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Opiniones y comentarios de aprendices correspondientes a Regression Modeling in Practice por parte de Universidad Wesleyana

239 calificaciones
48 revisiones

Acerca del Curso

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....

Principales revisiones


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


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

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26 - 44 de 44 revisiones para Regression Modeling in Practice

por Meigui Y

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.

por priya x

Mar 03, 2016

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

por Abdullah A M A

Aug 16, 2016

great effort was paid preparing this course .

por Franzi S

Feb 06, 2016

I truly enjoyed this course and I learned a lot. I particularly liked the structure and setup. My suggestions for improvement would be to provide more background information and further reading on the subjects covered and to maybe have a handout that summarizes the most important concepts and key messages of each week.

por Γεώργιος Κ

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.

por Alex S

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.

por Ali R

Jul 30, 2016

simple and useful

por Enyang W

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..

por Aneeshaa S C

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.

por Sandra M

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.

por Aurimas D

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.

por Craig S

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.

por Bakr G

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).

por Jason M

May 21, 2016

Similar to other courses in this specialization, the material is very nice (although slightly easy and straightforward), but the course instructors do not moderate the discussions enough to make them a useful tool. Especially when I'm paying for the specialization, I would appreciate responses to my questions.

por Victoria

Jun 26, 2016

Extremely boring and not

por Sabah R

Mar 02, 2016

The course contents are unfortunately very poorly designed, and this has been an ongoing trend in this overall specialization.

The weekly lecture videos are at times quite unclear and leave more questions unanswered than they answer. I (and other learners) have found glaring gaps in the course content again and again. The examples that the lecture videos use do not nearly cover the variety of datasets being used by the learners in the course; and the least that could have been done is to at least inform learners of what to expect if they were dealing with a different research question and using an alternate dataset.

I know the defense may be that the videos are meant to be introductory / gateway to a deeper understanding of that particular topic. The course moderators / instructors did a terrible job in keeping up with answering key questions that were posted in the discussion board. Yet most of the times when I was still searching for answers, looking up similar introductory videos in Youtube were far more helpful (and I didn't have to pay to watch them).

The assignments are poorly worded because the instructions are not always very clear, which in my opinion set up a lot of learners including myself for failure. The lectures are often broader than what should be considered when finishing the weekly assignment. It wasn't even clear at times of what was exactly being expected in the assignment. The rubric that I had to use to mark my peers' work meant their analysis had gaps because the criteria for assessment is not transparent AT ALL during the submission of an assignment (they are starkly different in terms of details).

All to say that I have been quite frustrated despite my best efforts to learn from these materials and much MUCH more can be done to improve the actual content of the lectures.

Please consider adding alternate resources AS WELL where learners can go seek the right answers if the answer is not provided by the lecture materials. This can help to cut back on countless precious hours for those who are juggling a full-time job and retraining / career change like I am, not to mention the countless other scenarios of other learners out there.

I am neither inattentive nor lazy, and learning this content is very important to me, if it wasn't clear through my comments here already.

One last comment, for those of you who are all very sceptical let me tell you that I know that I don't have to be a statistician to grade the course contents, and I really hope the intructors are paying attention to these and similar comments from those enrolled in this specialization and the others who have completed the course.

por Miyuan

Feb 11, 2016

too easy

por priyasmita g

Apr 06, 2016

The course was very shallow as if teaching english literature

por Amin F

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.