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.

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4 / 5

Terminé

Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.

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5 / 5

Terminé

I learned a lot through this course! It's not easy, and there's a lot of technical details that required me to watch the videos 2-3 times through to have a proper grasp, but super helpful stuff!

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3 / 5

Terminé

Lots of room for improvement on this course, the teacher really seems like he cares but he is a really bad teacher nonetheless. The course material is incomplete and not properly structured. Basically read the book if you want to learn something, otherwise the videos don't really help. Also, the course project is not worth it because you get no real feedback to compare your project to the ideal or at least expected answer. I would not recommend this course.

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2 / 5

Terminé

Just like the previous course in the specialization path (Statistical Inference) the course delves into some relevant topics however it doesn't feel as properly structured. While on the first week the lectures seem to try to give a basic and comprehensible learning of linear regression, once we start into the more advanced topics it gets confusing.Lots of formulas and concepts thrown at you without much clarification. For someone without any knowledge/background on statistics this can be quite difficult to grasp the concepts.The module for Poisson Regression is very poor in terms of information. just feels like a very light overview of the matter.The course should be reviewed or at least the indication of "Beginner Specialization.No prior experience required." should be updated to mention that some knowledge in statistics is recommended .

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4 / 5

Terminé

Thanks for the great course! I think the following can be improved: 1) More depth, I find myself keep looking for additional materials from other sources, e.g. proof of different theories, the course only provides overview, but didn't go deep enough 2) Project: I find the optional quiz project more interesting, the final project is too simple, and didn't include things we learnt such as GLM etc. A more comprehensive final project with more aspects of courses knowledge will be much better to re-solidate learning

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2 / 5

Terminé

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.

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5 / 5

Terminé

The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..

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1 / 5

Terminé

Personally, I am not a fan of this professor. He over-explains all of the topics, just to tell you at the end of the lecture that you don't need to know the specifics and can do it all with one function. He is very unengaging, difficult to follow, and rushes through lectures. And finally, HE BLOCKS THE SLIDES WITH HIS HEAD SO YOU CAN'T SEE THE NOTES. I feel like out of all the professors in this specialization course, there were so many others who could have taught the material better, especially since this is probably the most important course of the entire specialization. I feel like I only began to understand the material once I finished the course project, and even then I have no idea how regression models work.I'm now going to be taking a month or 2 off from the courses to read more about statistical inference and regression models on my own, since I feel completely unprepared for the upcoming Machine Learning course.

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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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