Voltar para Modelos Regressivos

4.4

2,796 classificações

•

470 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.

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por William A

•May 17, 2018

Loved it

por YANAN D

•May 24, 2019

Really Helpful

por Illich M

•Jun 16, 2017

Tough course! I had to take it a couple of times to understand it.

por Yadder A G

•Mar 28, 2019

The course was incredible. You can learn a lot of skills about regression models and even more. It would be incredible if the course could have more examples or little excercises.

por Nathan M

•Jun 11, 2016

Quite useful!

por Deliang L

•Nov 02, 2016

Very helpful!

por Dale H

•May 24, 2018

I felt I had to do a lot of investigation and research into the course topics on my own.... the material is not fed to you spoonful by spoonful. But coming at it this way, I learned a lot. The more effort you invest in this course, the bigger the payoff. The knowledge gained in this course has tremendous value in the data science workplace.

por Sindre F

•Aug 01, 2016

Interesting and important course!

I don't think this course is suitable for beginners. You need to know this stuff before you take the course. Works well as a refresher.

por Carlos

•Feb 25, 2016

This class, along with "Statistical Inference" and "Machine Language" , are the meat and potato's for data science. I had taken most, if not all of these classes as an undergrad many years ago . The tools for stats have changed significantly and these classes being taught with the open source R language, really put you at the forefront of this new field.

por Wesley E

•Feb 15, 2016

Great introduction and plenty of resources for more in depth study.

por Lowell R

•Oct 07, 2016

Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.

por Carlos M

•Jul 11, 2017

I learned a lot of theory and practical applications of residuals. The swirl assignments were great too!

por gerson d o

•Nov 24, 2019

Wonderful!!!!

por Arcenis R

•Jan 18, 2016

This course is packed with great lessons and Prof. Caffo puts it all together very cogently.

por Tine M

•May 11, 2018

Definitely a difficult course but a very interesting one.

por Daniel J R

•Dec 19, 2018

Quite practical. It does encourage one to follow-up with a more advanced course.

por Alzum S M

•Jan 08, 2019

Very much thank you for teaching me such an awesome course

por David J B

•Feb 19, 2019

Probably the most conceptually challenging and practically useful course in the JH data science certification series (so far... I have a few more courses to complete).

por Nathalie D

•Mar 11, 2019

Great course, but please check those subtitles that are occasionally completely off!

por Paul R

•Mar 13, 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

por Manny R

•Mar 22, 2019

Really Fun Course. There is a lot to learn in this topic and this could be studied for a lifetime. I feel like I could apply this to discover solutions for issues at work.

por Vidya M S

•Mar 14, 2017

The concepts are well explained and precise. I think it depends on the individual to dive deeper into the topic by independent learning. Good data examples. Also following the suggested book of the author helps with some extra excercises. However , I feel extra practice questions would help .

por Utkarsh Y

•Sep 28, 2016

It is a good course for learning regression model implementation in R. You may need to have a basic understanding of popular regression models like linear & logistic as the course doesn't cover mathematical aspects in detail

por Pawel D

•Dec 18, 2016

This course is much improved, when compared with Statistical Inference. The instructor have put much effort in making the lectures interesting and casual, at the same time not loosing the value of contents. I especially liked some subtle jokes - just a finishing human touch.

Some lectures from 4th week were not very well rehearser and shot hastily. Some quiz question were disproportionately difficult, but most of material is covered in course or course materials. Otherwise the course is very educational.

por Luong M Q

•Oct 17, 2017

some complicated contents that are hard to fully grasp.

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