Linear Regression and Modeling に戻る

4.7

1,060件の評価

•

183件のレビュー

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....

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.

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.

フィルター：

by 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.

by 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!

by 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.

by Ana C

•Oct 30, 2016

Excellent Course. Mine, the teacher is a great great teacher. The mentors help a lot.

Technical parts, coursera platform should work better

by Luis F R C

•Oct 27, 2016

Excellent course, I think it still could include more content!

by Erik B

•Feb 26, 2017

Good, but a little "smaller" than the Inferential statistics course (which is very complete). I would have liked to also learn Logistics regression, which I now have to learn elsewhere.

by Saif U K

•Jul 20, 2016

An extremely good introductory course. A must for undergraduates. The style of teaching is fluid and you learn concepts step by step. For more advanced learners the only drawback I see is that this is, by default, an introductory course.But still for advanced learners it can be a great (and I really mean great) refresher.

by Jessye M

•Jan 13, 2017

This course was good. However, compared to the other courses in the specialisation had less content. I would have liked to have videos on logistic regression as well.

by Duane S

•Mar 30, 2017

This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.

by Daniel C

•Apr 20, 2017

Very useful insights and lea

by zhenyue z

•Jun 07, 2016

nice lecture, but it is really too short, not into too much details.

by Ananda R

•Mar 14, 2018

excellent

by Charles G

•Jan 20, 2018

Good but I felt some gaps in the material made it difficult to learn. Also, the quiz questions are focused on attention to detail "gotcha" questions. This can be frustrating.

by 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!

by 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'.

by 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

by FangYiWang

•Apr 19, 2019

A good course for Bayesian statistics.

by Lalu P L

•Apr 22, 2019

Could be more informative

by Guillermo U O G

•May 12, 2019

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

by Natalie R

•Jun 03, 2019

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

by Siyao G

•Aug 06, 2019

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

by 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.

by QIAN Y

•Jul 01, 2016

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

by 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.

by 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.)