Zurück zu Linear Regression and Modeling

Sterne

1,505 Bewertungen

•

278 Bewertungen

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....

TM

21. Juli 2020

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

23. Mai 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.

Filtern nach:

von Diego R G

•26. Mai 2019

It's a very good course for starting to learn about linear regression. Just be aware that the quality of this course is a bit lower than the previous two. There are fewer videos, the book material is shorter (less suggested exercises and the chapters cover fewer things about linear regression) and some quiz exercises of week 2, which should only cover simple linear regression, have some questions about multiple linear regression which is the 3rd week's topic.

Also, as in the previous two courses, the emphasis is on statistics, not programming with R. This means that if you already know statistics and only want to learn how to use R, there are probably better courses out there for you. But if you want to learn or improve your knowledge of statistics, and also learn how to use R, then do take this course. I think that it's much better to start learning R by actually doing some statistical work and seeing first hand what the software is capable of doing with only a few lines of code, even if you don't fully understand the code's syntax at first.

With all that said, if you take the course PAY ATTENTION TO THE LECTURES, READ THE CHAPTERS and DO THE SUGGESTED EXERCISES. I can't stress this enough. If you don't do all of that, you won't learn as much as you should, and it's painfully obvious that some students didn't do all of that when you review their final R projects. Also, take your time with that final project because that's where you will actually learn some things about R and use what you have learned about statistics (you will have to use google to learn how to code some things properly).

von Mindaugas Ž

•7. Jan. 2019

The course is good regarding concepts and theoretical exercises, but poor regarding applying new knowledge in R. Since the course is introductory, an instruction how to install R and a list of R functions without clear explanation how they should be applied in general regression situations makes me explore other sources to learn how to apply those concepts (e.g. DataCamp, CRAN-RProject, etc) and then get back to learn theory? Sorry for expectations but course should provide a full and integrated package of knowledge and skills, especially for beginners.

Furthermore, no Machine Learning (ML) is covered as a tool to run a regression.

My proposal is to provide an algorithm with a comprehensive example how to run a regression using R. From data to final model, step-by-step.

von Omar K

•22. Sep. 2016

Very good course. while it does not cover everything. the teacher does a great job explaining things in a simple manner. My feed back would be to move ANOVA into this module.

von Assaf B

•15. März 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.

von Vijay P S

•15. Sep. 2017

fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.

though you need to complete the prior courses to understand this.

von Richard M

•5. Feb. 2019

Really great course, clear and easy to follow. Highlight recommended.

von Katy S

•18. Apr. 2020

I enjoyed the course and learned a lot overall. However, we were extraordinarily unprepared for the final project. The dataset was full of characteristics that were explicitly not covered in the course. I had to do a ton of outside research in order to complete the project and understand what I was doing.

Additionally, the forums are completely unhelpful. I never got a reply to any of my questions, and saw many other unanswered questions while browsing.

von mark n

•27. Juli 2018

Great instruction on stats, however the R portion a weekly project that is largely self directed, very little instruction.

von QIAN Y

•1. Juli 2016

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

von M. I F

•9. Juni 2016

She just started with wk 2. There should have been more explanation and videos in week 1...not very interesting. I think statistics you need to take in person.

von Anukul

•3. Apr. 2019

it provides a superficial knowledge. A deep understanding of subject can not be gain from this course

von Syed S R

•13. Sep. 2018

Not suitable for beginners

von Adedayo M A

•1. Juni 2020

This is arguably the best online course I have ever done. The teacher and the way she drives home her point her spot-on. Until now, I have struggled with most aspects of linear regression, e.g model selection, model fitting and interpretation of the results. Undertaken this course has cleared all these shortcomings and I can't wait to start analyzing my PhD project. I appreciate the opportunity afforded me by Duke University and Coursera for participating in the course, it will indeed help me in my academic pursuit and lastly many thanks to the facilitator of the course Dr. Mine Çetinkaya-Rundel

I appreciate.

Adedayo Michael, AWONIYI

von Ghali M

•30. Juli 2020

Well structured course , huge thank to you,I learned a lot, in fact, while learning this course, I was in a discovery internship in an office of agriculture, and I was giving data about date production of a certain product, rain, temperature, and thanks to this course I knew what to do and I manage to fit a multiple linear model.The result was not perfect due to the lack of data and other factors, but I was very satisfied with my work.

von Monique O V

•8. Juni 2020

Excellent course. I feel this specialization and this course are far more rigorous than the statistics class I took in college. The professor does an excellent job of making the material very intuitive. The steps needed to develop a sound linear regression model are very clearly explained; the diagnostics are clearly laid out with examples of how to test each condition. I highly recommend this class.

von Anne B

•29. Okt. 2018

This course was very challenging. I learn a lot with the model we have to find and it is very interesting to note other students. None of us found the same results. For me, it is very strange not to know at the end what are the good results. It seems that you change the subject overtime. Do you send the correction?

