Вернуться к Регрессионные модели

4.4

Оценки: 2,507

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Рецензии: 432

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....

автор: 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.

автор: 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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Рецензии: 412

автор: Rodrigo Olivares

•Apr 16, 2019

great

автор: 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.

автор: 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).

автор: Dora McAlpin

•Mar 30, 2019

Good class.

автор: Yadder Aceituno González

•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.

автор: Manny Ruiz

•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.

автор: Seyedeh Maryam Mousavi

•Mar 14, 2019

Awesome class! Highly recommended.

автор: Paul Ringsted

•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.

автор: Nathalie Descusse-Brown

•Mar 11, 2019

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

автор: Claudio Francisco Sobral

•Mar 11, 2019

This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!

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