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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
3,267 個評分
562 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....

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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.

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426 - 回归模型 的 450 個評論(共 543 個)

創建者 Teppakorn

2016年6月22日

Advance topic in regression model.

創建者 Serg C

2017年10月31日

Not an easy one, definitely !

=)

創建者 Norman B

2016年2月7日

A decent overview of regression

創建者 Nicolás H

2020年11月9日

Muy buenas herramientas!!

創建者 Manpreet S

2019年10月23日

Good Course for beggining

創建者 Dan B

2018年9月27日

Very hard to understand

創建者 Prabesh S

2016年5月6日

Very intuitive course

創建者 Yogesh A

2017年10月13日

Good course content

創建者 Vincent G

2017年10月9日

fantastic course

創建者 Nevon L D

2018年9月27日

Builds Heavil

創建者 Mariano F

2016年6月12日

Great course.

創建者 Anup K M

2018年10月22日

good content

創建者 Mohammad M

2021年4月12日

informative

創建者 Dora M

2019年3月30日

Good class.

創建者 Khairul I K

2017年3月23日

2 thumbs up

創建者 Manojkumar P

2016年11月8日

Nice Course

創建者 Rohit K S

2020年9月21日

Nice one!!

創建者 Johnnery A

2020年2月12日

Excellent!

創建者 Mohamed A

2018年1月3日

Great Deal

創建者 Timothy V B

2017年5月19日

good intro

創建者 Yuekai L

2016年3月7日

Nice.

創建者 Normand D

2016年2月1日

As for the Statistical Inference course, this course is amazing but is presented in a more complex way than it should be. Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.

Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing. Too often the details are thrown at us without being properly framed in the context or without having the proper concept being introduced progressively.

I have a theory about teaching since I was 15, and so far it has proven to be true. Imagine that learning is about climbing a mountain in which tall steps have been carved. Each step is taller than the student. The teacher is somewhere higher than the students (not necessarily at the top, if there is such a thing).

The job of the teacher is to throw boxes (concepts) and balls (details) of different size, shape and colors. The job of the student is to catch these boxes and balls and to put the right balls in the right boxes in order to make a staircase out of it to climb (at least) one of the giant stair up.

A good teacher makes sure to throw the concepts first than the details and to clearly specify which balls go into which box, as well as which boxes go inside/over which other boxes.

But most teacher simply throw the balls and boxes in an not so well structured manner, so the poor students try to catch as many as he can, but also miss a lot of them. His hands can hold a limited amount of balls. If he doesn't have the right box to put them, he would either miss the next balls, or put the one he hold in his hand in the wrong box.

Bottom line, the best teachers are those who focus on the concepts (and context) and make sure that the concepts are well understood before introducing details to stuck in these concepts. From my experience our brain (or at least mine) better learn this way. It is as if our brain need first to establish a category-pattern (the concept/context) to which it will associate detail-patterns. But without a proper category-pattern, our brain is having a hard time to properly remember the detail-patterns or miss-associate them to the wrong category-pattern (which create even more confusion).

Hope it was helpful somehow...

創建者 Will J

2019年9月22日

Pros: The instructors of this course are absolutely knowledgable on the content here. The content itself is challenging and applicable to real-world data science challenges. Using R makes this a good course for today's (2019) current programming world as many professional statisticians will use this language day-to-day.

Cons: The content feels mismanaged. Sometimes the Lectures don't prep you for the practice assignments, and sometimes neither of those prep you for the quizzes particularly well. I had also hoped for some more engaging video content from a course this expensive. Having a professor in his office hastily work through material while there are police sirens outside isn't exactly pro-level instruction (It is in Baltimore, so I get it).

Overall, it's worth it if you've got the time to power through relatively dull lectures. The R based practice assignments are wonderful and the final project incorporates things together nicely.

創建者 Janardhan K

2017年11月16日

The course was of average quality. It could have been better. Brian's slides in the video don't correspond 1-1 with the slides made available. The coverage and explanation of the material could have been better. The instructor's presentation could be more engaging (fewer 'ums' while talking). It was not immediately clear how to answer some questions on the Week 4 quiz, and also the course project, even after reviewing the material multiple times. One example: Brian says that the ANOVA test can only be used to compare models, when the model being compared has normally distributed residuals (using the Shapiro test). No advice is given about what to do if they are not normally distributed, which is what happened in the project.

創建者 Raphael R

2016年10月31日

I am no used to this educational system so I find difficult to follow without any proof or demonstration of the mathematical tools. I find proofs necessary for a good understanding of concepts. Another benefit of proof will be to have a more rigorous framework for variable names in the explanations. Even though this is more a practical course, it will benefit from being a bit more rigorous ; so at least people can make proofs on they own.

Other than that, it is a great course. Very practical and to the point.