Thorough review of simple linear regression and multiple linear regression with a good bit of well-explained theory and challenging assignments. Highly recommended for those getting their feet wet in regression and for those already familiar with the techniques but need to brush up on the theoretical aspects of it. One of the better courses on Coursera.

Peer reviews appear to be unreliably completable. After performing all the work I have not found any way to get 2 of 6 peer reviews completed so I could complete the course. I am providing 1 star because the lack of ability to receive credit for valid work seems to be "table stakes". It is a minimum bar of performance. Further, this course is a valuable component in the Boulder masters program. For a ~ $20K it seems that Boulder may be exploiting students.

This course is the best out there for those who want to learn R for regression and the theoretical foundation of regression. Professor Zaharatos explains the meth background needed in a excellent way. My knoweldge of math limited yet I was able to pass most of the assignments. The assignments are very well thought. Some minor problems with the autograded assignments but most of them are manageable after sometime. I highly recommend that course for everyone who making at their mid-way to statistics.

It is a course with some difficulty specially the auto graded assignments, but if you are really interested to learn about the statistics behind linear regression you will find this course excellent.

Something i did not like is the peer review assignments sometimes they took to much time to be reviewed.

A lot of work with several peer reviews, but it get you into R for Regression Analysis. Well laid out course. need knowledge of Linear algrebra for this course.

The depth of understanding reached more than expected. The best lecture of coursera.

The best course I ever had in statistics

Great instructor! He explained the material very well. I didn't get much out of the peer review assignments. I would prefer more code work or the peer review could be on explaining certain parts of the code output like in the Machine Learning classes.

Content and intent is great. The autograder for assignments is frustrating.