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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

4.4
звезд
Оценки: 3,248
Рецензии: 557

О курсе

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....

Лучшие рецензии

KA
16 дек. 2017 г.

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
31 янв. 2017 г.

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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451–475 из 537 отзывов о курсе Регрессионные модели

автор: Satish V

8 апр. 2019 г.

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).

автор: Deepanshu R

23 июня 2020 г.

Some of the course lectures introduced a lot of new terms hampering the actual topic being discussed. I know we are expected to do a lot of self-learning. But, I found some random youtube videos more explanatory than some of the lectures here. I could understand the concepts better through those youtube videos because they were more easy-flowing and less cluttered.

автор: Mark B

30 мар. 2021 г.

The material was relevant, but the project related to material that was not covered in the course. As a result, there was clearly rampant cheating on the project. The first two that I reviewed were word for word exactly the same. Others appeared to be very similar. I do not know why projects are not run through screening software when submitted.

автор: Asif M A

23 окт. 2016 г.

I enjoyed the earlier courses more. I did not like the way the materials were provided. There were a lot of very complex ideas were presented, in a very concise and brief manner. Also, there should be more exercises to practice. May be its me, but, I guess, I might need more time to fully comprehend the materials.

автор: Boban D

7 мая 2018 г.

Much better than the inference course given by Mr. Caffo. This time at last I could follow the materials being covered. He is plotitng more often and scribbling on the slides which helps understanding the materials being covered by establishing a connection between the isolated issues in regression analysis.

автор: Codrin K

28 мар. 2018 г.

To me, the approach was too much from the theory of statistics and its mathematical foundations; I would have appreciated a more applied approach for this course in the specialization. So starting from examples, questions anout data and then working towards theory instead of the other way around.

автор: VenusW

9 янв. 2017 г.

This course is great, instructor is good, however, the material of this course is not well organized, even the swirl practice is not put in the correct week, not in the same pace as the lectures. The quiz and project are far much easier than lecture content.

автор: Brandon K

30 мар. 2016 г.

I found the videos tough to watch. I was hoping for something that would be more practical for non-statisticians, but the lectures mainly devolved into mathematical proofs. That said, I did learn some from this class. Just not as much as I'd hoped.

автор: Zach

4 февр. 2016 г.

There's just something about the course content that is difficult to attain. It's presented at way too high of a level without enough tangible examples of getting down into the weeds of how to actually perform and interpret the models and functions.

автор: 장진욱

14 февр. 2016 г.

The flows of courses instructed by Caffo(Statistical Inference and Regression Models) are too long to concentrate it and the quiz is ​not quite related in lecture.

However, Contents of the book is really good, as well as homework in the book.

автор: Sarah R

20 мар. 2016 г.

The instructor is at time incomprehensible. It would be helpful to speak more slowly and pause more often. Otherwise he sounds like repeating something that he's so well memorized after many years of teaching.

автор: Ramesh G

4 июня 2020 г.

Good introduction to linear regression models but fell awfully short on diving a little deep into GLMs and going through use cases to convey how models are built, evaluated and updated in a systemic manner.

автор: Fulvio B

27 апр. 2020 г.

The course is interesting but probably overambitious. I think that if you do not have previous experience, with the material provided, it would be hard to have a real understanding of the topics covered.

автор: Pepijn d G

23 мая 2016 г.

The course is good. Unlike the previous courses I took in this track, there was almost no interaction in the forums and also no-one to give feedback. I wonder if there were any TA's present in this run.

автор: Raul M

16 янв. 2019 г.

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

автор: benjamin s

20 июня 2018 г.

A good (although slightly frustrating) course, attempted once but had to come back after studying the material in class, quite a heavy course if you've not been taught regression before

автор: Guilherme B D J

21 авг. 2016 г.

Given the importance of this subject, this course should have been split in two or more or have a longer duration to properly address subjects as GLM or model selection techniques.

автор: Marco A M A

9 мая 2016 г.

This course is better than Statistical Inference, and I think it is as useful. Non credit excersise are still very good at helping with understanding in practice what is going on.

автор: Rok B

28 июня 2019 г.

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

автор: Daniela R L

19 апр. 2021 г.

These videos are better than the previous ones in this specialization but it gets too repetitive and long and boring. The swirl activities are the way to go!

автор: Jesse K

2 нояб. 2018 г.

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

автор: Jason M C

29 мар. 2016 г.

This is a decent class, covering linear regression and a few of its variants in good detail. It's a challenging subject, but presented acceptably here.

автор: Anamaria A

12 мар. 2017 г.

Lots of material needs additional study (from different sources) as it's only summarily explained. Much math without the link to the praxis :-(

автор: Manuel M M

10 февр. 2020 г.

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

автор: LU Z

26 сент. 2018 г.

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