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Learner Reviews & Feedback for Regression Models by Johns Hopkins University

4.4
stars
3,340 ratings

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.

DA

Mar 10, 2019

This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!

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151 - 175 of 556 Reviews for Regression Models

By Benjamin S

Jan 12, 2018

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.

By Veronica G

Jan 1, 2021

I seldom write critical comments for Coursera courses because the many courses I've taken have been quite well-designed so far. This one I feel obliged to write something, which may or may not make a difference judging by how much care was given to designing this course in the first place. From the resource allocation perspective, this course does more harm than good because the minimal amount of knowledge you gain from this course is not worth the amount of time you spent trying to figure out how the lecturer perceives and conveys statistical concepts in such a confusing way.

Bottomline: if you don't need the Data Science specialization certificate from JHU, you are WAY BETTER OFF by taking the Basic Statistics + Inferential Statistics courses provided by University of Amsterdam. I completed those two courses myself. The lecturers there truly made an effort to make the materials as engaging and intuitive as possible. You will not waste your time by taking those courses instead.

If you thought the Statistical Inference course was bad enough, try taking the Regression Models course. It refreshes your understanding about how bad a course can be. Below are some major problems:

1. The delivery of the materials is very dry. I can't tell if meaningful effort was put into creating engaging examples so that students can better understand the material. The mathematical and theoretical parts were poorly explained with inconsistent notations and insufficient elaborations about the concepts. The lecturer often jumps from very basic concepts to very advanced/complex concepts without enough transition/explanation. I had to constantly consult a friend who's very good at statistics to bridge the gaps.

2. The lecture notes are way too chaotic. Many times the PDFs provided do not match what was shown in the videos at all. Several pages in the PDFs were not covered by the video lectures, and vice versa.

3. Stepwise regression was not even covered in this course. Many students ended up using stepwise regression for the course project. Maybe students are just jumping ahead before applying the more fundamental techniques covered in this course, or maybe stepwise regression should have been covered??

4. I wish there were a lecture at the end that walks through one case study and applies most of the core techniques covered in this course. In Roger's Exploratory Data Analysis course he did one at the end and applied many things he taught in the fragmented lectures in an integrated manner. That was super helpful.

Some minor good things about this course that I did not gain from the UvA courses:

- The hodgepodge lecture provides some very interesting materials.

- The simulation examples about covariate adjustment are quite intuitive and facilitate understanding.

By sanjeev i

Feb 29, 2016

The course content was very brief and well structured, Regression being a rather vast topic demands a lot more time. 4 weeks seemed a bit less! Overall satisfied by what the course offered.

By Daniel C J

Aug 2, 2017

Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!

By Aisha H

Feb 2, 2016

Loved the course and the content. Only critique is that I would have liked to have a lecture about transformations, and interpretation of transformed data coefficients.

By Samy S

Feb 25, 2016

Good introduction to the usefulness and traps of linear models. By the way, having the teacher filmed for the lectures does provide a more engaging experience.

By Elnaz H N

Feb 18, 2024

The instructor explains the course in detail. He speaks word by word and if you are an international student, you will be able to understand what he says.

By Maxim M

Dec 10, 2017

A very good course, goes deeply into the material. The pace of the professor is ok. It's nice that he uses some practical cases to explain the theory.

By 20e

Aug 6, 2018

Helpful!

If there is more introduction about the common problems people may encounter during working in the real world, the course will be better!

By Paul F G

Jun 12, 2018

Excellent, highly focused course with current R libraries for learning various regression methods and methodology. I highly recommend this course.

By Hernan S

May 19, 2018

This course is perfect to get started with Regression Models in R! I think you would need some familiarity with the statistical concept though.

By Emanuele M

Aug 11, 2016

It's a great course and tought very well. It required effort, you apply many of previously teach concept and requires a lot of excercise

By Abhinav G

Jun 28, 2017

Very Helpful course. I am from a non -stats background and this has helped me a lot in understanding such deep concepts of Statistics.

By Connor B

Sep 12, 2019

Learned a lot and enjoyed the course project. Would like to have two course projects because I gain the most out of them.

By Ivana L

Jan 30, 2016

One of the most valuable course in series. Also one of the hardest, expecially if you are newbie to regression models.

By Joseph R

Mar 3, 2016

A very well organized course with nice simple explanations and introductions into the world of regression models

By Gregorio A A P

Aug 26, 2017

Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

By Marcelo G

Aug 14, 2016

Outstanding material with different levels of difficulty and depth on the subject. Great source material.

By Erika G

Jun 27, 2016

I had a lot of fun in this course. The exercises in the text and quizzes help me understand the concepts

By hyunwoo j

Mar 16, 2016

easy to understand and full of new idea about using R.

especially 'manipulate' package is very useful

By Carlos A D V

Jul 26, 2018

The best course of the Data Science Specilization until now and by far. Very practical and useful!

By Ahmed M S K

Jun 20, 2017

One of the best courses on Coursera for sure. Thank you so much. Regression has never been easier.

By Guilherme B F

Mar 22, 2018

Really good. Easy to follow and great even if you just need a refresher in regression models.

By Arcenis R

Jan 18, 2016

This course is packed with great lessons and Prof. Caffo puts it all together very cogently.

By Walter T

Dec 8, 2016

A well defined learning path to understand the fundation of machine learning techniques.