Back to Linear Regression and Modeling

4.7

stars

1,335 ratings

•

237 reviews

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....

Jul 22, 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

May 24, 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.

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By Ana C

•Oct 30, 2016

Excellent Course. Mine, the teacher is a great great teacher. The mentors help a lot.

Technical parts, coursera platform should work better

By Janice H

•Jun 05, 2020

Lecture explanations are fantastic as are slides. Pace is appropriate. R information is a little sketchy but manageable with diligence.

By Nathan H

•Dec 19, 2018

Very informative for an introduction. Wish it was longer and more mathematical, but there are other courses on Coursera for that.

By Tony G

•Jan 29, 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.

By Scott T

•Aug 09, 2016

Great course. I only wish there was more time spent on dealing with more complex situations such as overfitting.

By Shivani J

•Apr 05, 2020

I liked the course. I learnt a lot while working on its project. Instructor's way of teaching is very engaging.

By Elham L

•Apr 07, 2020

The material in this course is explained very well. However it requires one has the knowledge in using R.

By Siyao G

•Aug 06, 2019

Contents are easier compared with other courses in this series. Quite systematic and easy to understand.

By Natalie R

•Jun 03, 2019

Clearly presented. R instruction is pretty minimal, so there is a lot of trial and error and googling.

By Guillermo U O G

•May 12, 2019

I liked, but I guess it could improve little by including more topics in linear regression analysis.

By Jian S

•Dec 12, 2016

I learnt quite a bit. One of the most useful courses! I would suggest add more exercises in R.

By NG Y W

•Dec 12, 2016

This course has provided me with a good and simple understanding on the concept

By Amir Z

•Sep 01, 2016

This is a great course for this specialization but don't expect much depth.

By zhenyue z

•Jun 07, 2016

nice lecture, but it is really too short, not into too much details.

By Luis F R C

•Oct 27, 2016

Excellent course, I think it still could include more content!

By Anna D

•May 22, 2017

Great course and lots of useful knowledge!

By Nikhil K

•Jan 25, 2020

Not covered entire regression technique

By FangYiWang

•Apr 19, 2019

A good course for Bayesian statistics.

By Mohammed S S

•Jun 08, 2020

Great model with clear explanations

By Daniel C

•Apr 20, 2017

Very useful insights and lea

By Lalu P L

•Apr 22, 2019

Could be more informative

By Syed M R A

•Mar 20, 2018

Awesome course.

By Toan T L

•Dec 11, 2018

A good course

By Ananda R

•Mar 14, 2018

excellent

By Micah H

•Apr 30, 2018

Other nits about the depth and breadth of the course aside, I thought it was a good course. The main critique I have to offer is the lack of emphasis of using the power of R. When teaching model selection, the course should have at least provided instruction—or at least a written resource—on how to write the R code for automating forward/backward selection by R^2.* Being a course about using R as well as about linear regression and modeling, it seems like the appropriate thing to do.

(*A classmate whose final project I peer-reviewed used for loops to run the forward model selection based on R^2. That's how I learned about it.)

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