Retour à Linear Regression and Modeling

étoiles

1,590 évaluations

This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio....

TM

21 juil. 2020

A great primer on linear regression with labs that help to establish understanding and a project that is focused enough not to be overwhelming, and allows the learner to play around with the concepts

PK

23 mai 2017

Very good course taught by Dr. Mine who is as always a very good teacher. The videos are very eloquent and easy to understand. Highly recommend it if you are looking for a basic refresher course.

Filtrer par :

par Diego R G

•26 mai 2019

It's a very good course for starting to learn about linear regression. Just be aware that the quality of this course is a bit lower than the previous two. There are fewer videos, the book material is shorter (less suggested exercises and the chapters cover fewer things about linear regression) and some quiz exercises of week 2, which should only cover simple linear regression, have some questions about multiple linear regression which is the 3rd week's topic.

Also, as in the previous two courses, the emphasis is on statistics, not programming with R. This means that if you already know statistics and only want to learn how to use R, there are probably better courses out there for you. But if you want to learn or improve your knowledge of statistics, and also learn how to use R, then do take this course. I think that it's much better to start learning R by actually doing some statistical work and seeing first hand what the software is capable of doing with only a few lines of code, even if you don't fully understand the code's syntax at first.

With all that said, if you take the course PAY ATTENTION TO THE LECTURES, READ THE CHAPTERS and DO THE SUGGESTED EXERCISES. I can't stress this enough. If you don't do all of that, you won't learn as much as you should, and it's painfully obvious that some students didn't do all of that when you review their final R projects. Also, take your time with that final project because that's where you will actually learn some things about R and use what you have learned about statistics (you will have to use google to learn how to code some things properly).

par Mindaugas Ž

•7 janv. 2019

The course is good regarding concepts and theoretical exercises, but poor regarding applying new knowledge in R. Since the course is introductory, an instruction how to install R and a list of R functions without clear explanation how they should be applied in general regression situations makes me explore other sources to learn how to apply those concepts (e.g. DataCamp, CRAN-RProject, etc) and then get back to learn theory? Sorry for expectations but course should provide a full and integrated package of knowledge and skills, especially for beginners.

Furthermore, no Machine Learning (ML) is covered as a tool to run a regression.

My proposal is to provide an algorithm with a comprehensive example how to run a regression using R. From data to final model, step-by-step.

par Omar K

•22 sept. 2016

Very good course. while it does not cover everything. the teacher does a great job explaining things in a simple manner. My feed back would be to move ANOVA into this module.

par Assaf B

•15 mars 2018

The mathematical depth of this course, is insufficient even at its targeted level, and therefore a lot of practical manipulations of the data, and fine tuning of the model could be had if a week more has been put into this course.

Easy does not equate fun, after completing this course, I left the specialization.

par Vijay P S

•15 sept. 2017

fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.

though you need to complete the prior courses to understand this.

par Richard M

•5 févr. 2019

Really great course, clear and easy to follow. Highlight recommended.

par Katy S

•18 avr. 2020

I enjoyed the course and learned a lot overall. However, we were extraordinarily unprepared for the final project. The dataset was full of characteristics that were explicitly not covered in the course. I had to do a ton of outside research in order to complete the project and understand what I was doing.

Additionally, the forums are completely unhelpful. I never got a reply to any of my questions, and saw many other unanswered questions while browsing.

par mark n

•27 juil. 2018

Great instruction on stats, however the R portion a weekly project that is largely self directed, very little instruction.

par QIAN Y

•1 juil. 2016

Compared to other courses in the specification, this course content is too shallow and brief.

par M. I F

•9 juin 2016

She just started with wk 2. There should have been more explanation and videos in week 1...not very interesting. I think statistics you need to take in person.

par Anukul

•3 avr. 2019

it provides a superficial knowledge. A deep understanding of subject can not be gain from this course

par Syed S R

•13 sept. 2018

Not suitable for beginners

par Adedayo M A

•1 juin 2020

This is arguably the best online course I have ever done. The teacher and the way she drives home her point her spot-on. Until now, I have struggled with most aspects of linear regression, e.g model selection, model fitting and interpretation of the results. Undertaken this course has cleared all these shortcomings and I can't wait to start analyzing my PhD project. I appreciate the opportunity afforded me by Duke University and Coursera for participating in the course, it will indeed help me in my academic pursuit and lastly many thanks to the facilitator of the course Dr. Mine Çetinkaya-Rundel

I appreciate.

Adedayo Michael, AWONIYI

par GHALI M

•30 juil. 2020

Well structured course , huge thank to you,I learned a lot, in fact, while learning this course, I was in a discovery internship in an office of agriculture, and I was giving data about date production of a certain product, rain, temperature, and thanks to this course I knew what to do and I manage to fit a multiple linear model.The result was not perfect due to the lack of data and other factors, but I was very satisfied with my work.

par Monique O V

•8 juin 2020

Excellent course. I feel this specialization and this course are far more rigorous than the statistics class I took in college. The professor does an excellent job of making the material very intuitive. The steps needed to develop a sound linear regression model are very clearly explained; the diagnostics are clearly laid out with examples of how to test each condition. I highly recommend this class.

par Anne B

•29 oct. 2018

This course was very challenging. I learn a lot with the model we have to find and it is very interesting to note other students. None of us found the same results. For me, it is very strange not to know at the end what are the good results. It seems that you change the subject overtime. Do you send the correction?

