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Voltar para Linear Regression and Modeling

Comentários e feedback de alunos de Linear Regression and Modeling da instituição Universidade Duke

4.7
estrelas
1,288 classificações
226 avaliações

Sobre o curso

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....

Melhores avaliações

PK

May 24, 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.

LH

May 15, 2020

It has been a great adventure so far. I still greatly appreciate how final projects are constructed that gives us freedom to choose our approach to the problems within the data set.

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151 — 175 de 225 Avaliações para o Linear Regression and Modeling

por Olga

Jan 27, 2019

Great course!

por Aidi B

Aug 12, 2018

Great Lesson!

por Luo Y

May 30, 2018

Very helpful!

por Ricardo B

Nov 08, 2017

Great course.

por Tian Z

Oct 11, 2017

Very helpful!

por fanjieqi

Feb 01, 2018

Pretty Good!

por Theo A

Dec 21, 2017

Good course.

por Agustin G

Oct 01, 2017

Excelent !!!

por José M C

Mar 22, 2017

Very useful.

por Kuntal G

Oct 27, 2016

Great Course

por Vedant G

Jul 03, 2020

informative

por Yichang L

May 03, 2020

Good course

por Eduardo M

Aug 14, 2019

Excellent!

por Md N I S

Dec 07, 2019

Worth it!

por gerardo r g

Jul 10, 2019

Excellent

por BillyLin

Aug 07, 2016

很棒 学到很多东西

por Bouquegneau

Oct 10, 2017

perfect

por Byeong-eok K

Jul 13, 2017

Great.

por Gencay I

Jan 03, 2019

10/10

por Sanan I

Jun 04, 2020

,

por Robert

Nov 23, 2018

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por Yu Y

Oct 27, 2016

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por Walter V

Jun 28, 2020

The key concepts of linear regression are explain really well, without heavy mathematical explanation, that is good, because the main concepts are what it important.

The project at the end of the course is REALLY good, you can learn a lot from the analysis and investigation you need to do on it, it took me around 30 hours to really understand and complete (a full time job of 1 week), which is really nice.

I give 4 stars to the course, because they don't dig very much in variables selection, specially with categorical variables, with are the ones i had an hard time during the project. Note: It was hard, because it was difficult, but in the process i learnt a lot of things investigating.

Besides this point, the course is really good to say: "I know the basics of linear regression, I know how to handle it in R", the topic of "Linear Regression and Modeling" is of course much, much more larger than what can be explained in 1 course.

por Neeraj P

Feb 08, 2017

First, this course will enable me to understand the quantitative part of a research. Additionally, this will help a student to understand the essence of performing such numerical calculations and will make us understand the relationship between different variables.

Secondly, this is the need of the hour and such numerical functions are used worldwide so, learning this course will help in almost every field be it 'Management' be it 'Social Sciences' or be it 'Human Behaviour'.

por Veliko D

Oct 20, 2019

The course is good and the material is presented clearly. The capstone project is very good and makes you really use all the knowledge obtained in the course and the pre-prequisite course Inferral Statistics. My only dissatisfaction is that the course was rather short: only 3 weeks of material and 1 capstone. Therefor it covered less material then I expected. For example, I expected logistic regression to be covered.