This course is great, instructor is good, however, the material of this course is not well organized, even the swirl practice is not put in the correct week, not in the same pace as the lectures. The quiz and project are far much easier than lecture content.

I found the videos tough to watch. I was hoping for something that would be more practical for non-statisticians, but the lectures mainly devolved into mathematical proofs. That said, I did learn some from this class. Just not as much as I'd hoped.

There's just something about the course content that is difficult to attain. It's presented at way too high of a level without enough tangible examples of getting down into the weeds of how to actually perform and interpret the models and functions.

The flows of courses instructed by Caffo(Statistical Inference and Regression Models) are too long to concentrate it and the quiz is not quite related in lecture.

However, Contents of the book is really good, as well as homework in the book.

The instructor is at time incomprehensible. It would be helpful to speak more slowly and pause more often. Otherwise he sounds like repeating something that he's so well memorized after many years of teaching.

Good introduction to linear regression models but fell awfully short on diving a little deep into GLMs and going through use cases to convey how models are built, evaluated and updated in a systemic manner.

The course is interesting but probably overambitious. I think that if you do not have previous experience, with the material provided, it would be hard to have a real understanding of the topics covered.

The course is good. Unlike the previous courses I took in this track, there was almost no interaction in the forums and also no-one to give feedback. I wonder if there were any TA's present in this run.

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

A good (although slightly frustrating) course, attempted once but had to come back after studying the material in class, quite a heavy course if you've not been taught regression before

Given the importance of this subject, this course should have been split in two or more or have a longer duration to properly address subjects as GLM or model selection techniques.

This course is better than Statistical Inference, and I think it is as useful. Non credit excersise are still very good at helping with understanding in practice what is going on.

Useful class, but the content often simple in nature was explained in a confusing/complicated way. But the material is important and there is purchase for taking the class

These videos are better than the previous ones in this specialization but it gets too repetitive and long and boring. The swirl activities are the way to go!

The material was a little disjointed and not always explained with examples. Passing this course required a significant amount of outside study and research.

This is a decent class, covering linear regression and a few of its variants in good detail. It's a challenging subject, but presented acceptably here.

Lots of material needs additional study (from different sources) as it's only summarily explained. Much math without the link to the praxis :-(

The content was exposed in a very confused manner. I did not like how the teacher explained. It seemed more difficult than it really is

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

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

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

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

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

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

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