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Avis et commentaires pour d'étudiants pour Modèles de régression par Université Johns-Hopkins

4.4
étoiles
3,203 évaluations
540 avis

À propos du cours

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....

Meilleurs avis

KA
16 déc. 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 janv. 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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51 - 75 sur 520 Avis pour Modèles de régression

par Sai S S

9 juil. 2017

Thanks much. Good course. Would have loved a tougher final project (eg. using logistic regression). How about adding two variants for all final projects - 1. lots of things to do vs. 2. more technically complex ?

par Marco C

24 avr. 2018

I studied Regression Models in other courses, but only now I feel I'm in the matter. Thanks to the Instructor for the really good explanation and especially for the ability to convey the passion for Statistics.

par Mikhail M

10 sept. 2016

Extremely useful and exciting. Everything from previous modules fall in places and you may see some practical implementations from the course. By the way R is awesome!

Many thanks to faculty, you do a great job.

par Samer A

10 juil. 2018

Great Course. Brian Caffo has a way to explain regression without sinking deep into hard math. You obviously need to walk the extra mile and search for yourself, but the course definitely gives you the map.

par Massimo M

13 mars 2018

Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.

par Kristin A

17 déc. 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.

par Keidzh S

3 juil. 2018

Strong and effective course. Completely makes better my math skilss. Thank you Brian Caffo and other masters for this course. Looking forward to start tne next course from John Hopkins University.

par Alexis C

11 août 2017

Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!

par Ivan Y

14 févr. 2018

I learned a lot through this course! It's not easy, and there's a lot of technical details that required me to watch the videos 2-3 times through to have a proper grasp, but super helpful stuff!

par Alán G

28 mai 2019

It is an excellent initial approach to Regression Models. I was able to apply some of the models in my work. Further analysis of the mathematical and statistical theory is highly recommended.

par Camilla J

4 janv. 2018

The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..

par Lowell R

7 oct. 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.

par Sanjeev I

29 févr. 2016

The course content was very brief and well structured, Regression being a rather vast topic demands a lot more time. 4 weeks seemed a bit less! Overall satisfied by what the course offered.

par Vinicio D S

23 avr. 2018

Great course to get the basics on Linear Models and Inference. Great Introduction to Logistic Regression and Poisson Regression. Good emphasis in Diagnostics of the main assumptions

par Steven C

15 mars 2017

Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.

par Boris K

29 oct. 2019

Along with the Statistical Inference Class and Building Predictive Models Class this is one of the best in this Specialization. It is reasonably tough, well-taught, overall great.

par Yadder A

27 mars 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.

par Juan V

16 oct. 2017

It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.

par Andretti

1 mars 2017

This course has been the most difficult in the Dara Science track so far, but you get a more in depth knowledge in data analysis and interpretation based on statistical models.

par Daniel C J

2 août 2017

Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!

par Sindre F

1 août 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.

par Aisha H

2 févr. 2016

Loved the course and the content. Only critique is that I would have liked to have a lecture about transformations, and interpretation of transformed data coefficients.

par Connor G

18 sept. 2017

Extremely valuable content to my pursuit of a career in data science. This, paired with the Machine Learning, are giving me great insights into predictive analytics.

par Ioannis B

1 août 2017

Exceptional course for the subject of Regression. You can really understand the foundations and build on it with R. Congratulations to the instructors and the team.

par Samy S

25 févr. 2016

Good introduction to the usefulness and traps of linear models. By the way, having the teacher filmed for the lectures does provide a more engaging experience.