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學生對 杜克大学 提供的 Linear Regression and Modeling 的評價和反饋

4.7
1,453 個評分
262 條評論

課程概述

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....

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TM
2020年7月21日

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
2017年5月23日

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.

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226 - Linear Regression and Modeling 的 250 個評論(共 259 個)

創建者 Janice H

2020年6月5日

Lecture explanations are fantastic as are slides. Pace is appropriate. R information is a little sketchy but manageable with diligence.

創建者 Nathan H

2018年12月19日

Very informative for an introduction. Wish it was longer and more mathematical, but there are other courses on Coursera for that.

創建者 Tony G

2017年1月29日

Good overview of regression modeling. Would have liked to see more on logistic regression. But that's ok, can read it on my own.

創建者 Scott T

2016年8月9日

Great course. I only wish there was more time spent on dealing with more complex situations such as overfitting.

創建者 Shivani J

2020年4月5日

I liked the course. I learnt a lot while working on its project. Instructor's way of teaching is very engaging.

創建者 Elham L

2020年4月7日

The material in this course is explained very well. However it requires one has the knowledge in using R.

創建者 Siyao G

2019年8月6日

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

創建者 Natalie R

2019年6月3日

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

創建者 Guillermo U O G

2019年5月11日

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

創建者 marvin m

2020年11月6日

The Lab could be better if there was a video that goes with it. But overall I love this course.

創建者 Jian S

2016年12月12日

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

創建者 NG Y W

2016年12月12日

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

創建者 Amir Z

2016年8月31日

This is a great course for this specialization but don't expect much depth.

創建者 zhenyue z

2016年6月6日

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

創建者 Luis F R C

2016年10月27日

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

創建者 Anna D

2017年5月22日

Great course and lots of useful knowledge!

創建者 Nikhil K

2020年1月25日

Not covered entire regression technique

創建者 FangYiWang

2019年4月18日

A good course for Bayesian statistics.

創建者 Mohammed S S

2020年6月8日

Great model with clear explanations

創建者 Daniel C

2017年4月19日

Very useful insights and lea

創建者 Lalu P L

2019年4月21日

Could be more informative

創建者 Syed M R A

2018年3月19日

Awesome course.

創建者 Toan L T

2018年12月11日

A good course

創建者 Ananda R

2018年3月14日

excellent

創建者 Micah H

2018年4月30日

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.)