It will be nice to know if we reasoned correctly.

von Can Z

•19. Dez. 2017

Great course. The instructor is very clear on the statistical concepts and thorough on the application of various methods. I learned a lot about how to do regression analysis from this course. The R integration is very helpful as well. Overall great course! Everybody should take it and complete all the quizzes and the final project.

von Sherrod B

•16. Juli 2019

This course was exactly what I needed for a project involving logistic regression. Difficult (way past beginner!) but clear. Doing all the exercises in the workbook cemented my knowledge. Good final project. Very interesting to see other people's results from the final project. Great teacher! Thanks Duke!

von Dario B

•19. Dez. 2018

Great course, just like the rest of the specialization.

I am just missing math formality, but I guess that I shall target a different type of course (perhaps even of platform) for that.

Great professor; one can see that besides mastering the material, she has done the homework regarding teaching techniques.

von Sehrish M

•17. Juli 2020

This course helped me in understanding the real story behind the numbers that we get in outcomes after running a linear regression , making predictions and interpreting the data. the tutor is really good and explains everything in a very simple manner. Thanks Courseera and thanks Duke University.

von Sandro H

•22. Juni 2020

The content of the regression course is an essential first step into the world of data modeling. Mine as always did a remarkable job of intertwining theory with practice. The final project was certainly time-consuming and challenging, but extremely worth it to integrate the material well.

von Vishal T

•3. Juni 2020

The course is very interesting and the concepts behind the regression analysis are very well explained and the pedagogy adopted by the professor is excellent and with the various examples and in between video quizzes, the implementation part of the concept was given complete justice.

von anand v

•5. Juli 2020

This course checked off many boxes: theoretical concepts, assumptions, R code, interpreting the output, thought-provoking questions, non-trivial quizzes and interesting data analysis project. I am grateful to the instructor for putting together such a useful course. Thank you !

von Minas-Marios V

•1. März 2017

As with the previous courses on this Specialiazation, the instructor makes the difference. With detailed examples, clear explanations and a very handy supplementary e-book provided for free, this is a must course for everyone wanting to learn Statistics. Highly recommended!

von Ann N

•2. Aug. 2017

I truly enjoyed this course. It's one of the most useful and easier to understand than the rest. Could use more samples and information on how to deal with Categorical Data though.

Final Project is a full-time job. 1-week while working full-time is hardly enough time.

- Sinn und Zweck im Leben finden
- Medizinische Forschung verstehen
- Japanisch für Anfänger
- Einführung in Cloud Computing
- Grundlagen der Achtsamkeit
- Grundlagen des Finanzwesens
- Maschinelles Lernen
- Maschinelles Lernen mittels Sas Viya
- Die Wissenschaft des Wohlbefindens
- Contact-Tracing im Kontext von COVID-19
- KI für alle
- Finanzmärkte
- Einführung in die Psychologie
- Erste Schritte mit AWS
- Internationales Marketing
- C++
- Predictive Analytics und Data-Mining
- UCSD: Learning How to Learn
- Michigan: Programming for Everybody
- JHU: R-Programmierung
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- KI für Medizin
- Guter Umgang mit Worten: Redaktionelles Schreiben
- Modellbildung von Infektionskrankheiten
- Die Aussprache des US-amerikanischen Englisch
- Software-Testautomatisierung
- Deep Learning
- Python für alle
- Data Science
- Geschäftsgründungen
- Excel-Kenntnisse für Beruf
- Data Science mit Python
- Finance for Everyone
- Kommunikationsfähigkeiten für Ingenieure
- Verkaufstraining
- Career Brand Management
- Wharton: Unternehmensanalytik
- Penn: Positive Psychology
- Washington: Maschinelles Lernen
- CalArts: Grafikdesign

- Zertifikate über berufliche Qualifikation
- MasterTrack-Zertifizierungen
- Google IT-Support
- IBM Datenverarbeitung
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI: Angewandtes Projektmanagement
- Zertifizierung in Instructional Design
- Zertifizierung in Bauwesen und -management
- Zertifizierung in Big Data
- Zertifizierung Maschinelles Lernen für Analytics
- Zertifizierung in Innovation Management & Entrepreneurship
- Zertifizierung in Nachhaltigkeit und Entwicklung
- Zertifizierung in Soziale Arbeit
- Zertifizierung KI und maschinelles Lernen
- Zertifizierung in Räumliche Datenanalyse und Visualisierung

- Abschlüsse in Informatik
- Business-Abschlüsse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Data Science
- Bachelorabschlüsse
- Bachelor of Computer Science
- MS Elektrotechnik
- Bachelor Completion Degree
- MS Management
- MS Informatik
- MPH
- Master-Abschluss in Buchhaltung
- MCIT
- MBA online
- Master of Applied Data Science
- Global MBA
- Master in Innovation & Entrepreneurship
- MCS Data Science
- Master in Informatik
- Master-Abschluss in Public Health