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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- Week 1 -
**Week 1: Least Squares and Linear Regression**

This week, we focus on least squares and linear regression. - Week 2 -
**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 -
**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 -
**Week 4: Logistic Regression and Poisson Regression**

This week, we will work on generalized linear models, including binary outcomes and Poisson regression.

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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5 / 5

Terminé

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.

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5 / 5

Terminé

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.

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4 / 5

Terminé

Good course for basic regression. Would have enjoyed more time spent on properly interpreting results and how they are relevant to answering business questions.

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4 / 5

Terminé

Really appreciate the depth of this course, as well as the changes Prof. Caffo made in his teaching style since his Statistical Inference course. However, the reasoning behind some of the more complex topics, like GLMs, aren't adequately explained, and the Swirl lessons are presented in a strange and disorienting order.

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2 / 5

Terminé

Honestly the materials of this course are really confusing. So many focus on the mathematical value instead of real examples and scenarios to use the concepts reached. Also it would benefit if there was a clear message coming through, like Machine Learning course where things follow a order.If it was not by the book of Mr.Field with Statistics in r, I would never be able to understand what was really being said in this course. Or what was the best strategy to effectively do a proper regression analysis and what would be the best models.

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1 / 5

Terminé

If you thought that the previous course (Statistical Inference) with Brian Caffo was a horrible experience -- think twice and get ready for Regression Models. It is way worse. Imagine an instructor starting his explanation by showing you some (rather involved) formula and immediately jumping to the discussion of the various terms without actually telling you clearly what this formula is for and how to use it. Then you will get a pretty good idea about the instructor for this course. He is a horrible teacher, who clearly does not understand what teaching is and how it should be done properly. Total waste of time.

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3 / 5

Terminé

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.

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2 / 5

Terminé

Very math heavy and not super useful for psychology students. Without a tutor, that I had to pay $30 an hour in addition to this course, I would not have passed. The layout was rather convoluted, there were several spelling mistakes (one that completely changed the meaning of a QUIZ question) and it was not as conceptual as I was hoping for. The conceptual limitation is big for me as I don't care about the math, I'm a psych undergrad trying to learn statistics for my honors thesis, not a math course. It also made it difficult to apply what we learned since the data we worked with wasn't that easy to understand and was incredibly boring (car mpg data and insect sprays??). I'm also slightly upset that coursera signed me up for a subscription when all I wanted was one course, very cheeky.

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4 / 5

Terminé

Brian did better job in this course to elaborate and demonstrate with examples. No doubt Brian is extremely knowledge about this subject. Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding. That's why I take one star away.

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2 / 5

Terminé

I was optimistic about this class because it started out fixing some of the pedagogical mistakes the professor made in Statistical Inference, but by the time we got to week 3, it was pretty clear that the course was trying to accomplish too much in 4 weeks, and instead of focusing on the most important parts of regression and making sure they were taught well and understood clearly, I feel the course tried to do far too much. The only reason I gave it two stars instead of one star was the course project was relatable - choosing the best transmission for maximizing mpg is a real-world problem that I can (and did) have a discussion with my mother about. Too many assignments are about something completely inane, like guinea pig teeth or flower petals. If you're going to inspire students to learn the material, the examples (and data) must be relatable to them.

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