Chevron Left
Вернуться к Регрессионные модели

Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

4.4
звезд
Оценки: 3,203
Рецензии: 540

О курсе

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....

Лучшие рецензии

KA
16 дек. 2017 г.

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
31 янв. 2017 г.

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.

Фильтр по:

326–350 из 520 отзывов о курсе Регрессионные модели

автор: Jamison C

28 авг. 2018 г.

Excellent course, though I recommend you supplement applied practice by using the principal instructor, Dr. Brian Caffo's book, to answer practice questions if you want to retain these content-packed lessons. Better yet, begin each week by looking at the quiz and printing it out. As you view the relevant content, answer the related questions (which are generally presented in order of delivery).

автор: Richard M A

23 дек. 2016 г.

This was better than the statistical inference course, but Brian still puts too much emphasis on the precision of his language (as if he's teaching to other mathematicians) which makes it difficult to understand. I would like to see a bit more dumbed down explanation of the mathematics in the examples (similar to Sal at Khan Academy). If that happened, this would definitely be a 5 star course.

автор: Sandesh

25 июня 2019 г.

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.

автор: Christopher B

28 февр. 2017 г.

This course was an improvement in teaching modality from the statistical inference course, with more polished content, but the link between the lectures and the actual exercises was still a bit strained. Overall, it felt like there was a bit of a disconnect between the swirl exercises and the lectures, and this led to a lot of self-teaching.

автор: Paul R

13 мар. 2019 г.

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: Miguel C

3 мая 2020 г.

I really enjoyed the course. Even though I had already learned linear regression and logistic regression from a computer science perspective, I still learned a lot since the course approaches these subjects from a statistical view. The content was interesting and challenging, so I am really glad that I took this course.

автор: Max M

19 нояб. 2017 г.

Really appreciate the depth of this course, as well as the changes Prof. Caffo made in his teaching style since his Statistical Inference course. However, the reasoning behind some of the more complex topics, like GLMs, aren't adequately explained, and the Swirl lessons are presented in a strange and disorienting order.

автор: Cesar L

1 нояб. 2016 г.

Great course. The content might be improved to be more clear. I feel that sometimes the instructor assumes we are familiar with some concepts we have not seen in previous courses. Also, some times he does a very good job explaining the WHY before the HOW, and some other times he does not. Very knowledgeable instructor.

автор: Kevin H

9 нояб. 2016 г.

Something was missing from this course. I cam away with an increased understanding of regression but I still feel like I struggle with many concepts and had to put in much more time than the recommended.

Still when I found the answer it was all still contained and maybe the material itself is just advanced.

my 2c

автор: Jeremy J

12 нояб. 2016 г.

Like the way the Prof uses media. This is a very light touch on a very deep subject so it has to balance analytical work with the light "trust me and just do it" approach. The balance was mostly there although on a couple items I don't know that I had a good enough grip to know what I don't know.

автор: Wei W

23 окт. 2017 г.

Brian did better job in this course to elaborate and demonstrate with examples. No doubt Brian is extremely knowledge about this subject. Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding. That's why I take one star away.

автор: Vidya M S

14 мар. 2017 г.

The concepts are well explained and precise. I think it depends on the individual to dive deeper into the topic by independent learning. Good data examples. Also following the suggested book of the author helps with some extra excercises. However , I feel extra practice questions would help .

автор: Linda W

3 июня 2016 г.

This course will give you a good basic foundation in regression models. However, do be prepared to do a good amount of work besides just viewing the videos. I would recommend at the very least to go through the exercises in the 'recommended textbook' to gain a better understanding.

автор: Kim K

8 авг. 2018 г.

You will need to know the subject before taking this class in order to understand or be able to put in a large amount of time to learn. The book "Introduction to Statistical Learning" is an excellent supplement to the course. Rigorous and rewarding when you put the work in.

автор: Ada

14 нояб. 2016 г.

Regression models was almost just as difficult as statistical inference. Again, the swirls and exercises were of great help. The pace, as always, was quite fast, but in the end all the pieces fitted together. Congratulations on a job well done!

автор: Peter G

10 февр. 2016 г.

First 3 weeks give very reasonable overview of the subject - topics of linear / polynomial / multivariate regression are covered quite well.

Week 4 is a bit sloppy and ad-hoc, comparing to first 3 weeks - GLMs are given poorly.

автор: Utkarsh Y

28 сент. 2016 г.

It is a good course for learning regression model implementation in R. You may need to have a basic understanding of popular regression models like linear & logistic as the course doesn't cover mathematical aspects in detail

автор: Tim M

5 окт. 2020 г.

The course is informative & well taught. I would have liked to spend more time on GLM models, such as logistic regression. The Swirl assignments seem a bit outdated method of learning code and a bit of a hassle.

автор: Jim M

23 июля 2020 г.

Content is excellent and in depth. Structure could be better to present materials in a more organized fashion, particularly on how all the concepts and tools relate, and complex results interpretation.

автор: Andrew W

20 февр. 2018 г.

Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.

автор: Diego C

4 мая 2019 г.

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.

автор: Andrew W

5 апр. 2018 г.

Very good at presenting basic concepts. I highly reccomend saving the quiz questions as a good guide as to what you should know. I wish there were more material on generalized linear models.

автор: Arturo M K

10 дек. 2016 г.

I was hoping to learn about PROBIT models. I know they are very similar to LOGIT ones, but still... the pace is a little bit too fast and I think it requires more time than what it says.

автор: Bill K

10 февр. 2016 г.

This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.

автор: Manny R

22 мар. 2019 г.

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.