It will be nice to know if we reasoned correctly.

par Can

•19 déc. 2017

Great course. The instructor is very clear on the statistical concepts and thorough on the application of various methods. I learned a lot about how to do regression analysis from this course. The R integration is very helpful as well. Overall great course! Everybody should take it and complete all the quizzes and the final project.

par Sherrod B

•16 juil. 2019

This course was exactly what I needed for a project involving logistic regression. Difficult (way past beginner!) but clear. Doing all the exercises in the workbook cemented my knowledge. Good final project. Very interesting to see other people's results from the final project. Great teacher! Thanks Duke!

par Dario B

•19 déc. 2018

Great course, just like the rest of the specialization.

I am just missing math formality, but I guess that I shall target a different type of course (perhaps even of platform) for that.

Great professor; one can see that besides mastering the material, she has done the homework regarding teaching techniques.

par Sehrish M

•17 juil. 2020

This course helped me in understanding the real story behind the numbers that we get in outcomes after running a linear regression , making predictions and interpreting the data. the tutor is really good and explains everything in a very simple manner. Thanks Courseera and thanks Duke University.

par Sandro H

•22 juin 2020

The content of the regression course is an essential first step into the world of data modeling. Mine as always did a remarkable job of intertwining theory with practice. The final project was certainly time-consuming and challenging, but extremely worth it to integrate the material well.

par Q R

•17 mars 2021

I do like the Duke way of teaching statistic, very clear and easy to understand. The final project is interesting and you can learn a lot while doing it, but it won't be enough to do it using the knowledge from this course only, you need to learn from online researching along the way.

par Vishal T

•3 juin 2020

The course is very interesting and the concepts behind the regression analysis are very well explained and the pedagogy adopted by the professor is excellent and with the various examples and in between video quizzes, the implementation part of the concept was given complete justice.

par anand v

•5 juil. 2020

This course checked off many boxes: theoretical concepts, assumptions, R code, interpreting the output, thought-provoking questions, non-trivial quizzes and interesting data analysis project. I am grateful to the instructor for putting together such a useful course. Thank you !

par Minas-Marios V

•1 mars 2017

As with the previous courses on this Specialiazation, the instructor makes the difference. With detailed examples, clear explanations and a very handy supplementary e-book provided for free, this is a must course for everyone wanting to learn Statistics. Highly recommended!

- Analyste de données Google
- Gestion de projet Google
- Conception d'expérience utilisateur Google
- Google IT Support
- Science des données IBM
- Analyste de données d'IBM
- Analyse des données IBM avec Excel et R
- Analyste de cybersécurité d'IBM
- Ingénierie des données IBM
- Développeur(euse) Cloud Full Stack IBM
- Marketing appliqué au réseau social Facebook
- Analyse marketing sur Facebook
- Sales Development Representative Salesforce
- Opérations de ventes Salesforce
- Connaître la comptabilité sur le bout des doigts
- Préparation à la certification Google Cloud : architecte de Cloud
- Préparation à la certification Google Cloud : ingénieur(e) en données sur Cloud
- Lancez votre carrière
- Préparez-vous pour obtenir un certificat
- Faire progresser votre carrière

- cours gratuits
- Apprendre une langue
- python
- Java
- conception web
- SQL
- Cursos Gratis
- Microsoft Excel
- Gestion de projet
- Cybersécurité
- Ressources humaines
- Cours gratuits en Science de données
- parler anglais
- Rédaction de contenu
- Développement Web Full Stack
- Intelligence artificielle
- Programmation en C
- Compétences en communication
- Blockchain
- Voir tous les cours

- Compétences pour les équipes en charge de la science de données
- Prise de décisions basées sur les données
- Compétences en génie logiciel
- Compétences personnelles pour les équipes d'ingénieurs
- Compétences en gestion
- Compétences en marketing
- Compétences pour les équipes en charge des ventes
- Compétences en gestion de produits
- Compétences en finance
- Cours populaires de science des données au Royaume-Uni
- Beliebte Technologiekurse in Deutschland
- Certifications populaires en cybersécurité
- Certifications populaires en informatique
- Certifications SQL populaires
- Guide de carrière de responsable marketing
- Guide de carrière de chef de projet
- Compétences de programmation en Python
- Guide de carrière de développeur Web
- Compétences d'analyste de données
- Compétences pour un concepteur UX

- Certificats MasterTrack®
- Certificats Professionnels
- Certificats d'université
- MBA & diplômes commerciaux
- Diplômes en science des données
- Diplômes en informatique
- Diplômes en analyse des données
- Diplômes de santé publique
- Diplômes en sciences sociales
- Diplômes en gestion
- Diplômes des meilleures universités européennes
- Masters
- Licences
- Diplôme avec un Parcours de performance
- Cours de BSc
- Qu'est-ce qu'une licence ?
- Combien de temps dure un Master ?
- Un MBA en ligne vaut-il le coup ?
- 7 façons de payer ses études supérieures
- Voir tous les certificats