About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Beginner Level

Approx. 10 hours to complete

English

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Skills you will gain

Statistics
Linear Regression
R Programming
Regression Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 10 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.88/5 (120 Ratings)

Mine Çetinkaya-Rundel

Associate Professor of the Practice

Department of Statistical Science

358,515 Learners

7 Courses

Syllabus - What you will learn from this course

Content Rating94%(4,214 ratings)

Week1

Week 1

22 minutes to complete

About Linear Regression and Modeling

22 minutes to complete

1 video (Total 2 min), 2 readings

Week2

Week 2

2 hours to complete

Linear Regression

2 hours to complete

8 videos (Total 47 min), 3 readings, 2 quizzes

Week3

Week 3

3 hours to complete

More about Linear Regression

3 hours to complete

3 videos (Total 24 min), 5 readings, 3 quizzes

Week4

Week 4

5 hours to complete

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. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.

5 hours to complete

7 videos (Total 57 min), 6 readings, 3 quizzes

7 videos

Introduction2mMultiple Predictors11mAdjusted R Squared10mCollinearity and Parsimony3mInference for MLR11mModel Selection11mDiagnostics for MLR7m

6 readings

Lesson Learning Objectives10mLesson Learning Objectives10mWeek 3 Suggested Readings and Exercises10mWeek 3 Lab Instructions (RStudio)10mWeek 3 Lab Instructions (RStudio Cloud)10mProject Instructions, Data files, and Checklist 2h

3 practice exercises

Week 3 Practice Quiz30mWeek 3 Quiz20mWeek 3 Lab30m

Reviews

4.8

305 reviews

5 stars
80.20%
4 stars
16.06%
3 stars
2.93%
2 stars
0.24%
1 star
0.54%

TOP REVIEWS FROM LINEAR REGRESSION AND MODELING

by TMJul 21, 2020

A great primer on linear regression with labs that help to establish understanding and a project that is focused enough not to be overwhelming, and allows the learner to play around with the concepts

by PKMay 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.

by YOJan 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

by MMSep 26, 2021

linear regression is well taught throughout the course, but I think learning other types of regression modeling would be useful as well and adding them to the course materials is really good.

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Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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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Linear Regression and Modeling

About this Course

Skills you will gain