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The key concepts of linear regression are explain really well, without heavy mathematical explanation, that is good, because the main concepts are what it important.

The project at the end of the course is REALLY good, you can learn a lot from the analysis and investigation you need to do on it, it took me around 30 hours to really understand and complete (a full time job of 1 week), which is really nice.

I give 4 stars to the course, because they don't dig very much in variables selection, specially with categorical variables, with are the ones i had an hard time during the project. Note: It was hard, because it was difficult, but in the process i learnt a lot of things investigating.

Besides this point, the course is really good to say: "I know the basics of linear regression, I know how to handle it in R", the topic of "Linear Regression and Modeling" is of course much, much more larger than what can be explained in 1 course.

First, this course will enable me to understand the quantitative part of a research. Additionally, this will help a student to understand the essence of performing such numerical calculations and will make us understand the relationship between different variables.

Secondly, this is the need of the hour and such numerical functions are used worldwide so, learning this course will help in almost every field be it 'Management' be it 'Social Sciences' or be it 'Human Behaviour'.

The course is good and the material is presented clearly. The capstone project is very good and makes you really use all the knowledge obtained in the course and the pre-prequisite course Inferral Statistics. My only dissatisfaction is that the course was rather short: only 3 weeks of material and 1 capstone. Therefor it covered less material then I expected. For example, I expected logistic regression to be covered.

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.

Awesome course with very clear material! I do wish that the course had a bit broader of a scope (i.e. also covering logistic regression and other kinds of regression with non-numerical response variables). Compared to the Inferential Statistics course, it feels like there was a bit less material. Otherwise, I was very happy with this course. :)

An extremely good introductory course. A must for undergraduates. The style of teaching is fluid and you learn concepts step by step. For more advanced learners the only drawback I see is that this is, by default, an introductory course.But still for advanced learners it can be a great (and I really mean great) refresher.

The material is very straightforward and gives a great introduction to multiple linear regression. My only reservation is the length of the course, which seems to be a bit shorter than other courses in the certification. Would love to have more material/in-depth exposure to components available to us in R.

It's a nice overview of linear regression but I feel like there needs to be more time spent on model selection processes, collinearity/confounding/intermediate variables, and interaction terms. It's super important for accurate model building for research purposes.

Again, Dr. Mine Cetinkaya Rundel is amazing. However, linear regression is a vast topic, and maybe another week could have been better. But nonetheless, the concepts explained herein are crystal clear, succinct, and taught in an engaging manner.

Really enjoyed this course! It teaches you the theory you need to understand how a linear regression model works, how to check that your model fulfils certain conditions so that it is valid, and how to build and implement your model in practice!

Great introduction to linear regression. Nice, clean R tutorials via the labs. The lectures do become a little monotonous, but there there are linked readings in a nice, open-source textbook if reading suits you better than listening.

Very good and gentle introduction to linear regression. The final assignment however uses dataset which is very risky to use with linear regression (not all conditions were met in all the assignments I rated!). This is confusing.

A great course on regression. Though some topics weren't taught in the lecture but they can be easily covered using the links provided in the course. Additionally, a more detailed lecture on diagnostics plot can be useful.

A very practical course with hands on sessions in regression. I loved the final project which actually helped me to apply everything that I learnt during the 4 week period and hence cleared so many doubts I had in mind.

i would like it very much if the course had more auxiliar videos about R code, because is maybe the most dificult thing to follow on the course. In general the course is amazing, very well explained, i learned a lot.

This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.

Good, but a little "smaller" than the Inferential statistics course (which is very complete). I would have liked to also learn Logistics regression, which I now have to learn elsewhere.

Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.

A nice experience, clear explanations, lots of exercises that are really important. The project also allowed one to reflect carefully on how to use R to carry out the analysis.

Good but I felt some gaps in the material made it difficult to learn. Also, the quiz questions are focused on attention to detail "gotcha" questions. This can be frustrating.

Nice course. The downside is that it only explains interpretation of linear regression, but not enough details about how linear regression is performed from math point of view.