Back to Regression Models

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3,228 ratings

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549 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 Michael H

•Jan 21, 2018

Thanks for the great course! I think the following can be improved: 1) More depth, I find myself keep looking for additional materials from other sources, e.g. proof of different theories, the course only provides overview, but didn't go deep enough 2) Project: I find the optional quiz project more interesting, the final project is too simple, and didn't include things we learnt such as GLM etc. A more comprehensive final project with more aspects of courses knowledge will be much better to re-solidate learning

By Sandro G

•Jun 24, 2017

This is the first time that I take a course about regression models. I I found it very useful and enteresting, may be for someone who already know this argument it could be less useful, because in some part it is lacking. I mean above all about some example that could be a little bit more complex than those presented in the videos and that more probably it could be more similar to real cases. In anycase, I would like to thank a lot the teachers and courser for this occasion to learn given to me and others !

By Pawel D

•Dec 18, 2016

This course is much improved, when compared with Statistical Inference. The instructor have put much effort in making the lectures interesting and casual, at the same time not loosing the value of contents. I especially liked some subtle jokes - just a finishing human touch.

Some lectures from 4th week were not very well rehearser and shot hastily. Some quiz question were disproportionately difficult, but most of material is covered in course or course materials. Otherwise the course is very educational.

By Yusuf E

•Aug 15, 2018

I am almost certain that regression models have more relavance in an academic setting than industry. But this doesn't affect really how I graded this course. I wish Brian skipped over the first week which entirely deals with regression to the mean. Weeks 2 and 3 were very good and detailed.

I am not sure if logistic classifier is mentioned in the next course but it would probably be best if this part would be included in the ML course. Other than that great course and very challenging quizzes.

By Amitabh M

•Apr 11, 2020

This is the best course of the data science curriculum. The book is excellent, problem videos on YouTube teach how to answer the quizzes and pursue peer review project. The logistics regression part is weaker in the textbook as well as in lecture material. Prof. Brian Caffo is the best of the three teachers as he provides the most of the components needed either in the book or in his videos unlike others.

By Jamison C

•Aug 28, 2018

Excellent course, though I recommend you supplement applied practice by using the principal instructor, Dr. Brian Caffo's book, to answer practice questions if you want to retain these content-packed lessons. Better yet, begin each week by looking at the quiz and printing it out. As you view the relevant content, answer the related questions (which are generally presented in order of delivery).

By Richard M A

•Dec 23, 2016

This was better than the statistical inference course, but Brian still puts too much emphasis on the precision of his language (as if he's teaching to other mathematicians) which makes it difficult to understand. I would like to see a bit more dumbed down explanation of the mathematics in the examples (similar to Sal at Khan Academy). If that happened, this would definitely be a 5 star course.

By Sandesh

•Jun 25, 2019

For the content covered, I think the course does a good job exposing students to fundamental concepts while also highlighting how much more there is to research in order to gain a solid understanding of this subject matter. The course offers a good foundation, and I hope they come out with a more advanced version of this course for more guided exposure.

By Christopher B

•Feb 28, 2017

This course was an improvement in teaching modality from the statistical inference course, with more polished content, but the link between the lectures and the actual exercises was still a bit strained. Overall, it felt like there was a bit of a disconnect between the swirl exercises and the lectures, and this led to a lot of self-teaching.

By Paul R

•Mar 13, 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

By Miguel C

•May 3, 2020

I really enjoyed the course. Even though I had already learned linear regression and logistic regression from a computer science perspective, I still learned a lot since the course approaches these subjects from a statistical view. The content was interesting and challenging, so I am really glad that I took this course.

By Max M

•Nov 19, 2017

Really appreciate the depth of this course, as well as the changes Prof. Caffo made in his teaching style since his Statistical Inference course. However, the reasoning behind some of the more complex topics, like GLMs, aren't adequately explained, and the Swirl lessons are presented in a strange and disorienting order.

By Cesar L

•Nov 1, 2016

Great course. The content might be improved to be more clear. I feel that sometimes the instructor assumes we are familiar with some concepts we have not seen in previous courses. Also, some times he does a very good job explaining the WHY before the HOW, and some other times he does not. Very knowledgeable instructor.

By Kevin H

•Nov 9, 2016

Something was missing from this course. I cam away with an increased understanding of regression but I still feel like I struggle with many concepts and had to put in much more time than the recommended.

Still when I found the answer it was all still contained and maybe the material itself is just advanced.

my 2c

By Jeremy J

•Nov 12, 2016

Like the way the Prof uses media. This is a very light touch on a very deep subject so it has to balance analytical work with the light "trust me and just do it" approach. The balance was mostly there although on a couple items I don't know that I had a good enough grip to know what I don't know.

By Wei W

•Oct 23, 2017

Brian did better job in this course to elaborate and demonstrate with examples. No doubt Brian is extremely knowledge about this subject. Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding. That's why I take one star away.

By Vidya M S

•Mar 14, 2017

The concepts are well explained and precise. I think it depends on the individual to dive deeper into the topic by independent learning. Good data examples. Also following the suggested book of the author helps with some extra excercises. However , I feel extra practice questions would help .

By Linda W

•Jun 3, 2016

This course will give you a good basic foundation in regression models. However, do be prepared to do a good amount of work besides just viewing the videos. I would recommend at the very least to go through the exercises in the 'recommended textbook' to gain a better understanding.

By Kim K

•Aug 8, 2018

You will need to know the subject before taking this class in order to understand or be able to put in a large amount of time to learn. The book "Introduction to Statistical Learning" is an excellent supplement to the course. Rigorous and rewarding when you put the work in.

By Scot M

•Mar 31, 2021

The course materials are engaging and thorough and the quizzes are at a reasonable level of difficulty. The concluding course project instructions are unclear and it takes some creative thinking to understand how to fully relate these to the course content.

By Ada

•Nov 14, 2016

Regression models was almost just as difficult as statistical inference. Again, the swirls and exercises were of great help. The pace, as always, was quite fast, but in the end all the pieces fitted together. Congratulations on a job well done!

By Peter G

•Feb 10, 2016

First 3 weeks give very reasonable overview of the subject - topics of linear / polynomial / multivariate regression are covered quite well.

Week 4 is a bit sloppy and ad-hoc, comparing to first 3 weeks - GLMs are given poorly.

By Utkarsh Y

•Sep 28, 2016

It is a good course for learning regression model implementation in R. You may need to have a basic understanding of popular regression models like linear & logistic as the course doesn't cover mathematical aspects in detail

By Tim M

•Oct 5, 2020

The course is informative & well taught. I would have liked to spend more time on GLM models, such as logistic regression. The Swirl assignments seem a bit outdated method of learning code and a bit of a hassle.

By Jim M

•Jul 23, 2020

Content is excellent and in depth. Structure could be better to present materials in a more organized fashion, particularly on how all the concepts and tools relate, and complex results interpretation.

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