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Learner Reviews & Feedback for Regression Models by Johns Hopkins University

4.4
stars
3,274 ratings
563 reviews

About the Course

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....

Top reviews

KA
Dec 16, 2017

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
Jan 31, 2017

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 - 450 of 544 Reviews for Regression Models

By David E L B

May 18, 2017

Really helpful and well presented.

By Teppakorn

Jun 22, 2016

Advance topic in regression model.

By Serg C

Oct 31, 2017

Not an easy one, definitely !

=)

By Norman B

Feb 7, 2016

A decent overview of regression

By Nicolás H

Nov 9, 2020

Muy buenas herramientas!!

By Manpreet S

Oct 23, 2019

Good Course for beggining

By Dan B

Sep 27, 2018

Very hard to understand

By Prabesh S

May 6, 2016

Very intuitive course

By Yogesh A

Oct 13, 2017

Good course content

By Vincent G

Oct 9, 2017

fantastic course

By Nevon L D

Sep 27, 2018

Builds Heavil

By Mariano F

Jun 12, 2016

Great course.

By Anup K M

Oct 22, 2018

good content

By Mohammad M

Apr 12, 2021

informative

By Dora M

Mar 30, 2019

Good class.

By Khairul I K

Mar 23, 2017

2 thumbs up

By Manojkumar P

Nov 8, 2016

Nice Course

By Rohit K S

Sep 21, 2020

Nice one!!

By Johnnery A

Feb 12, 2020

Excellent!

By Mohamed A

Jan 3, 2018

Great Deal

By Timothy V B

May 19, 2017

good intro

By Yuekai L

Mar 7, 2016

Nice.

By Normand D

Feb 1, 2016

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...

By Will J

Sep 22, 2019

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.

By Janardhan K

Nov 16, 2017

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.