Voltar para Modelos Regressivos

4.4

estrelas

3,185 classificações

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536 avaliações

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....

MM

12 de Mar de 2018

Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.

KA

16 de Dez de 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.

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por R. H

•19 de Mar de 2020

The timing on this course is very inaccurate - it should take much longer than 4 weeks, 6 weeks at the absolute minimum. I say this because Week 4 has so much information crammed in of all different types of General Linear Models (i.e. models that are not necessarily a straight line). Binomials, Poisson, splines - each of these topics could have their own weeks, but instead they are quickly summarized for one week with the student expect to understand them for the quiz. The other issue, which has been a problem with all courses in this specialization, is the discussion boards. They are totally abandoned by mods; good luck finding any post that isn't "grade my project? I'll grade yours!" despite a mod post that says such requests will be deleted. The board is totally flood with those requests, and makes me wonder how many people are passing these classes wrongly because "if u give me 100 i will grade yours too!" It totally devalues the program. The creators seemingly abandoning Coursera have made this certificate a waste.

por Kaspar M

•12 de Out de 2020

There's some useful material in the course. There were some major issues though: 1) there is so much to cover that this really ought to be broken into two courses or more. It is not a 4-week course. It would really be helpful to break it into chunks and include some more comprehensive exercises so the learner can get a full grasp of the subject. The quizzes, particularly the final one, were curiously disconnected from the course material. The final project as assigned was just straight-out baffling. I noticed some learners submitting garbage solutions for review, presumably just so they could look at what other people were doing to figure out what they were supposed to be doing. Oh one more thing: Caffo never explains what ANOVA is, he just starts using it. Overall: I would like to know who is doing a well-designed MOOC on this, because I would like to take it.

por Mohamed A

•2 de Nov de 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

•6 de Jun de 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

•29 de Set de 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

•1 de Mar de 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 Simon

•1 de Set de 2017

The concepts behind this course are really important. However, I feel that the material is not up to the needed level.

I am missing a good solid material that explains properly the theory behind these methods. I had to revert to other books (that could have well showed up as references in the course material) to get a proper understanding.

por Thej K

•13 de Mai de 2019

Worst teaching by Brian Caffo! typos in quizes after 4 years even. And brian has put very littel effort into making it digestable for students. Look at his lectures on youtube and I have commented at each lecture! So bad. A simple googling outside of his notes was so much more better for understanding regression!

por Daniel M

•20 de Jan de 2016

Un curso difícil de entender si no tienes la base matemática de regresión. Uno no sabe por dónde empezar, cualquiera de los cursos de esta serie (Statistical Inference, R programming...) pareciera que te saturan de información. Es bueno para curiosos con bases en R y que quieren saber más de Regresión

por Siddharth T S

•5 de Out de 2020

Both the video lectures and the book coast through some important topics that they should have spent more time explaining. The homework exercises and quizzes are definitely useful, but the subpar teaching efforts meant that I had to refer to outside sources for understanding the key concepts.

por Jing Z

•8 de Fev de 2016

I just realized that you have to upgrade(pay $49) in order to submit the quiz and receive the feedback. That's depressing since my purpose is to watch the video and check out what I learned so far without getting any certificate. The policy here bring huge inconvenience for people like me.

por Grigory S

•20 de Ago de 2018

One of the most difficult courses in the whole programme. From my point of view it is very important, but not so well explained. I had to go through other training sessions in order to understand the concept based on numerous practical examples and then return to Coursera to finish it up.

por Stefano G

•20 de Jul de 2017

I love the content but:

imprecision (a lot),

lack of explanation

...

for one of the most difficult subject in the specialization.

Last commit/update for the video from the teacher 1/2 year ago: are the materials update?

por Coral P

•20 de Jul de 2017

I would like to propose that instead of putting the optional reading materials at the back, it should be put up front and mandatory. Else we can't follow the videos

por Jorge P

•7 de Jun de 2016

Should cover a lot of dfificuties when the model assumptions are violated and should be for a longer time or having a second course about this theme.

por João R

•20 de Ago de 2017

Needs more practical examples. Could be rerecorded. I love mathematical theory but past week 2 it is really too theoretical, in my opinion.

por Brian

•12 de Fev de 2016

way to much emphasis on non-data science. This one course covers more information that the rest of the courses combined..

por Rich

•2 de Mar de 2016

Very difficult. Needs homework problems guided by videos like Statistical Inference coarse to make easier.

por Albert B

•9 de Jan de 2017

To fast pace and missing lot of content to make this lesson enjoyable!!!

por Rezoanoor/CS/Rezoanoor R

•20 de Abr de 2020

The course was nowhere near of interesting. It was arduous and boring.

por Izabela E

•12 de Ago de 2016

Difficult, fast peaced and not well explained. Requires a lot of work.

por Sepehr S

•11 de Mar de 2016

The instructor is not good and doesn't explain things clearly.

por Daniel R

•14 de Mai de 2016

Some topics that are important, are obviated

por Joseph D

•29 de Abr de 2016

Coursera keeps changing my rating. Not cool.

por Ankit S

•23 de Out de 2018

not effective for new learnners

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