Voltar para Modelos Regressivos

4.4

estrelas

2,874 classificações

•

482 avaliações

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....

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.

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.

Filtrar por:

por Jesse K

•Nov 03, 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

•Mar 29, 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

•Mar 12, 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

•Feb 10, 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

•Sep 26, 2018

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

por Hendrik F

•Jan 17, 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

•Apr 24, 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

•Mar 20, 2018

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

por Andres C S

•Mar 02, 2016

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

por Erwin V

•Dec 20, 2016

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

por Prabeeti B

•Sep 17, 2019

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

por Suleman W

•Nov 10, 2017

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

por Rafal K

•Feb 28, 2017

Many things are not clear enough in multivariable regression part.

por Eric L

•Feb 03, 2016

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

por Ansh T

•Mar 22, 2020

Topics like logistic regression were not explained clearly

por Angela W

•Nov 27, 2017

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

por Gareth S

•Jul 16, 2017

Expects a level of statistical knowledge already.

por David S

•Nov 05, 2018

needed to consult external resources extensively

por Lei M

•Aug 23, 2017

Some of the materials are too much math for me.

por xuwei l

•Sep 22, 2016

the lecture notes is a bit confusing

por Marcela Q

•Jan 06, 2020

Terrible professor, good book

por Hani M

•Oct 24, 2017

was tough

por Liew W P

•Aug 29, 2016

With all respect, Professor, if you are reading all our comments, I think you are a really smart person and you should take all the negative feedback from your students here, positively and constructively. As having good knowledge will never be equals to able to produce good students. Personally, I feel that you should lower down yourself and speak to the level of your students/audience. Use more simple examples, draw a big picture in our minds on what is this course all about, what are we going to achieve, in each of the topics, what are we going to look at and what methods available.

For foundation class like this, I think few simple examples and introducing one or two useful methods in each topic would be more than sufficient to us. The objective should be providing us with the basic knowledge, get us interested in this subject, and able to apply those well taught basic knowledge. As when we are interested, for sure we will go and do more research, and some might even would like to move on to intermediate or advance levels.

The key point here is "speak to the level of your audience". Even if you are able to talk everything above the sky and up to the moon. If no one able to understand you, it is useless.

por Barry S

•Mar 15, 2016

This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material. Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

A more structured final project would have been helpful. The instructions on this project weren't quite so blunt as to say "Take this data set, do some regression-y stuff and come back with something about these two variables," but that's basically as far as our instructions went. It could have been a great learning experience to have a more detailed guide through the construction of a regression analysis, but instead an assignment which was 40% of our grade was put together as an afterthought. It was the assignment equivalent of stopping in the 7-11 a block away from a birthday party to buy a card.

Also, in terms of delivering the content: Mr. Caffo needs to structure his slide/video arrangements so that he is not standing in front of the text. Think of it from the point of view of somebody wanting to listen and read at the same time.

por R. H

•Mar 19, 2020

The timing on this course is very inaccurate - it should take much longer than 4 weeks, 6 weeks at the absolute minimum. I say this because Week 4 has so much information crammed in of all different types of General Linear Models (i.e. models that are not necessarily a straight line). Binomials, Poisson, splines - each of these topics could have their own weeks, but instead they are quickly summarized for one week with the student expect to understand them for the quiz. The other issue, which has been a problem with all courses in this specialization, is the discussion boards. They are totally abandoned by mods; good luck finding any post that isn't "grade my project? I'll grade yours!" despite a mod post that says such requests will be deleted. The board is totally flood with those requests, and makes me wonder how many people are passing these classes wrongly because "if u give me 100 i will grade yours too!" It totally devalues the program. The creators seemingly abandoning Coursera have made this certificate a waste.

- IA para todos
- Introdução ao TensorFlow
- Redes neurais e aprendizagem profunda
- Algoritmos, parte 1
- Algoritmos, parte 2
- Aprendizagem Automática
- Aprendizagem automática com Python
- Aprendizagem automática usando o Sas Viya
- Linguagem R
- Introdução à programação com Matlab
- Análise de dados com Python
- Fundamentos da AWS: Going Cloud Native
- Fundamentos da Google Cloud Platform
- Engenharia de confiabilidade do site
- Fale inglês profissionalmente
- A ciência do bem-estar
- Aprendendo a Aprender
- Mercados Financeiros
- Testes de hipóteses em saúde pública
- Princípios da liderança no cotidiano

- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Ciência de dados aplicada com Python
- Fundamentos de negóciosFundamentos dos Negócios
- Arquitetura com o Google Cloud Platform
- Engenharia de dados em Google Cloud Platform
- Excel para MySQL
- Aprendizagem de máquina avançada
- Matemática para aprendizagem automática
- Carros autoguiáveis
- Revolução do Blockchain para a empresa
- Análises empresariaisAnálises Empresariais
- Habilidades em Excel para negócios
- Marketing digitalMarketing Digital
- Análise estatística com R para saúde pública
- Fundamentos da imunologia
- Anatomia
- Gestão da inovação e Design Thinking
- Princípios da psicologia positiva