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Voltar para Modelos Regressivos

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

4.4
estrelas
2,848 classificações
475 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

KA

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.

BA

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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401 — 425 de {totalReviews} Avaliações para o Modelos Regressivos

por Manuel M M

Feb 10, 2020

The content was exposed in a very confused manner. I did not like how the teacher explained. It seemed more difficult than it really is

por LU Z

Sep 26, 2018

Starting from the first week swirl practice, course content is poorly organized making even simple concept difficult to understand.

por Hendrik F

Jan 17, 2016

I find it very tough to understand everything. Buying the course book helps to overcome this. You have to dedicate a lot of time.

por Mark S

Apr 24, 2018

Lots of math, but it would be more productive to focus more on the output of R and better understand the results

por Mertz

Mar 20, 2018

Bad audio and video quality. Too fast on some complex ideas and too slow when come repetitions between videos...

por Andres C S

Mar 02, 2016

I think this course needs more emphasis on practical applications and less mathematical background.

por Erwin V

Dec 20, 2016

Very interesting course, yet course content could be spread more evenly (week 4 is really a lot)

por Prabeeti B

Sep 17, 2019

Course has more theoretical concept than application.. It has to be more application based

por Suleman W

Nov 10, 2017

I did find it difficult to follow and understand some of the materials.

por Rafal K

Feb 28, 2017

Many things are not clear enough in multivariable regression part.

por Eric L

Feb 03, 2016

good quick overview, could have more actual R examples in lectures

por Angela W

Nov 27, 2017

I learned a lot, but it was so much content for 4 weeks!

por Gareth S

Jul 16, 2017

Expects a level of statistical knowledge already.

por David S

Nov 05, 2018

needed to consult external resources extensively

por Lei M

Aug 23, 2017

Some of the materials are too much math for me.

por xuwei l

Sep 22, 2016

the lecture notes is a bit confusing

por Marcela Q

Jan 06, 2020

Terrible professor, good book

por Hani M

Oct 24, 2017

was tough

por Liew W P

Aug 29, 2016

With all respect, Professor, if you are reading all our comments, I think you are a really smart person and you should take all the negative feedback from your students here, positively and constructively. As having good knowledge will never be equals to able to produce good students. Personally, I feel that you should lower down yourself and speak to the level of your students/audience. Use more simple examples, draw a big picture in our minds on what is this course all about, what are we going to achieve, in each of the topics, what are we going to look at and what methods available.

For foundation class like this, I think few simple examples and introducing one or two useful methods in each topic would be more than sufficient to us. The objective should be providing us with the basic knowledge, get us interested in this subject, and able to apply those well taught basic knowledge. As when we are interested, for sure we will go and do more research, and some might even would like to move on to intermediate or advance levels.

The key point here is "speak to the level of your audience". Even if you are able to talk everything above the sky and up to the moon. If no one able to understand you, it is useless.

por Barry S

Mar 15, 2016

This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material. Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

A more structured final project would have been helpful. The instructions on this project weren't quite so blunt as to say "Take this data set, do some regression-y stuff and come back with something about these two variables," but that's basically as far as our instructions went. It could have been a great learning experience to have a more detailed guide through the construction of a regression analysis, but instead an assignment which was 40% of our grade was put together as an afterthought. It was the assignment equivalent of stopping in the 7-11 a block away from a birthday party to buy a card.

Also, in terms of delivering the content: Mr. Caffo needs to structure his slide/video arrangements so that he is not standing in front of the text. Think of it from the point of view of somebody wanting to listen and read at the same time.

por Mohamed A

Nov 02, 2016

This course failed greatly to balance the workload by week. The third week which I think was the most important one have too many information to learn and assimilate whereas the first two weeks could be rearranged to start multivariate regression earlier. Another proof of week 3 issue: the related swirl exercises start in week2 (2 of them) and finish in week4 (2 more exercises) !!!!!

I think one of the most important expertise and knowledge that a data scientist must know and master was unfairly squeezed in one week leaving no time for the learner/student to do more search/exercises on the subject.

por Pedro J

Jun 06, 2016

The professor doesn't explain clearly as part of the videos is his correcting himself or saying the same thing two or three times. And why must the videos show the teacher? It distracts from the slides and seeing him move doesn't help understand anything better

Concepts like VIF or hat values are not very well explained by the teacher, at least the SWIRL lesson explains it correctly. ANOVA and ANCOVA are mentioned in the description but they aren't explained anywhere. ANOVA is used without any explanation of what it is.

I found myself searching online for other sources to understand the concepts.

por Lee D

Sep 30, 2016

I again found many of the lectures to be difficult to follow along, there seems to be lots of different styles of videos in the way that the person was superimposed on the slides. In fact it was often impossible to read the text in the slide due to the size of the presenters head which obscured the text. Honestly this data science course is getting worse as the months progress, you really should think of updating the content of the course if you want to continue to charge money for it. 2 stars as I did actually learn something despite the quality of the material and its delivery.

por Brian S C

Mar 01, 2016

Overall okay course but the lectures are too focused on theory with some applications to the real world. I think this course needs to be reconfigured and taught from an applied focus instead of 30% applied 70% theory.

Also the new format is horrible and TAs are nonexistent as are discussions in general on the forums now. The TAs were a critical learning component before especially considering that unlike on EdX where course staff actually participates in the forums, on Coursera I do not think I have ever observed course staff actively participating in the forums.

por Benjamin S

Jan 12, 2018

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.