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Вернуться к Регрессионные модели

Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

Оценки: 3,307

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


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.


10 мар. 2019 г.

This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!

Фильтр по:

476–500 из 549 отзывов о курсе Регрессионные модели

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: Ben 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.

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

автор: Ansh T

22 мар. 2020 г.

Topics like logistic regression were not explained clearly

автор: Angela W

27 нояб. 2017 г.

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

автор: Gareth S

16 июля 2017 г.

Expects a level of statistical knowledge already.

автор: David S

4 нояб. 2018 г.

needed to consult external resources extensively