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437 Bewertungen

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....

von MM

•Mar 13, 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.

von KA

•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.

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419 Bewertungen

von YANAN DONG

•May 24, 2019

Really Helpful

von Nino Požar

•May 24, 2019

Similarly to statistical inference, this is a bit harder course in the specialization. Still passable and recommendable.

von Andrew

•May 16, 2019

Great introduction to regression models. A ton packed into the class. Be ready to be challenged, but you'll learn a lot.

von Thej Kiran Ravichandran

•May 13, 2019

Worst teaching by Brian Caffo! typos in quizes after 4 years even. And brian has put very littel effort into making it digestable for students. Look at his lectures on youtube and I have commented at each lecture! So bad. A simple googling outside of his notes was so much more better for understanding regression!

von Ekaterina Soboleva

•May 12, 2019

It was a very usefull course. It is a very good approach to the theme - the main essence without much math difficulty.

von Diego Costa

•May 04, 2019

Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.

von MEKIE YOUDOU RALPH KEVIN

•May 02, 2019

Really interesting and full of advices.

But would like to dig more into the Logistic and poisson regression residuals explanations :)

von Rodrigo Olivares

•Apr 16, 2019

great

von Don Moffatt

•Apr 10, 2019

Overall an excellent course, but there were some issues with the wrong function being specified in one quiz (Q3q6) and the wrong answer in another. Apparently it has been that way for years, according to the forum. The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that. However, the estimated time for completion of each week is ridiculously short compared to reality. Five hours? For me it was more like 20 hours, and more if I did all the Swirl exercises. Such low-balling on the time estimates is typical of the Data Science stream. The final project is given as 2 hours but it was closer to 15 for me. i wish Coursera would go back to the stream model where you could bump yourself to the next intake. That is much less stressful for busy working people like me.

von Satish Venkataraman

•Apr 08, 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).

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