# 學生對 卫斯连大学 提供的 回归建模实践 的評價和反饋

4.4
242 個評分
48 條評論

## 課程概述

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you....

## 熱門審閱

##### VM

Mar 07, 2017

Awesome course. More than regression generation, they have explained in details about how to interpret regression coefficients and results and how to make conclusions. 5 Stars

##### PC

Nov 28, 2016

This was a great course. I've done a few in the area of stats, regression and machine learning now and the Wesleyan ones are the most well-rounded of all of them

## 26 - 回归建模实践 的 44 個評論（共 44 個）

Jun 22, 2018

Many useful and must to know things. I am not satisfied by the explanations to difficult tasks that need further understanding and deepness. Anyway I am glad to have taken that course, it offered knowledge to me. In fact I recommend it to those that have heard of regression and need supportive material.

Jan 04, 2016

This was another strong course from Wesleyan, and well produced. However some of the weekly problems were vague and it was not clear exactly what was being sought or how they would be evaluated, as they have been very clear in the previous mini courses in this specialization.

Dec 05, 2016

This is a great beginner level course for those have no programming experience. But I would suggest the content to be extended to 8 weeks instead of 4 weeks.

Sep 26, 2016

Assignmetns allow student to choose dataset and analysis - but are not very well structured.

Detailed exaplanations.

Mar 03, 2016

Good for understanding concepts and running code in SAS but still needs more depth to the coursework.

Jan 21, 2019

Good course with a lot of applicable techniques. Thanks for your time and teachings!

Aug 16, 2016

great effort was paid preparing this course .

May 30, 2016

Good Course. To the point and clean lectures.

Jul 30, 2016

simple and useful

Mar 28, 2016

There is a lot of self-teaching with these courses because there are no professors present to reach out to with questions. In addition, the course staff do not always respond promptly nor are they fully knowledgeable about all aspects of error messages that may arise out of coding. At times the code that were provided in the lecture videos were out of date and a lot of time was spent on googling to find the updated code. This is definitely not a beginner coder course and I do not recommend it to anyone who has not coded before.

Jul 31, 2017

course could've had more depth. expected explanation on more data scenarios. for example, logistic regression when the explanatory variables are quantitative.

even interpretation of output. course is too brief. barely gives you an introduction to the subject.

Mar 19, 2019

The course itself was nice, but the review for the assignments was really annoying, I always had to wait sooo many days..

Jan 08, 2016

As with all Coursera courses, there is no way to communicate with the tutors, which is vey important in such a specialised course. The course also requires finishing assignments while needed clarification and explanation is not always provided in the course videos. in some assignment a required step was only explained in the following week (Centering variables).

Assignments review depend only on peer review, when peers in most of the time don't read through assignment completely or open attachments leading to poor grading based on 'missed information' that are actually attached in submissions.

Also this course along with the rest of the courses in the Data Analysis and Interpretation need some more focus on using SAS and other data analysis tools and the way to present results in a more appealing way. (this could be given one extra lecture in each course).

Mar 15, 2016

I learned a few things. The videos were good. The feedback from the student reviews was erratic. There was some good, thoughtful feedback and some was nonsense. I took the free option. I don't think I'd ever pay for a course where evaluations are conducted by people who understand the material no better than I do.

This is the second course in this series that I completed. I also finished the machine learning course. I think I'm done with this specialization.

Jan 07, 2019

unbalance course. in my opinion simple topics were over-explained and difficult topics were under-explained. I personally would prefer to know more about regression in the first place and only then try to adapt them to the data. perhaps it my lack of knowledge.

Aug 08, 2017

This specialization was great up to this course! All the content are reviewed superficially and it seems the instructors are just trying to teach recipes and there is no intuitive explanations, especially on multiple regression and the tests for evaluating it.

Apr 06, 2016

The course was very shallow as if teaching english literature

Jun 26, 2016

Extremely boring and not

Feb 11, 2016

too easy