Regression Models に戻る

4.4

2,790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 Brandon K

•Mar 30, 2016

I found the videos tough to watch. I was hoping for something that would be more practical for non-statisticians, but the lectures mainly devolved into mathematical proofs. That said, I did learn some from this class. Just not as much as I'd hoped.

by Erwin V

•Dec 20, 2016

Very interesting course, yet course content could be spread more evenly (week 4 is really a lot)

by Lei M

•Aug 23, 2017

Some of the materials are too much math for me.

by Suleman W

•Nov 10, 2017

I did find it difficult to follow and understand some of the materials.

by Jinwook C

•Feb 14, 2016

The flows of courses instructed by Caffo(Statistical Inference and Regression Models) are too long to concentrate it and the quiz is not quite related in lecture.

However, Contents of the book is really good, as well as homework in the book.

by Raphael R

•Oct 31, 2016

I am no used to this educational system so I find difficult to follow without any proof or demonstration of the mathematical tools. I find proofs necessary for a good understanding of concepts. Another benefit of proof will be to have a more rigorous framework for variable names in the explanations. Even though this is more a practical course, it will benefit from being a bit more rigorous ; so at least people can make proofs on they own.

Other than that, it is a great course. Very practical and to the point.

by Hani M

•Oct 24, 2017

was tough

by Mertz

•Mar 20, 2018

Bad audio and video quality. Too fast on some complex ideas and too slow when come repetitions between videos...

by Angela W

•Nov 27, 2017

I learned a lot, but it was so much content for 4 weeks!

by Mark S

•Apr 24, 2018

Lots of math, but it would be more productive to focus more on the output of R and better understand the results

by Anamaria A

•Mar 12, 2017

Lots of material needs additional study (from different sources) as it's only summarily explained. Much math without the link to the praxis :-(

by Boban D

•May 07, 2018

Much better than the inference course given by Mr. Caffo. This time at last I could follow the materials being covered. He is plotitng more often and scribbling on the slides which helps understanding the materials being covered by establishing a connection between the isolated issues in regression analysis.

by Ahmad A

•Nov 09, 2016

Requires much more than a month to digest the material and complete the assignments. A default/initial one-week offering is too tight unless you are only taking the course (not working). I know one can complete the course in more than a single round and I did that but I still don't think the expectations should be set for a single month.

Instruction (video content) can be much better, at least compared to a lot of other courses on Coursera.

by Gareth S

•Jul 16, 2017

Expects a level of statistical knowledge already.

by ANDREW L

•Jan 27, 2016

Better than Stat Inference, and gave some reasonable intuition, but could be improved I think by focussing on more understanding and less maths and formulas. Some of it did seem to be - here' s a formula, plug the numbers in to get the quiz question right, whereas in reality (in the world of work) that question is completely unrealistic - you have raw data and you need to do the regression and understand what it means.

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 Pepijn d G

•May 23, 2016

The course is good. Unlike the previous courses I took in this track, there was almost no interaction in the forums and also no-one to give feedback. I wonder if there were any TA's present in this run.

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

by Erick J G L

•Feb 01, 2018

Lots of room for improvement on this course, the teacher really seems like he cares but he is a really bad teacher nonetheless. The course material is incomplete and not properly structured. Basically read the book if you want to learn something, otherwise the videos don't really help.

Also, the course project is not worth it because you get no real feedback to compare your project to the ideal or at least expected answer. I would not recommend this course.

by Feng H

•May 17, 2017

Not impressed. Dr. Caffo tried to use non-calculus, non-linear-algrebra ways to explain complex concepts and derivations. IMO, he should not have done that. It only made things more confusing. Also the final project is so unsatisfactory in that we were to analyze the data with 32 obs but 11 variables! How robust could it be? Was expecting something much more challenging than that.

by Andres C S

•Mar 02, 2016

I think this course needs more emphasis on practical applications and less mathematical background.

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 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.