke University
Taught by: Mine Çetinkaya-Rundel, Assistant Professor of the Practice
Department of Statistical Science
| Basic Info | Course 3 of 5 in the Statistics with R Specialization |
| Level | Beginner |
| Commitment | 4 weeks of study, 5-7 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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 video, 2 readings
- Video: Introduction to Statistics with R
- Reading: About Statistics with R Specialization
- Reading: More about Linear Regression and Modeling
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 videos, 3 readings, 1 practice quiz
- Reading: Lesson Learning Objectives
- Video: Introduction
- Video: Correlation
- Video: Residuals
- Video: Least Squares Line
- Reading: Lesson Learning Objectives
- Video: Prediction and Extrapolation
- Video: Conditions for Linear Regression
- Video: R Squared
- Video: Regression with Categorical Explanatory Variables
- Reading: Week 1 Suggested Readings and Practice
- Practice Quiz: Week 1 Practice Quiz
Graded: Week 1 Quiz
WEEK 2
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 videos, 3 readings, 1 practice quiz
- Reading: Lesson Learning Objectives
- Video: Outliers in Regression
- Video: Inference for Linear Regression
- Video: Variability Partitioning
- Reading: Week 2 Suggested Readings and Exercises
- Practice Quiz: Week 2 Practice Quiz
- Reading: Instructions for Week 1 & 2 Lab
Graded: Week 2 Quiz
Graded: Week 1 & 2 Lab
WEEK 3
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 videos, 4 readings, 1 practice quiz
- Video: Introduction
- Reading: Lesson Learning Objectives
- Video: Multiple Predictors
- Video: Adjusted R Squared
- Video: Collinearity and Parsimony
- Reading: Lesson Learning Objectives
- Video: Inference for MLR
- Video: Model Selection
- Video: Diagnostics for MLR
- Reading: Week 3 Suggested Readings and Exercises
- Practice Quiz: Week 3 Practice Quiz
- Reading: Instructions for Week 3 Lab
Graded: Week 3 Quiz
Graded: Week 3 Lab
WEEK 4
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
- Reading: Project Files and Rubric
Graded: Data Analysis Project
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Creators
Duke University
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.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Another excellent course in this series.
The content is very basic but useful!
I truly enjoyed this course. It's one of the most useful and easier to understand than the rest. Could use more samples and information on how to deal with Categorical Data though.
Final Project is a full-time job. 1-week while working full-time is hardly enough time.
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AI
Loved the course; content, exercises and the final assignment we very good. Loved the instructor's energy!!