Voltar para Modelos Regressivos

4.4

2,740 classificações

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464 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 Chris W

•Nov 27, 2016

Great course.

por Andrew K

•Mar 13, 2017

Good foundation in the Data Science Certification for Practical Machine Learning. There are 3 areas that I would like to dig deeper so far: Statistical Inference, Regression Models and Practical Machine Learning (perhaps + Deep Learning).

por Albert C G

•Sep 02, 2016

Great - learned a bunch

por BAUYRJAN J

•Feb 01, 2017

Excellent course, but you have to use other materials from different courses as well.

por Farhan M

•Feb 26, 2017

Amazing course, I learnt a lot.

por Sai S S

•Jul 09, 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 ?

por Berthold J

•Jun 24, 2017

Very good lecture and also decent level of difficulties that requires to think/read additional stuff.

por Pragya k

•May 22, 2017

Exceptional Learning!

por Andrew V

•May 15, 2017

Nicely presented and understandable course with a challenging an interesting project.

por Fernando M

•Sep 04, 2017

Love it

por Lawar S V

•Apr 19, 2017

Very Good Course !

por Muzaffar H

•Oct 14, 2017

A very good data analysis course, highly useful for quantitative method and empirical findings.

por Joonas S

•May 29, 2017

Really good introduction to regression models.

por Artem V

•May 16, 2017

Complicated but good information!

por Paul F G

•Jun 13, 2018

Excellent, highly focused course with current R libraries for learning various regression methods and methodology. I highly recommend this course.

por Handong D B

•Aug 15, 2016

very concise and well taught

por Amelia L

•Nov 29, 2016

Learned a lot

por Yubo Y

•Apr 03, 2016

concise, practical and straight forward

por Mikhail M

•Sep 11, 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.

por Damien C

•Dec 06, 2016

Great ressources. Usefull presentations, maybe too rich for a newbie.

It was too fast for me. Could be done in 2x more time :/

por Shubham S Y

•Jun 26, 2017

Good for clearing out your basic Regression doubts and that too in R!

por Light0617

•Dec 28, 2016

great courses!!! very practical but no a lot of mathemetics

por Do H L

•Jun 17, 2016

This course gives a very thorough and rigorous treatment to the topic of regression models.

It teaches you how to derive from the ground, how regression models are made and how to interpret every information available through regression models.

Although the lectures are very lengthy and dry, the course offers a very rich well of information that is not readily available else where.

Thanks to Brian Caffo for the wealth of information about regression models taught through this course!

por Priyanka V I

•Aug 26, 2017

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por Pratikchha N

•May 31, 2016

great

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