Voltar para Modelos Regressivos

estrelas

3,241 classificações

•

554 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 Vincent G

•9 de Out de 2017

fantastic course

por Nevon L D

•27 de Set de 2018

Builds Heavil

por Mariano F

•12 de Jun de 2016

Great course.

por Anup K M

•22 de Out de 2018

good content

por Mohammad M

•12 de Abr de 2021

informative

por Dora M

•30 de Mar de 2019

Good class.

por Khairul I K

•23 de Mar de 2017

2 thumbs up

por Manojkumar P

•8 de Nov de 2016

Nice Course

por Rohit K S

•21 de Set de 2020

Nice one!!

por Johnnery A

•12 de Fev de 2020

Excellent!

por Mohamed A E M

•3 de Jan de 2018

Great Deal

por Timothy V B

•19 de Mai de 2017

good intro

por Yuekai L

•7 de Mar de 2016

Nice.

por Normand D

•1 de Fev de 2016

As for the Statistical Inference course, this course is amazing but is presented in a more complex way than it should be. Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.

Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing. Too often the details are thrown at us without being properly framed in the context or without having the proper concept being introduced progressively.

I have a theory about teaching since I was 15, and so far it has proven to be true. Imagine that learning is about climbing a mountain in which tall steps have been carved. Each step is taller than the student. The teacher is somewhere higher than the students (not necessarily at the top, if there is such a thing).

The job of the teacher is to throw boxes (concepts) and balls (details) of different size, shape and colors. The job of the student is to catch these boxes and balls and to put the right balls in the right boxes in order to make a staircase out of it to climb (at least) one of the giant stair up.

A good teacher makes sure to throw the concepts first than the details and to clearly specify which balls go into which box, as well as which boxes go inside/over which other boxes.

But most teacher simply throw the balls and boxes in an not so well structured manner, so the poor students try to catch as many as he can, but also miss a lot of them. His hands can hold a limited amount of balls. If he doesn't have the right box to put them, he would either miss the next balls, or put the one he hold in his hand in the wrong box.

Bottom line, the best teachers are those who focus on the concepts (and context) and make sure that the concepts are well understood before introducing details to stuck in these concepts. From my experience our brain (or at least mine) better learn this way. It is as if our brain need first to establish a category-pattern (the concept/context) to which it will associate detail-patterns. But without a proper category-pattern, our brain is having a hard time to properly remember the detail-patterns or miss-associate them to the wrong category-pattern (which create even more confusion).

Hope it was helpful somehow...

por Will J

•22 de Set de 2019

Pros: The instructors of this course are absolutely knowledgable on the content here. The content itself is challenging and applicable to real-world data science challenges. Using R makes this a good course for today's (2019) current programming world as many professional statisticians will use this language day-to-day.

Cons: The content feels mismanaged. Sometimes the Lectures don't prep you for the practice assignments, and sometimes neither of those prep you for the quizzes particularly well. I had also hoped for some more engaging video content from a course this expensive. Having a professor in his office hastily work through material while there are police sirens outside isn't exactly pro-level instruction (It is in Baltimore, so I get it).

Overall, it's worth it if you've got the time to power through relatively dull lectures. The R based practice assignments are wonderful and the final project incorporates things together nicely.

por Janardhan K

•16 de Nov de 2017

The course was of average quality. It could have been better. Brian's slides in the video don't correspond 1-1 with the slides made available. The coverage and explanation of the material could have been better. The instructor's presentation could be more engaging (fewer 'ums' while talking). It was not immediately clear how to answer some questions on the Week 4 quiz, and also the course project, even after reviewing the material multiple times. One example: Brian says that the ANOVA test can only be used to compare models, when the model being compared has normally distributed residuals (using the Shapiro test). No advice is given about what to do if they are not normally distributed, which is what happened in the project.

por Raphael R

•31 de Out de 2016

I am no used to this educational system so I find difficult to follow without any proof or demonstration of the mathematical tools. I find proofs necessary for a good understanding of concepts. Another benefit of proof will be to have a more rigorous framework for variable names in the explanations. Even though this is more a practical course, it will benefit from being a bit more rigorous ; so at least people can make proofs on they own.

Other than that, it is a great course. Very practical and to the point.

por Amol K

•31 de Jan de 2016

This course goes on a very fast pace and simply does not have the charm of all the other courses in the specialization. I understand that a lot of content is covered within a month, but there should be supplementary course material available. Moreover, TAs should be more active on the forums. I have seen most of the questions just being discussed among the students. A little disappointed. Will probably have to watch all the material again to have confidence with it.

por Erick J G L

•31 de Jan de 2018

Lots of room for improvement on this course, the teacher really seems like he cares but he is a really bad teacher nonetheless. The course material is incomplete and not properly structured. Basically read the book if you want to learn something, otherwise the videos don't really help.

Also, the course project is not worth it because you get no real feedback to compare your project to the ideal or at least expected answer. I would not recommend this course.

por Andrew R

•7 de Mar de 2016

The material presented was of course useful, but I never really felt like I understood how it all tied together, or what the big picture was. I think that some case studies that show how all of the concepts relate to one another, or how they are used in the bigger picture would be helpful.

Also, as a suggestion, I feel that if something is important enough to be included in the quiz, it merits more than the briefest of mentions in the lecture.

por Ahmad A

•9 de Nov de 2016

Requires much more than a month to digest the material and complete the assignments. A default/initial one-week offering is too tight unless you are only taking the course (not working). I know one can complete the course in more than a single round and I did that but I still don't think the expectations should be set for a single month.

Instruction (video content) can be much better, at least compared to a lot of other courses on Coursera.

por ANDREW L

•27 de Jan de 2016

Better than Stat Inference, and gave some reasonable intuition, but could be improved I think by focussing on more understanding and less maths and formulas. Some of it did seem to be - here' s a formula, plug the numbers in to get the quiz question right, whereas in reality (in the world of work) that question is completely unrealistic - you have raw data and you need to do the regression and understand what it means.

por Feng H

•16 de Mai de 2017

Not impressed. Dr. Caffo tried to use non-calculus, non-linear-algrebra ways to explain complex concepts and derivations. IMO, he should not have done that. It only made things more confusing. Also the final project is so unsatisfactory in that we were to analyze the data with 32 obs but 11 variables! How robust could it be? Was expecting something much more challenging than that.

por Satish V

•8 de Abr de 2019

The instructor's delivery and content, although very professorial was very dry. For students who don't have that much of a background in regression and statistical inference, I think it would be good to get to the gist/summary - i.e the what (what kind of problem we are trying to solve) and the how (how to do it in R and more importantly how to interpret the results).

por Deepanshu R

•23 de Jun de 2020

Some of the course lectures introduced a lot of new terms hampering the actual topic being discussed. I know we are expected to do a lot of self-learning. But, I found some random youtube videos more explanatory than some of the lectures here. I could understand the concepts better through those youtube videos because they were more easy-flowing and less cluttered.

- 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