# 學生對 约翰霍普金斯大学 提供的 回归模型 的評價和反饋

4.4
3,262 個評分
561 條評論

## 課程概述

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....

## 熱門審閱

KA
2017年12月16日

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
2017年1月31日

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.

## 476 - 回归模型 的 500 個評論（共 542 個）

2018年11月2日

The material was a little disjointed and not always explained with examples. Passing this course required a significant amount of outside study and research.

2016年3月29日

This is a decent class, covering linear regression and a few of its variants in good detail. It's a challenging subject, but presented acceptably here.

2017年3月12日

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

2020年2月10日

The content was exposed in a very confused manner. I did not like how the teacher explained. It seemed more difficult than it really is

2018年9月26日

Starting from the first week swirl practice, course content is poorly organized making even simple concept difficult to understand.

2016年1月17日

I find it very tough to understand everything. Buying the course book helps to overcome this. You have to dedicate a lot of time.

2018年4月24日

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

2018年3月20日

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

2016年3月1日

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

2016年12月20日

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

2019年9月17日

Course has more theoretical concept than application.. It has to be more application based

2020年4月22日

I think a revamping of the concepts in a more ellabroate way is required in the course

2017年11月9日

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

2017年2月28日

Many things are not clear enough in multivariable regression part.

2016年2月2日

good quick overview, could have more actual R examples in lectures

2020年3月22日

Topics like logistic regression were not explained clearly

2017年11月27日

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

2017年7月16日

Expects a level of statistical knowledge already.

2018年11月4日

needed to consult external resources extensively

2017年8月23日

Some of the materials are too much math for me.

2016年9月22日

the lecture notes is a bit confusing

2020年1月6日

Terrible professor, good book

2017年10月24日

was tough

2016年3月15日

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.

2020年3月19日

The timing on this course is very inaccurate - it should take much longer than 4 weeks, 6 weeks at the absolute minimum. I say this because Week 4 has so much information crammed in of all different types of General Linear Models (i.e. models that are not necessarily a straight line). Binomials, Poisson, splines - each of these topics could have their own weeks, but instead they are quickly summarized for one week with the student expect to understand them for the quiz. The other issue, which has been a problem with all courses in this specialization, is the discussion boards. They are totally abandoned by mods; good luck finding any post that isn't "grade my project? I'll grade yours!" despite a mod post that says such requests will be deleted. The board is totally flood with those requests, and makes me wonder how many people are passing these classes wrongly because "if u give me 100 i will grade yours too!" It totally devalues the program. The creators seemingly abandoning Coursera have made this certificate a waste.