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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:

401–425 из 549 отзывов о курсе Регрессионные модели

автор: Scipione S

14 июля 2020 г.

I suggest to revie some videos. There is some repetition, especially in week 3.

автор: Marijus B

28 апр. 2020 г.

swirl exercises needs to be fixed, could not complete it because of the bug

автор: BIBHUTI B P

24 июля 2017 г.

Wonderful experience of assimilating the techniques and tricks in this mod

автор: Sudheer P

28 дек. 2016 г.

This is a great course. The content clearly explains the regression model.

автор: Koen V

23 сент. 2019 г.

The explanation of the right answers from the quiz were quite handy!

автор: Humberto R

13 февр. 2018 г.

Great course. My prefered so far in the data science specialization

автор: Mingda W

5 июня 2018 г.

Great, but need more examples and projects to practice the skills.

автор: antonio q

21 мар. 2018 г.

to me the more challenging course, well done though, thanks a lot

автор: Hariharan D

11 сент. 2017 г.

Intuitive course, liked it. Technical equations are challenging.

автор: 桂鹏

15 июня 2017 г.

sufficient depth but explnation is not sufficient in many places

автор: Piotr K

23 окт. 2016 г.

Sometimes videos were hard to understand, especially in week 3.

автор: Frank O

12 июля 2021 г.

Mathematically difficult topic for me, but very well conveyed

автор: Alexandros A

8 февр. 2016 г.

I expected more in Binomial Regression and Poisson regression

автор: Yiyang Z

24 авг. 2019 г.

Very informative, but could be more interesting and concise.

автор: Manuel E

3 июля 2019 г.

Hard class, documentation could be better, but good content.

автор: Alzum S M

8 янв. 2019 г.

Very much thank you for teaching me such an awesome course

автор: Pooia L

13 сент. 2018 г.

This is a very nice course provided you study a lot for it

автор: Karthik R

7 авг. 2017 г.

Knowledge on Statistics will help in better understanding.

автор: Luong M Q

16 окт. 2017 г.

some complicated contents that are hard to fully grasp.

автор: H Y

8 февр. 2017 г.

Content regarding variable selection is kind of rough.

автор: Camilo Y

14 мар. 2017 г.

Great introduction to regression models. Pretty clear

автор: Madhuri

26 окт. 2016 г.

prerequisites are very mandatory to do this course

автор: pulkit k

9 июня 2018 г.

It lacked practical application, not impressed.

автор: Mitraputra G

14 янв. 2017 г.

A little monotonous sometimes. Otherwise good.

автор: Mehrshad E

18 дек. 2017 г.

I found SWIRL more helpful than the lectures.