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.
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.
Robert is taking this course right now, spending 3 hours a week on it and found the course difficulty to be hard.
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.
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%.
The problem with mr. Caffo, is that he rarely finishes his ideas. He starts talking about a statistical method and concludes nothing tangible or usable, or practical. For instance, when using anova to select models, I had to look different sources to understand how to read properly the F factor. He just enunciates possible tools and mathematical instruments and then expects us to use them to solve practical problems.
I was hard to apply his methods to the course, how hard it is going to be in the real life?
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 inferior to the free OpenIntro textbook. Course quizzes and the project were unclearly specified and quirky in focus, while the project's peer grading was a bit random. I survived this course (and Inferential Statistics) by taking a normal university course on statistics simultaneously. Also, I followed up on this one by reviewing logistic regression in the OpenIntro textbook.
As an aside, the Inferential Statistics and Regression Models courses seem almost completely detached from the rest of the sequence, which is a shame.
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Brandtcompleted this course, spending 3 hours a week on it and found the course difficulty to be easy.
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 regression, multiple linear regression, regression model diagnostics, and logistic and poisson regression.
The material here is rather strangely-presented. As with Statistical Inference, it is light on intuition, so students will have a hard time applying techniques learned here in an appropriate way without previous experience. Although a fair …
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 regression, multiple linear regression, regression model diagnostics, and logistic and poisson regression.
The material here is rather strangely-presented. As with Statistical Inference, it is light on intuition, so students will have a hard time applying techniques learned here in an appropriate way without previous experience. Although a fair amount of math is presented for each topic, it is not at a level deep enough for students to really grasp how it works, so the course tries to walk a line behind mathematical rigor and application and mostly fails at both. The course project was relatively straightforward, using the mtcars dataset in R to predict miles per gallon by transmission type (automatic versus manual), with adjustments for other variables in the dataset. Overall I think I spent less than 10-12 hours on the entire course, and I technically took this concurrently with Statistical Inference, although I enrolled in this course only after completing the majority of the previous course.
Overall, two stars. Probably the least useful course in the specialization so far. I have some previous experience in linear regression and have taken a graduate-level course in the area and have published several papers using these techniques, and I did not find this course to be particularly intuitive or useful. A new specialization (Statistics with R) from Duke will include a course covering these topics, so this may be a better choice for someone wanting to learn how to apply these techniques to his/her own data.
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.
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Viditcompleted this course, spending 14 hours a week on it and found the course difficulty to be hard.
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.. half-baked 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
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Jasoncompleted this course, spending 3 hours a week on it and found the course difficulty to be medium.
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.
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Stevecompleted this course, spending 10 hours a week on it and found the course difficulty to be hard.
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 effective teacher. If you love lectures that consist of long proofs and derivations, you'll love Professor Caffo. I prefer more practical lectures, where you're shown how to apply what you're learning to the real world. There was none of that.
Fortunately I already had knowledge of some of the topics covered. Otherwise, I would have been in deep trouble. As it was, I had to take the class twice because I wasn't getting it the first time. In fact, I had to take all of Professor Caffo's classes twice. That wasn't necessary with the other two instructors in the Data Science specialization.
I'd recommend the University of Washington's class on Regression in its Machine Learning specialization, instead.
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 another course by Brian Caffo but in this one, I sometimes felt some hesitations in his voice on how to explain the topic and that just makes me more and more confused (I'd say I have engineering background and not having a strong math knowledge) and in order to have better understanding of the course I also tried to review it more multiple times but even that doesn't change any difference.
So, I dropped the course in the middle of week 3 and switch to another course by the University of Washington which is part of machine learning specialization path.
just awful!!!! Not worth watching!! Definitely not worth paying for it !!!! I only did because I wanted to get the certificate. I got everything I needed to know from different classes
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.
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Maciejcompleted this course, spending 4 hours a week on it and found the course difficulty to be medium.
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 well-thought, 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 .