Back to Regression Models

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558 reviews

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....

KA

Dec 16, 2017

Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.

BA

Jan 31, 2017

It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.

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By LU Z

•Sep 26, 2018

Starting from the first week swirl practice, course content is poorly organized making even simple concept difficult to understand.

By Hendrik F

•Jan 17, 2016

I find it very tough to understand everything. Buying the course book helps to overcome this. You have to dedicate a lot of time.

By Mark S

•Apr 24, 2018

Lots of math, but it would be more productive to focus more on the output of R and better understand the results

By Mertz

•Mar 20, 2018

Bad audio and video quality. Too fast on some complex ideas and too slow when come repetitions between videos...

By Andres C S

•Mar 1, 2016

I think this course needs more emphasis on practical applications and less mathematical background.

By Erwin V

•Dec 20, 2016

Very interesting course, yet course content could be spread more evenly (week 4 is really a lot)

By Prabeeti B

•Sep 17, 2019

Course has more theoretical concept than application.. It has to be more application based

By Praveen J

•Apr 22, 2020

I think a revamping of the concepts in a more ellabroate way is required in the course

By Suleman W

•Nov 9, 2017

I did find it difficult to follow and understand some of the materials.

By Rafal K

•Feb 28, 2017

Many things are not clear enough in multivariable regression part.

By Eric L

•Feb 2, 2016

good quick overview, could have more actual R examples in lectures

By Ansh T

•Mar 22, 2020

Topics like logistic regression were not explained clearly

By Angela W

•Nov 27, 2017

I learned a lot, but it was so much content for 4 weeks!

By Gareth S

•Jul 16, 2017

Expects a level of statistical knowledge already.

By David S

•Nov 4, 2018

needed to consult external resources extensively

By Lei M

•Aug 23, 2017

Some of the materials are too much math for me.

By xuwei. l

•Sep 22, 2016

the lecture notes is a bit confusing

By Marcela Q

•Jan 6, 2020

Terrible professor, good book

By Hani M

•Oct 24, 2017

was tough

By Barry S

•Mar 15, 2016

This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material. Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

A more structured final project would have been helpful. The instructions on this project weren't quite so blunt as to say "Take this data set, do some regression-y stuff and come back with something about these two variables," but that's basically as far as our instructions went. It could have been a great learning experience to have a more detailed guide through the construction of a regression analysis, but instead an assignment which was 40% of our grade was put together as an afterthought. It was the assignment equivalent of stopping in the 7-11 a block away from a birthday party to buy a card.

Also, in terms of delivering the content: Mr. Caffo needs to structure his slide/video arrangements so that he is not standing in front of the text. Think of it from the point of view of somebody wanting to listen and read at the same time.

By R. H

•Mar 19, 2020

The timing on this course is very inaccurate - it should take much longer than 4 weeks, 6 weeks at the absolute minimum. I say this because Week 4 has so much information crammed in of all different types of General Linear Models (i.e. models that are not necessarily a straight line). Binomials, Poisson, splines - each of these topics could have their own weeks, but instead they are quickly summarized for one week with the student expect to understand them for the quiz. The other issue, which has been a problem with all courses in this specialization, is the discussion boards. They are totally abandoned by mods; good luck finding any post that isn't "grade my project? I'll grade yours!" despite a mod post that says such requests will be deleted. The board is totally flood with those requests, and makes me wonder how many people are passing these classes wrongly because "if u give me 100 i will grade yours too!" It totally devalues the program. The creators seemingly abandoning Coursera have made this certificate a waste.

By Renata G

•Mar 28, 2021

I came from

Statistical Inference and I felt very sorry when I see the same instructor here at this course.

Regression Models is a very important subject and I am very interested in real learning. However, this course was very, very, very disappointing to me. I am deep sorry for my sincerity... but this was not as high-quality course. Total lack of any support from the TA/instructor team is frustrating. Wikipedia, youtube videos, books... were much more helpful and effective for me. I believe the real issue lay in the teaching style. Brian seemed a very intelligent person, but he does not teach well. His way of explaining things was really bad: he speaks too fast (sometimes he changes terms...), he skips from slide to slide very quickly, he often do not provide adequate explanations. He also does not approach realistic cases to apply Regression Models in day a day basis. His book is only a copy of his teaching slides. I certainly do not recommend this course.

By Kaspar M

•Oct 12, 2020

There's some useful material in the course. There were some major issues though: 1) there is so much to cover that this really ought to be broken into two courses or more. It is not a 4-week course. It would really be helpful to break it into chunks and include some more comprehensive exercises so the learner can get a full grasp of the subject. The quizzes, particularly the final one, were curiously disconnected from the course material. The final project as assigned was just straight-out baffling. I noticed some learners submitting garbage solutions for review, presumably just so they could look at what other people were doing to figure out what they were supposed to be doing. Oh one more thing: Caffo never explains what ANOVA is, he just starts using it. Overall: I would like to know who is doing a well-designed MOOC on this, because I would like to take it.

By Mohamed A

•Nov 2, 2016

This course failed greatly to balance the workload by week. The third week which I think was the most important one have too many information to learn and assimilate whereas the first two weeks could be rearranged to start multivariate regression earlier. Another proof of week 3 issue: the related swirl exercises start in week2 (2 of them) and finish in week4 (2 more exercises) !!!!!

I think one of the most important expertise and knowledge that a data scientist must know and master was unfairly squeezed in one week leaving no time for the learner/student to do more search/exercises on the subject.

By Pedro J

•Jun 6, 2016

The professor doesn't explain clearly as part of the videos is his correcting himself or saying the same thing two or three times. And why must the videos show the teacher? It distracts from the slides and seeing him move doesn't help understand anything better

Concepts like VIF or hat values are not very well explained by the teacher, at least the SWIRL lesson explains it correctly. ANOVA and ANCOVA are mentioned in the description but they aren't explained anywhere. ANOVA is used without any explanation of what it is.

I found myself searching online for other sources to understand the concepts.

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