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Voltar para Modelos Regressivos

Comentários e feedback de alunos de Modelos Regressivos da instituição Universidade Johns Hopkins

2,702 classificações
456 avaliações

Sobre o curso

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....

Melhores avaliações


Dec 17, 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.


Feb 01, 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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326 — 350 de {totalReviews} Avaliações para o Modelos Regressivos

por 桂鹏

Jun 15, 2017

sufficient depth but explnation is not sufficient in many places

por David E L B

May 18, 2017

Really helpful and well presented.

por Yuekai L

Mar 07, 2016


por Alexandros A

Feb 08, 2016

I expected more in Binomial Regression and Poisson regression

por Kevin H

Nov 09, 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

por Luong M Q

Oct 17, 2017

some complicated contents that are hard to fully grasp.

por B S

Jul 02, 2018

Nice course. It would however be better to include a summary how to approach an analysis.

por 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 .

por 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

por Gianluca M

Oct 20, 2016

To me, this is by far the best course in the series. It deals with the scientific foundation of how to do data science: regression models, residuals, measures of the quality of the prediction, etc. The teacher is clearly a mathematician and has an academic style of presenting. He is very clear and chooses the subject in a clever way. One always understands what he or she is doing.

Highly recommended. It doesn't get five stars only because it covers only the basics; I would have really liked it to last twice as much!

por Federico A V R

Sep 14, 2017

Would love to see more hands on practical explanations rather tan mostly slides.

Content is great though!

por 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.

por Norman B

Feb 08, 2016

A decent overview of regression

por Teppakorn

Jun 22, 2016

Advance topic in regression model.

por Talant R

Oct 25, 2016

Great course to learn various regression models and "R" tools to implement them efficiently, but

was little hard to keep with the deadline.

por Ankush K

Jan 16, 2018

really informative with helpful examples.

por 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.

por Bill K

Feb 10, 2016

This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.

por Manojkumar P

Nov 08, 2016

Nice Course

por 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.

por Andrea S

Feb 25, 2017

Very good material but often too fragmentend/messy. The notes would need re-writing.

por Mohamed T

Mar 19, 2018

Great course, learned a lot. The only point is that I was hoping to learn more about general linear models and its applications.

por Billy J

Apr 07, 2016

Videos were very difficult to follow along with. Overall, I learned a good amount though.

por Diego C

May 04, 2019

Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.

por Andrew

May 16, 2019

Great introduction to regression models. A ton packed into the class. Be ready to be challenged, but you'll learn a lot.