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ジョンズ・ホプキンズ大学(Johns Hopkins University) による Regression Models の受講者のレビューおよびフィードバック

4.4
2,796件の評価
470件のレビュー

コースについて

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....

人気のレビュー

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.

BA

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.

フィルター:

Regression Models: 401 - 425 / 450 レビュー

by Hani M

Oct 24, 2017

was tough

by David S

Nov 05, 2018

needed to consult external resources extensively

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 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 Lei M

Aug 23, 2017

Some of the materials are too much math for me.

by VenusW

Jan 10, 2017

This course is great, instructor is good, however, the material of this course is not well organized, even the swirl practice is not put in the correct week, not in the same pace as the lectures. The quiz and project are far much easier than lecture content.

by Erwin V

Dec 20, 2016

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

by benjamin s

Jun 20, 2018

A good (although slightly frustrating) course, attempted once but had to come back after studying the material in class, quite a heavy course if you've not been taught regression before

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 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 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 Joseph D

Apr 29, 2016

Coursera keeps changing my rating. Not cool.

by Paul K

Mar 28, 2017

Slightly better than the Statistical Inference course, but many of the same technical and delivery defects persist. With an otherwise high quality program, I recommend re-producing the inference and regression lectures to increase the overall value of the curriculum.

by Coral P

Jul 20, 2017

I would like to propose that instead of putting the optional reading materials at the back, it should be put up front and mandatory. Else we can't follow the videos

by Benjamin S

Jan 12, 2018

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.

by Simon

Sep 01, 2017

The concepts behind this course are really important. However, I feel that the material is not up to the needed level.

I am missing a good solid material that explains properly the theory behind these methods. I had to revert to other books (that could have well showed up as references in the course material) to get a proper understanding.

by Sepehr S

Mar 11, 2016

The instructor is not good and doesn't explain things clearly.

by Jing Z

Feb 08, 2016

I just realized that you have to upgrade(pay $49) in order to submit the quiz and receive the feedback. That's depressing since my purpose is to watch the video and check out what I learned so far without getting any certificate. The policy here bring huge inconvenience for people like me.

by Lee D

Sep 30, 2016

I again found many of the lectures to be difficult to follow along, there seems to be lots of different styles of videos in the way that the person was superimposed on the slides. In fact it was often impossible to read the text in the slide due to the size of the presenters head which obscured the text. Honestly this data science course is getting worse as the months progress, you really should think of updating the content of the course if you want to continue to charge money for it. 2 stars as I did actually learn something despite the quality of the material and its delivery.

by Albert B

Jan 09, 2017

To fast pace and missing lot of content to make this lesson enjoyable!!!

by Jorge P

Jun 07, 2016

Should cover a lot of dfificuties when the model assumptions are violated and should be for a longer time or having a second course about this theme.

by João R

Aug 20, 2017

Needs more practical examples. Could be rerecorded. I love mathematical theory but past week 2 it is really too theoretical, in my opinion.

by Daniel M G

Jan 21, 2016

Un curso difícil de entender si no tienes la base matemática de regresión. Uno no sabe por dónde empezar, cualquiera de los cursos de esta serie (Statistical Inference, R programming...) pareciera que te saturan de información. Es bueno para curiosos con bases en R y que quieren saber más de Regresión

by Barry S

Mar 15, 2016

This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material. Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

A more structured final project would have been helpful. The instructions on this project weren't quite so blunt as to say "Take this data set, do some regression-y stuff and come back with something about these two variables," but that's basically as far as our instructions went. It could have been a great learning experience to have a more detailed guide through the construction of a regression analysis, but instead an assignment which was 40% of our grade was put together as an afterthought. It was the assignment equivalent of stopping in the 7-11 a block away from a birthday party to buy a card.

Also, in terms of delivering the content: Mr. Caffo needs to structure his slide/video arrangements so that he is not standing in front of the text. Think of it from the point of view of somebody wanting to listen and read at the same time.

by Brian

Feb 12, 2016

way to much emphasis on non-data science. This one course covers more information that the rest of the courses combined..