Voltar para Modelos Regressivos

4.4

2,740 classificações

•

464 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....

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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por Jesse K

•Nov 03, 2018

The material was a little disjointed and not always explained with examples. Passing this course required a significant amount of outside study and research.

por David S

•Nov 05, 2018

needed to consult external resources extensively

por Satish V

•Apr 08, 2019

The instructor's delivery and content, although very professorial was very dry. For students who don't have that much of a background in regression and statistical inference, I think it would be good to get to the gist/summary - i.e the what (what kind of problem we are trying to solve) and the how (how to do it in R and more importantly how to interpret the results).

por Rok B

•Jun 28, 2019

Useful class, but the content often simple in nature was explained in a confusing/complicated way. But the material is important and there is purchase for taking the class

por Will J

•Sep 22, 2019

Pros: The instructors of this course are absolutely knowledgable on the content here. The content itself is challenging and applicable to real-world data science challenges. Using R makes this a good course for today's (2019) current programming world as many professional statisticians will use this language day-to-day.

Cons: The content feels mismanaged. Sometimes the Lectures don't prep you for the practice assignments, and sometimes neither of those prep you for the quizzes particularly well. I had also hoped for some more engaging video content from a course this expensive. Having a professor in his office hastily work through material while there are police sirens outside isn't exactly pro-level instruction (It is in Baltimore, so I get it).

Overall, it's worth it if you've got the time to power through relatively dull lectures. The R based practice assignments are wonderful and the final project incorporates things together nicely.

por Prabeeti B

•Sep 17, 2019

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

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 Rich

•Mar 03, 2016

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

por Simon

•Sep 01, 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 Jing Z

•Feb 08, 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 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 Albert B

•Jan 09, 2017

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

por Sepehr S

•Mar 11, 2016

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

por Jorge P

•Jun 07, 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

•Aug 20, 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 Daniel M G

•Jan 21, 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 Brian

•Feb 12, 2016

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

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 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 Joseph D

•Apr 29, 2016

Coursera keeps changing my rating. Not cool.

por Izabela E

•Aug 12, 2016

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

por Ritu B

•Feb 07, 2016

Appears more like a revision for those who already know the content than geared towards those new to the subject.

por Coral P

•Jul 20, 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 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.

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