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Voltar para Modelos Regressivos

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

4.4
estrelas
2,848 classificações
475 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

Dec 17, 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

Feb 01, 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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251 — 275 de {totalReviews} Avaliações para o Modelos Regressivos

por Xiaole Z

Jul 26, 2019

helpful!

por William A

May 17, 2018

Loved it

por Maggie L

Aug 28, 2017

Love it!

por Charles-Antoine d T

Oct 29, 2017

amazing

por Fernando M

Sep 04, 2017

Love it

por UDBODH

May 14, 2016

Had fun

por Adán H

Sep 13, 2017

thanks

por Fredy A

Jun 05, 2016

Great!

por Rodrigo O

Apr 16, 2019

great

por Rafael L G

Jun 01, 2017

Great

por Pratikchha N

May 31, 2016

great

por Dror D

Oct 05, 2018

Good

por Ganapathi N K

May 06, 2018

Good

por Yi-Yang L

May 10, 2017

Good

por Larry G

Feb 07, 2017

Nice

por Amit K R

Nov 21, 2017

ok

por Ganesh P

Mar 12, 2018

V

por Priyanka V I

Aug 26, 2017

.

por Andrea F

Apr 19, 2017

B

por Benjamin G J

Jan 05, 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

Apr 10, 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.

por Siying R

Aug 10, 2019

The lecture is pretty dry to me who had limited vocabulary in the field. It made me went out to find other easier lectures to help me understand. The lecture focus on explaining the basic concept of Regression Models and spend a big chunk of time to explain how the function works. I would prefer to have more time explaining what the numbers mean for the data. The questions in the quiz require us to understand the meaning of the data, so we know what function and number to apply. Maybe it is just me, finding it very challenging to see the connection between the lecture and the quiz.

por Romain F

Apr 01, 2017

Great course, although feeling as always a bit rushed on the last lectures. At least it makes you want to investigate more about the subject.

I find frustrating however not to have a proper instructor example of the final assignment, it is hard to review other participants work and realize what they / you have done wrong without actually knowing how best the assignment should have been fulfilled.

And as all courses in this specialization, there is not much interaction between participants, and not much effort by mentors to animate it

por Gianluca M

Oct 20, 2016

To me, this is by far the best course in the series. It deals with the scientific foundation of how to do data science: regression models, residuals, measures of the quality of the prediction, etc. The teacher is clearly a mathematician and has an academic style of presenting. He is very clear and chooses the subject in a clever way. One always understands what he or she is doing.

Highly recommended. It doesn't get five stars only because it covers only the basics; I would have really liked it to last twice as much!

por Michael H

Jan 21, 2018

Thanks for the great course! I think the following can be improved: 1) More depth, I find myself keep looking for additional materials from other sources, e.g. proof of different theories, the course only provides overview, but didn't go deep enough 2) Project: I find the optional quiz project more interesting, the final project is too simple, and didn't include things we learnt such as GLM etc. A more comprehensive final project with more aspects of courses knowledge will be much better to re-solidate learning