Voltar para Modelos Regressivos

estrelas

3,238 classificações

•

553 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....

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.

Filtrar por:

por Siddharth T S

•5 de Out de 2020

Both the video lectures and the book coast through some important topics that they should have spent more time explaining. The homework exercises and quizzes are definitely useful, but the subpar teaching efforts meant that I had to refer to outside sources for understanding the key concepts.

por Jing Z

•8 de Fev de 2016

I just realized that you have to upgrade(pay $49) in order to submit the quiz and receive the feedback. That's depressing since my purpose is to watch the video and check out what I learned so far without getting any certificate. The policy here bring huge inconvenience for people like me.

por Grigory S

•20 de Ago de 2018

One of the most difficult courses in the whole programme. From my point of view it is very important, but not so well explained. I had to go through other training sessions in order to understand the concept based on numerous practical examples and then return to Coursera to finish it up.

por Stefano G

•20 de Jul de 2017

I love the content but:

imprecision (a lot),

lack of explanation

...

for one of the most difficult subject in the specialization.

Last commit/update for the video from the teacher 1/2 year ago: are the materials update?

por Coral P

•20 de Jul de 2017

I would like to propose that instead of putting the optional reading materials at the back, it should be put up front and mandatory. Else we can't follow the videos

por Jorge P

•7 de Jun de 2016

Should cover a lot of dfificuties when the model assumptions are violated and should be for a longer time or having a second course about this theme.

por João R

•20 de Ago de 2017

Needs more practical examples. Could be rerecorded. I love mathematical theory but past week 2 it is really too theoretical, in my opinion.

por Brian

•12 de Fev de 2016

way to much emphasis on non-data science. This one course covers more information that the rest of the courses combined..

por Rich

•2 de Mar de 2016

Very difficult. Needs homework problems guided by videos like Statistical Inference coarse to make easier.

por Polly A

•3 de Mai de 2021

Would love to "Unenroll" but can't.

Can someone please take this course off my dashboard?

por Albert B

•9 de Jan de 2017

To fast pace and missing lot of content to make this lesson enjoyable!!!

por Rezoanoor/CS/Rezoanoor R

•20 de Abr de 2020

The course was nowhere near of interesting. It was arduous and boring.

por Izabela E

•12 de Ago de 2016

Difficult, fast peaced and not well explained. Requires a lot of work.

por Sepehr S

•11 de Mar de 2016

The instructor is not good and doesn't explain things clearly.

por Daniel R

•14 de Mai de 2016

Some topics that are important, are obviated

por Joseph D

•29 de Abr de 2016

Coursera keeps changing my rating. Not cool.

por Ankit S

•23 de Out de 2018

not effective for new learnners

por Vicky G

•1 de Jan de 2021

I seldom write critical comments for Coursera courses because the many courses I've taken have been quite well-designed so far. This one I feel obliged to write something, which may or may not make a difference judging by how much care was given to designing this course in the first place. From the resource allocation perspective, this course does more harm than good because the minimal amount of knowledge you gain from this course is not worth the amount of time you spent trying to figure out how the lecturer perceives and conveys statistical concepts in such a confusing way.

Bottomline: if you don't need the Data Science specialization certificate from JHU, you are WAY BETTER OFF by taking the Basic Statistics + Inferential Statistics courses provided by University of Amsterdam. I completed those two courses myself. The lecturers there truly made an effort to make the materials as engaging and intuitive as possible. You will not waste your time by taking those courses instead.

If you thought the Statistical Inference course was bad enough, try taking the Regression Models course. It refreshes your understanding about how bad a course can be. Below are some major problems:

1. The delivery of the materials is very dry. I can't tell if meaningful effort was put into creating engaging examples so that students can better understand the material. The mathematical and theoretical parts were poorly explained with inconsistent notations and insufficient elaborations about the concepts. The lecturer often jumps from very basic concepts to very advanced/complex concepts without enough transition/explanation. I had to constantly consult a friend who's very good at statistics to bridge the gaps.

2. The lecture notes are way too chaotic. Many times the PDFs provided do not match what was shown in the videos at all. Several pages in the PDFs were not covered by the video lectures, and vice versa.

3. Stepwise regression was not even covered in this course. Many students ended up using stepwise regression for the course project. Maybe students are just jumping ahead before applying the more fundamental techniques covered in this course, or maybe stepwise regression should have been covered??

4. I wish there were a lecture at the end that walks through one case study and applies most of the core techniques covered in this course. In Roger's Exploratory Data Analysis course he did one at the end and applied many things he taught in the fragmented lectures in an integrated manner. That was super helpful.

Some minor good things about this course that I did not gain from the UvA courses:

- The hodgepodge lecture provides some very interesting materials.

