Chevron Left
Retour à Linear Regression and Modeling

Avis et commentaires pour l'étudiant pour Linear Regression and Modeling par Université Duke

4.7
997 notes
177 avis

À propos du cours

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....

Meilleurs avis

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.

RZ

May 25, 2019

I feel I'm running out of complement words for this course series. In conclusion, clear teaching, helpful project, and knowledgeable classmates that I can learn from through final project.

Filtrer par :

151 - 175 sur 175 Examens pour Linear Regression and Modeling

par Sean T

Jul 04, 2018

Really enjoyed this course! It teaches you the theory you need to understand how a linear regression model works, how to check that your model fulfils certain conditions so that it is valid, and how to build and implement your model in practice!

par Anna D

May 22, 2017

Great course and lots of useful knowledge!

par Jian S

Dec 12, 2016

I learnt quite a bit. One of the most useful courses! I would suggest add more exercises in R.

par Tomasz J

Oct 15, 2017

Very good and gentle introduction to linear regression. The final assignment however uses dataset which is very risky to use with linear regression (not all conditions were met in all the assignments I rated!). This is confusing.

par Christian A

Apr 25, 2018

Really good course as the previous ones in this specialization. Could have included something more on checking for collinearity with categorical variables.

par Artur A B

May 10, 2017

The material is very straightforward and gives a great introduction to multiple linear regression. My only reservation is the length of the course, which seems to be a bit shorter than other courses in the certification. Would love to have more material/in-depth exposure to components available to us in R.

par Syed M R A

Mar 20, 2018

Awesome course.

par NG Y W

Dec 12, 2016

This course has provided me with a good and simple understanding on the concept

par 冯允鹏

Nov 27, 2016

Compared to the Course 2 Statistic inference, this session seems to be a little be informal and rush. But still learn a lot from the conception of linear regression!

par Aydar A

Dec 20, 2017

Nice course. The downside is that it only explains interpretation of linear regression, but not enough details about how linear regression is performed from math point of view.

par 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'.

par Dgo D

Mar 30, 2017

It was a really good introduction to Linear Model, I recommend this course to all people who wants to learn more about statistical analysis

par Guillermo U O G

May 12, 2019

I liked, but I guess it could improve little by including more topics in linear regression analysis.

par FangYiWang

Apr 19, 2019

A good course for Bayesian statistics.

par Lalu P L

Apr 22, 2019

Could be more informative

par Natalie R

Jun 03, 2019

Clearly presented. R instruction is pretty minimal, so there is a lot of trial and error and googling.

par Siyao G

Aug 06, 2019

Contents are easier compared with other courses in this series. Quite systematic and easy to understand.

par Zhao L

Aug 09, 2016

Covers the basic of Linear Regression, would like to see more advanced material.

par QIAN Y

Jul 01, 2016

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

par Brandon F

Oct 16, 2017

Provides a good overview, but I felt some loose ends were not addressed in terms of how stringent the conditions need to be met, and if one can use MLR when this is not the case.

par Assaf B

Mar 15, 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 Micah H

Apr 30, 2018

Other nits about the depth and breadth of the course aside, I thought it was a good course. The main critique I have to offer is the lack of emphasis of using the power of R. When teaching model selection, the course should have at least provided instruction—or at least a written resource—on how to write the R code for automating forward/backward selection by R^2.* Being a course about using R as well as about linear regression and modeling, it seems like the appropriate thing to do.

(*A classmate whose final project I peer-reviewed used for loops to run the forward model selection based on R^2. That's how I learned about it.)

par Kshitij T

Nov 03, 2017

Only contains linear regression as opposed to other model types

par Naivadhya

Jun 30, 2019

not so good

par 楼啸

Aug 18, 2019

The grading is just so annoying