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Voltar para Modelos Regressivos

Comentários e feedback de alunos de Modelos Regressivos da instituição Universidade Johns Hopkins

4.4
estrelas
3,243 classificações
555 avaliações

Sobre o curso

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....

Melhores avaliações

KA
16 de Dez de 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.

BA
31 de Jan de 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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301 — 325 de 535 Avaliações para o Modelos Regressivos

por RAHULR

3 de Jul de 2020

its good

por Xiaole Z

26 de Jul de 2019

helpful!

por William A

16 de Mai de 2018

Loved it

por Maggie L

27 de Ago de 2017

Love it!

por Charles-Antoine d T

29 de Out de 2017

amazing

por Fernando M

4 de Set de 2017

Love it

por UDBODH

14 de Mai de 2016

Had fun

por Ricardo A M C

22 de Jan de 2021

thanks

por Jeff L J D

13 de Nov de 2020

Thanks

por Adán H

13 de Set de 2017

thanks

por Fredy A

5 de Jun de 2016

Great!

por Rodrigo O

16 de Abr de 2019

great

por Rafael L G

1 de Jun de 2017

Great

por Pratikchha N

31 de Mai de 2016

great

por Dror D

5 de Out de 2018

Good

por Ganapathi N K

6 de Mai de 2018

Good

por Yi-Yang L

10 de Mai de 2017

Good

por Larry G

7 de Fev de 2017

Nice

por Sidra A

13 de Abr de 2021

grt

por Amit K R

21 de Nov de 2017

ok

por Ganesh P

12 de Mar de 2018

V

por Priyanka V I

26 de Ago de 2017

.

por Andrea F

19 de Abr de 2017

B

por Benjamin G J

5 de Jan de 2016

This is the best course of the bunch so far. These courses are really promising -- I've learned a lot from them and they probably have everything they could have at the price - but I'm leaving just one star off because I feel very strongly that some effort could really go a long way to making a better language map of these courses. A person leaves this course, and even more so the inference course, not being very clear one where their new capabilities lie in the spectrum, and without the strongest sense of how to experiment with linear models.

One case in point of the the huge strengths and a slight weakness of this course -- Professor Caffo mentions the wonderfully tantalizing fact that the application of linear models can get you most of the way to the top of a Kaggle competition. That feels true, I trust him, and it's really cool. But it would be SO. MUCH. COOLER. with an article showing a linear model attacking that kind of problem.

por John D M

10 de Abr de 2019

Overall an excellent course, but there were some issues with the wrong function being specified in one quiz (Q3q6) and the wrong answer in another. Apparently it has been that way for years, according to the forum. The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that. However, the estimated time for completion of each week is ridiculously short compared to reality. Five hours? For me it was more like 20 hours, and more if I did all the Swirl exercises. Such low-balling on the time estimates is typical of the Data Science stream. The final project is given as 2 hours but it was closer to 15 for me. i wish Coursera would go back to the stream model where you could bump yourself to the next intake. That is much less stressful for busy working people like me.