- The simulation examples about covariate adjustment are quite intuitive and facilitate understanding.

por Derek P

•18 de Ago de 2016

The course is essentially just a review of formulas with very little intuition explained to the beginner. It was necessary to use a collection of outside material from other courses and readings to learn the concepts. This course needs to be completely redone with a focus on developing a student's intuition for the material and then support this intuition with basic examples that build as the course progresses. A fundamental demonstration of how to use R to work through regression models (starting from square one) should be added so that this becomes a self-contained course. As it currently stands it is a collection of poorly integrated slides and concepts that serve to confuse the student more than educate. Other classes teach this material infinitely better.

por Fabiana G

•31 de Ago de 2016

I was really disappointed with this course. I took the other courses from Brian Caffo and truly enjoyed them. For the previous courses, I've always used the books and they helped me tremendously to be able to comprehend the material. There is a book for Regression Models but but it's a real mess. It feels like a draft that no one cared to take a second look. There is a bunch of wrong code and typos. The explanation doesn't go as far as it should. I had to resort to many different sources just to be able to get by the course. I hope the instructors review this course soon because it does not have the same quality as others. If they don't review it, don't bother paying for it. Try learning Regression Models elsewhere.

por Olivia U

•10 de Jun de 2020

This is, by far, the worse course of the whole specialization. The instructor has a talent to make this whole topic way more complicated than it is. I ended up auditing the Duke University course on the same subject to understand the concepts, as well as watching many youtube videos, which allowed me to properly do the course project (which is the only good thing about this course: applying what you've learned). I cannot recommend this course to anyone if it's not as part of the specialization.

por Lamont B

•21 de Set de 2020

I tried to just deal with this course and the previous one (statistical inference) because I have been doing this for a lot of years. It's because of that, I passed this class. Those with no experience will find it hard to understand what is being taught without some additional help. Additionally, nothing that is taught is focused on in the quizzes or the final project, just some pieces, so why use those as grading methods?

por Lawrence G

•5 de Jun de 2020

The most worthless waste of my time this year. I learned more in an hour of browsing external sources than I did from the entirety of the course material, which was poorly structured and extremely dull. Were I not so heavily invested in this specialisation already, I would have cancelled my subscription over it.

por Robert O

•6 de Abr de 2016

Very little depth. I don't recommend this if you don't already have background in statistics or R. I really didn't learn anything. I mostly just gamed the quizzes and projects.

por Tom

•22 de Jul de 2017

Terrible. If you want to learn about regression, even in R, go elsewhere. This course damages the brands of Johns Hopkins and Coursera...anybody heard of quality control?

- Como encontrar propósito e sentido na vida
- Compreendendo a pesquisa médica
- Japonês para iniciantes
- Introdução à computação em nuvem
- Fundamentos de Mindfulness
- Fundamentos de Finanças
- Aprendizagem Automática
- Aprendizagem automática usando o Sas Viya
- A ciência do bem-estar
- Rastreamento de Contato com a Covid-19
- IA para todos
- Mercados Financeiros
- Introdução à Psicologia
- Introdução à AWS
- Marketing internacional
- C++
- Análise Preditiva e Mineração de Dados
- Aprendendo a Aprender da UCSD
- Programação para todos da Universidade do Michigan
- Linguagem R da JHU
- Treinamento de CPI do Google CBRS

- Processamento da Linguagem Natural (PLN)
- IA para Medicina
- Bom com palavras: escrita e edição
- Modelagem de doenças infecciosas
- A pronúncia do inglês americano
- Automatização de teste de software
- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Fundamentos de negóciosFundamentos dos Negócios
- Habilidades em Excel para negócios
- Ciência de Dados com Python
- Finanças para todos
- Habilidades de comunicação para engenheiros
- Treinamento de vendas
- Desenvolvimento e gestão de marca pessoal
- Análise de Dados de Negócios da Wharton
- Psicologia Positiva da Universidade da Pensilvânia
- Aprendizagem Automática da Universidade de Washington
- Design Gráfico da CalArts

- Certificados profissionais
- Certificados MasterTrack
- Suporte de TI do Google
- Ciência de dados da IBM
- Engenharia de Dados do Google Cloud
- IA aplicada da IBM
- Arquitetura do Google Cloud
- Analista de Cibersegurança da IBM
- Automação da TI do Google com Python
- Profissional de Mainframe do IBM z/OS
- Gestão aplicada de projetos da UCI
- Certificado em Design Instrucional
- Certificado em Engenharia e Gerenciamento de Construção
- Certificado de Big Data
- Certificado de Aprendizagem Automática em Análise de Dados
- Certificado em Gestão de Inovação e Empreendedorismo
- Certificado de Sustentabilidade e Desenvolvimento
- Certificado de Serviço Social
- Certificado de IA e Aprendizagem Automática

- Graduações em Ciência da Computação
- Graduações em Negócios
- Graduações em Saúde Pública
- Graduações em Ciência de Dados
- Bacharelados
- Bacharelado em Ciência da Computação
- Mestrado em Engenharia Elétrica
- Conclusão de bacharelado
- Mestrado em Gestão
- Mestrado em Ciência da Computação
- Mestrado em Saúde Pública
- Mestrado em Contabilidade
- Mestrado em Tecnologia da Computação e da Informação
- MBA On-line
- Mestrado em Ciência de Dados Aplicada
- MBA Global
- Mestrado em Inovação e Empreendedorismo
- Mestrado em Ciência de Dados
- Mestrado em Ciência da Computação
- Mestrado em saúde pública