Overview
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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 scientistâ€™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.
Syllabus
 Week 1: Least Squares and Linear Regression
 This week, we focus on least squares and linear regression.
 Week 2: Linear Regression & Multivariable Regression
 This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
 Week 3: Multivariable Regression, Residuals, & Diagnostics
 This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
 Week 4: Logistic Regression and Poisson Regression
 This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
Taught by
Brian Caffo
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Reviews
2.7 rating, based on 33 Class Central reviews
4.4 rating at Coursera based on 3341 ratings
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As another one said before, I also survived mr. Caffo's courses. He probably is a good researcher and very intelligent man. But SUCKS as a teacher. Dropped the first time and retake it after using other books as sources, then I passed with 100%. Thâ€¦

I took this class a couple times hoping there would be some improvement in the presentation and materials. There was not. If you have some understanding of linear regression going in you run the risk of unlearning what you previously understood. Who knew this was possible . They need to completely redo this class.

I took this class last year and don't know if it's changed. I hope so. It tries to cover too much ground given it's only one month long. Another problem was the instructor, Brian Caffo, who seems like a good guy and good researcher, but not an effeâ€¦

I agree with a comment above  this class should ideally be completely redone (with a different instructor). The emphasis is on derivation of formulas and techniques, not applications to the real world. Also, the course "textbook" is significantly iâ€¦

I've struggled a lot to figure out what are the points of the topics which are explaining in this course but actually it wasn't any relation between them neither any practical usage. Despite I've learned so many statistics stuffs by having anotherâ€¦

Regression Models is the seventh course in the Data Science specialization. As with Statistical Inference, it is taught by Brian Caffo and suffers from the same issues as the preceding course. The course covers least squares, simple linear regressioâ€¦

The lectures are completely worthless and don't tell you what you should look at. Instead, its mathematical formulas with statements like:
"If you run his r statement..."
(5 lines of code with 10 lines of output...
"you can see that these are the covariants to use."
No, I didn't see it. You spent 90% of your time explaining random subtleties of a mathematical equation instead of telling us anything to do with the R code. Heck, anything to do with the material on a high level.
The material is not that hard (if you learn from other sources), but the course here does nothing to explain it to you.

with all due respect to personal accomplishments of instructure, he completely fails as a teacher.
Sometimes it became so difficult to figure out where he is leading the course to.. halfbaked background explanations are suddenly followed by burst of R codes, rushed through examples and not relatable quiz questions at times. the course quality went all the way downhill as it progressed. I am ending this course more confused now 
This course gave a thorough explanations of how regression models works. The instructor repeated some basic and difficult points again and again. R ggplot2 was used to visualize the models and residuals etc. There is a swirl() R package to help students understand the course deeply. If you have some mathematical background, try this course first. It will give you solid foundations of linear regression models. If you look for how to apply linear regression models in realities, this course will be a little difficult to start, but will reward finally.

Horrible presentation of the material! The instructor is clearly delusional  he has no idea what it means to teach a class. Don't take! Learn this material from other sources.

This is a decent class, covering linear regression and a few of its variants in good detail. It's a challenging subject, but presented acceptably here.



Although the lectures were a bit chaotic, the quizzes and the project assignment were perfect for me. TBH, I didn't watch the lectures unless there was something I couldn't solve on my own. The questions are wellthought, insightful and help understand the subject (assuming you really want to get into it). And I always found the right answer in the lectures.
All in all, this course is not suitable for people who would like to be dragged by the hand, and forced to learn something new.

The new vedios that they have added to the course are really good and I really appreciate the effort put in to improve the course. The book on leanpub is nice , the back exercises at the end of each chapter and the vedio solutions that they have provided for each and every question is awesome and it prepares you well for the quizzes and the project .


