Regression Models に戻る

4.4

2,791件の評価

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470件のレビュー

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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by Pooia L

•Sep 13, 2018

This is a very nice course provided you study a lot for it

by Chonlatit P

•Aug 19, 2018

Love this course. teach me to understand Linear Regression more, especially swirl class is great.

by Nevon L D

•Sep 27, 2018

Builds Heavil

by Daniiar B

•Sep 27, 2018

Very hard to understand

by Sameen S

•Oct 02, 2018

The lectures were a bit complex and lengthy.

by Anup K M

•Oct 22, 2018

good content

by Diego C

•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.

by John D M

•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.

by Dora M

•Mar 30, 2019

Good class.

by 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.

by Sandesh

•Jun 25, 2019

For the content covered, I think the course does a good job exposing students to fundamental concepts while also highlighting how much more there is to research in order to gain a solid understanding of this subject matter. The course offers a good foundation, and I hope they come out with a more advanced version of this course for more guided exposure.

by Manuel E

•Jul 03, 2019

Hard class, documentation could be better, but good content.

by Siying R

•Aug 10, 2019

The lecture is pretty dry to me who had limited vocabulary in the field. It made me went out to find other easier lectures to help me understand. The lecture focus on explaining the basic concept of Regression Models and spend a big chunk of time to explain how the function works. I would prefer to have more time explaining what the numbers mean for the data. The questions in the quiz require us to understand the meaning of the data, so we know what function and number to apply. Maybe it is just me, finding it very challenging to see the connection between the lecture and the quiz.

by Yiyang Z

•Aug 25, 2019

Very informative, but could be more interesting and concise.

by Koen V

•Sep 23, 2019

The explanation of the right answers from the quiz were quite handy!

by Luiz E B J

•Nov 26, 2019

The content is to long, maybe would be interesting split the content in other modules.

by Manpreet S

•Oct 23, 2019

Good Course for beggining

by Raul M

•Jan 16, 2019

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

by Sarah R

•Mar 20, 2016

The instructor is at time incomprehensible. It would be helpful to speak more slowly and pause more often. Otherwise he sounds like repeating something that he's so well memorized after many years of teaching.

by Marco A M A

•May 09, 2016

This course is better than Statistical Inference, and I think it is as useful. Non credit excersise are still very good at helping with understanding in practice what is going on.

by Guilherme B D J

•Aug 21, 2016

Given the importance of this subject, this course should have been split in two or more or have a longer duration to properly address subjects as GLM or model selection techniques.

by Gareth S

•Jul 16, 2017

Expects a level of statistical knowledge already.

by Codrin K

•Mar 28, 2018

To me, the approach was too much from the theory of statistics and its mathematical foundations; I would have appreciated a more applied approach for this course in the specialization. So starting from examples, questions anout data and then working towards theory instead of the other way around.

by Asif M A

•Oct 23, 2016

I enjoyed the earlier courses more. I did not like the way the materials were provided. There were a lot of very complex ideas were presented, in a very concise and brief manner. Also, there should be more exercises to practice. May be its me, but, I guess, I might need more time to fully comprehend the materials.

by xuwei l

•Sep 22, 2016

the lecture notes is a bit confusing