Comentários e feedback de alunos de Linear Regression and Modeling da instituição Universidade Duke

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.

This course was good. However, compared to the other courses in the specialisation had less content. I would have liked to have videos on logistic regression as well.

Compared to the Course 2 Statistic inference, this session seems to be a little be informal and rush. But still learn a lot from the conception of linear regression!

Really good course as the previous ones in this specialization. Could have included something more on checking for collinearity with categorical variables.

It was a really good introduction to Linear Model, I recommend this course to all people who wants to learn more about statistical analysis

Excellent Course. Mine, the teacher is a great great teacher. The mentors help a lot.

Technical parts, coursera platform should work better

Lecture explanations are fantastic as are slides. Pace is appropriate. R information is a little sketchy but manageable with diligence.

Very informative for an introduction. Wish it was longer and more mathematical, but there are other courses on Coursera for that.

Good overview of regression modeling. Would have liked to see more on logistic regression. But that's ok, can read it on my own.

Great course. I only wish there was more time spent on dealing with more complex situations such as overfitting.

I liked the course. I learnt a lot while working on its project. Instructor's way of teaching is very engaging.

The material in this course is explained very well. However it requires one has the knowledge in using R.

Contents are easier compared with other courses in this series. Quite systematic and easy to understand.

Clearly presented. R instruction is pretty minimal, so there is a lot of trial and error and googling.

I liked, but I guess it could improve little by including more topics in linear regression analysis.