Regression Models
date_range Débute le 13 mars 2017
event_note Se termine le 10 avril 2017
list 4 séquences
assignment Niveau : Introductif
label Informatique & Programmation
chat_bubble_outline Langue : Anglais
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3.6 /5
Avis de la communauté
107 avis

Les infos clés

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En résumé

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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Le programme

  • 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.

Le concepteur

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

La plateforme

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.

Avis de la communauté
3.6 /5 Moyenne
Le meilleur avis

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!

le 14 février 2018
Quelle note donnez-vous à cette ressource ?
le 20 février 2018

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.

le 14 février 2018

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!

le 1 février 2018

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.

le 30 janvier 2018

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 .

le 21 janvier 2018

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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