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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

4.4
звезд
Оценки: 3,220
Рецензии: 545

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.

Фильтр по:

451–475 из 526 отзывов о курсе Регрессионные модели

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: 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.

автор: Hendrik F

17 янв. 2016 г.

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

автор: Mark S

24 апр. 2018 г.

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

автор: Mertz

20 мар. 2018 г.

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

автор: Andres C S

1 мар. 2016 г.

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

автор: Erwin V

20 дек. 2016 г.

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

автор: Prabeeti B

17 сент. 2019 г.

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

автор: Praveen J

22 апр. 2020 г.

I think a revamping of the concepts in a more ellabroate way is required in the course

автор: Suleman W

9 нояб. 2017 г.

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

автор: Rafal K

28 февр. 2017 г.

Many things are not clear enough in multivariable regression part.

автор: Eric L

2 февр. 2016 г.

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