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学生对 杜克大学 提供的 Linear Regression and Modeling 的评价和反馈

4.7
1,102 个评分
190 条评论

课程概述

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....

热门审阅

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.

筛选依据:

126 - Linear Regression and Modeling 的 150 个评论(共 188 个)

创建者 Olga

Jan 27, 2019

Great course!

创建者 Aidi B

Aug 12, 2018

Great Lesson!

创建者 Luo Y

May 30, 2018

Very helpful!

创建者 Ricardo B

Nov 08, 2017

Great course.

创建者 Tian Z

Oct 11, 2017

Very helpful!

创建者 fanjieqi

Feb 01, 2018

Pretty Good!

创建者 Theo A

Dec 21, 2017

Good course.

创建者 Agustin G

Oct 01, 2017

Excelent !!!

创建者 José M C

Mar 22, 2017

Very useful.

创建者 Kuntal G

Oct 27, 2016

Great Course

创建者 Eduardo M

Aug 14, 2019

Excellent!

创建者 Md N I S

Dec 07, 2019

Worth it!

创建者 gerardo r g

Jul 10, 2019

Excellent

创建者 BillyLin

Aug 07, 2016

很棒 学到很多东西

创建者 Bouquegneau

Oct 10, 2017

perfect

创建者 Byeong-eok K

Jul 13, 2017

Great.

创建者 Gencay I

Jan 03, 2019

10/10

创建者 Robert

Nov 23, 2018

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创建者 Yu Y

Oct 27, 2016

.

创建者 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'.

创建者 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.

创建者 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.

创建者 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.

创建者 Aaradhya G

Jan 07, 2020

Again, Dr. Mine Cetinkaya Rundel is amazing. However, linear regression is a vast topic, and maybe another week could have been better. But nonetheless, the concepts explained herein are crystal clear, succinct, and taught in an engaging manner.

创建者 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!