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Opiniones y comentarios de aprendices correspondientes a modelos de regresión por parte de Universidad Johns Hopkins

4.4
estrellas
3,301 calificaciones
567 reseña

Acerca del 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....

Principales reseñas

KA

16 de dic. 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.

DA

10 de mar. de 2019

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!

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476 - 500 de 548 revisiones para modelos de regresión

por Raul M

16 de ene. de 2019

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.

por Ben S

20 de jun. de 2018

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

por Guilherme B D J

21 de ago. de 2016

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.

por Marco A M A

9 de may. de 2016

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.

por Rok B

28 de jun. de 2019

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

por Daniela R L

19 de abr. de 2021

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!

por Jesse K

2 de nov. de 2018

The material was a little disjointed and not always explained with examples. Passing this course required a significant amount of outside study and research.

por Jason M C

29 de mar. de 2016

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.

por Anamaria A

12 de mar. de 2017

Lots of material needs additional study (from different sources) as it's only summarily explained. Much math without the link to the praxis :-(

por Manuel M M

10 de feb. de 2020

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

por LU Z

26 de sep. de 2018

Starting from the first week swirl practice, course content is poorly organized making even simple concept difficult to understand.

por Hendrik F

17 de ene. de 2016

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

por Mark S

24 de abr. de 2018

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

por Mertz

20 de mar. de 2018

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

por Andres C S

1 de mar. de 2016

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

por Erwin V

20 de dic. de 2016

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

por Prabeeti B

17 de sep. de 2019

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

por Praveen J

22 de abr. de 2020

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

por Suleman W

9 de nov. de 2017

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

por Rafal K

28 de feb. de 2017

Many things are not clear enough in multivariable regression part.

por Eric L

2 de feb. de 2016

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

por Ansh T

22 de mar. de 2020

Topics like logistic regression were not explained clearly

por Angela W

27 de nov. de 2017

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

por Gareth S

16 de jul. de 2017

Expects a level of statistical knowledge already.

por David S

4 de nov. de 2018

needed to consult external resources extensively