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Learner Reviews & Feedback for Linear Regression and Modeling by Duke University

4.8
stars
1,681 ratings

About the 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....

Top reviews

TM

Jul 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

PK

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.

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1 - 25 of 311 Reviews for Linear Regression and Modeling

By Diego R G

May 26, 2019

It's a very good course for starting to learn about linear regression. Just be aware that the quality of this course is a bit lower than the previous two. There are fewer videos, the book material is shorter (less suggested exercises and the chapters cover fewer things about linear regression) and some quiz exercises of week 2, which should only cover simple linear regression, have some questions about multiple linear regression which is the 3rd week's topic.

Also, as in the previous two courses, the emphasis is on statistics, not programming with R. This means that if you already know statistics and only want to learn how to use R, there are probably better courses out there for you. But if you want to learn or improve your knowledge of statistics, and also learn how to use R, then do take this course. I think that it's much better to start learning R by actually doing some statistical work and seeing first hand what the software is capable of doing with only a few lines of code, even if you don't fully understand the code's syntax at first.

With all that said, if you take the course PAY ATTENTION TO THE LECTURES, READ THE CHAPTERS and DO THE SUGGESTED EXERCISES. I can't stress this enough. If you don't do all of that, you won't learn as much as you should, and it's painfully obvious that some students didn't do all of that when you review their final R projects. Also, take your time with that final project because that's where you will actually learn some things about R and use what you have learned about statistics (you will have to use google to learn how to code some things properly).

By Mindaugas Ž

Jan 7, 2019

The course is good regarding concepts and theoretical exercises, but poor regarding applying new knowledge in R. Since the course is introductory, an instruction how to install R and a list of R functions without clear explanation how they should be applied in general regression situations makes me explore other sources to learn how to apply those concepts (e.g. DataCamp, CRAN-RProject, etc) and then get back to learn theory? Sorry for expectations but course should provide a full and integrated package of knowledge and skills, especially for beginners.

Furthermore, no Machine Learning (ML) is covered as a tool to run a regression.

My proposal is to provide an algorithm with a comprehensive example how to run a regression using R. From data to final model, step-by-step.

By Assaf B

Mar 15, 2018

The mathematical depth of this course, is insufficient even at its targeted level, and therefore a lot of practical manipulations of the data, and fine tuning of the model could be had if a week more has been put into this course.

Easy does not equate fun, after completing this course, I left the specialization.

By Y. N

Jan 16, 2023

The course is great and I got the certificate for finishing the specialization. I'm a bit sad though.

There used to be a 4-th and a capstone course in this specialization, but they removed them from the requirements. The courses used to be a bit more difficult with a final project, they made them optional too.

I really hope they can improve the course content instead of removing things. The sense of achievement drops greatly knowing the courses get watered down in difficulty.

By Omar K

Sep 22, 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.

By mark n

Jul 27, 2018

Great instruction on stats, however the R portion a weekly project that is largely self directed, very little instruction.

By Qian R

Mar 17, 2021

I do like the Duke way of teaching statistic, very clear and easy to understand. The final project is interesting and you can learn a lot while doing it, but it won't be enough to do it using the knowledge from this course only, you need to learn from online researching along the way.

By Aditya N

Sep 13, 2020

The course is structured in a very informative way, it is easy to understand and at the same time difficult concepts are presented in a very easy way. The course instructor is awesome.

By Vijay P S

Sep 15, 2017

fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.

though you need to complete the prior courses to understand this.

By Alessandro D M

Jan 28, 2022

I really liked the way theoretical knowledge is combined with real study cases

By Richard M

Feb 5, 2019

Really great course, clear and easy to follow. Highlight recommended.

By Katy S

Apr 18, 2020

I enjoyed the course and learned a lot overall. However, we were extraordinarily unprepared for the final project. The dataset was full of characteristics that were explicitly not covered in the course. I had to do a ton of outside research in order to complete the project and understand what I was doing.

Additionally, the forums are completely unhelpful. I never got a reply to any of my questions, and saw many other unanswered questions while browsing.

By Σταυρος Β

Jan 17, 2022

Very good course and material overall - although a little lighter in content compared to the previous one (Inferential Statistics). I think you get a good intro to linear regression and getting to apply the knowledge in the assignment is very helpful. Beware though as the message boards are empty and it's very unlikely that you will get any questions answered. That's a serious issue with this class.

By Liam K

Mar 19, 2020

Overall it covers everything that you would want this course to.

However, I was a complete beginner when starting this course, and as a result I regularly got confused (especially when it came to coding in R)

3/5 stars for me

By QIAN Y

Jul 1, 2016

Compared to other courses in the specification, this course content is too shallow and brief.

By M. I F

Jun 9, 2016

She just started with wk 2. There should have been more explanation and videos in week 1...not very interesting. I think statistics you need to take in person.

By Anukul

Apr 3, 2019

it provides a superficial knowledge. A deep understanding of subject can not be gain from this course

By Jyotir P

May 3, 2020

The course required no prerequisites, but no one can complete the course without actually completing the courses preceding this in the specializing series.

By Syed S R

Sep 13, 2018

Not suitable for beginners

By Adedayo M A

Jun 1, 2020

This is arguably the best online course I have ever done. The teacher and the way she drives home her point her spot-on. Until now, I have struggled with most aspects of linear regression, e.g model selection, model fitting and interpretation of the results. Undertaken this course has cleared all these shortcomings and I can't wait to start analyzing my PhD project. I appreciate the opportunity afforded me by Duke University and Coursera for participating in the course, it will indeed help me in my academic pursuit and lastly many thanks to the facilitator of the course Dr. Mine Çetinkaya-Rundel

I appreciate.

Adedayo Michael, AWONIYI

By Ghali M

Jul 30, 2020

Well structured course , huge thank to you,I learned a lot, in fact, while learning this course, I was in a discovery internship in an office of agriculture, and I was giving data about date production of a certain product, rain, temperature, and thanks to this course I knew what to do and I manage to fit a multiple linear model.The result was not perfect due to the lack of data and other factors, but I was very satisfied with my work.

By Monique O V

Jun 8, 2020

Excellent course. I feel this specialization and this course are far more rigorous than the statistics class I took in college. The professor does an excellent job of making the material very intuitive. The steps needed to develop a sound linear regression model are very clearly explained; the diagnostics are clearly laid out with examples of how to test each condition. I highly recommend this class.

By Anne B

Oct 29, 2018

This course was very challenging. I learn a lot with the model we have to find and it is very interesting to note other students. None of us found the same results. For me, it is very strange not to know at the end what are the good results. It seems that you change the subject overtime. Do you send the correction?

It will be nice to know if we reasoned correctly.

By Can

Dec 19, 2017

Great course. The instructor is very clear on the statistical concepts and thorough on the application of various methods. I learned a lot about how to do regression analysis from this course. The R integration is very helpful as well. Overall great course! Everybody should take it and complete all the quizzes and the final project.

By Anna H

Nov 4, 2023

Thank you so much, Dr. Mine Çetinkaya-Rundel! I have learned a lot from this class, and I feel much more confident about pursuing my future plans. I would love to invite you to my institution, the University of Arkansas at Pine Bluff, and have our students learn from you and hear about the research you do. Thank you again!