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Linear Regression and Modeling

Linear Regression and Modeling

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8.3/10 (Our Score)
Product is rated as #35 in category R

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. Duke University has about 13,000 undergraduate and graduate students and a world–class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

Instructor Details

Mine Cetinkaya-Rundel is an Assistant Professor of the Practice at the Department of Statistical Science at Duke University. She received her Ph.D. in Statistics from the University of California, Los Angeles, and a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Cetinkaya-Rundel is primarily interested in innovative approaches to statistics pedagogy. Some of her recent work focuses on developing student-centered learning tools for introductory statistics courses, teaching computation at the introductory statistics level with an emphasis on reproducibility, and exploring the gender gap in self-efficacy in STEM fields. Her research interests also include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and a contributing member of the OpenIntro project, whose mission is to make educational products that are open-licensed, transparent, and help lower barriers to education. She is also a co-editor of the Citizen Statistician blog and a contributor to the Taking a Chance in the Classroom column in Chance Magazine.

Specification: Linear Regression and Modeling

Duration

14 hours

Year

2016

Level

Beginner

Certificate

Yes

Quizzes

No

51 reviews for Linear Regression and Modeling

4.2 out of 5
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  1. Jaime R

    Very useful and practical

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  2. Pedro G F M

    Great course! as a suggestion I Believe Duke should publish new courses on other prediction tools (like SVM, for example)

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  3. Robert

    /

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  4. Abdullatif A

    The flow of information is clear and understandable.

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  5. Toan T L

    A good course

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  6. Tulio R C

    Amazing content!

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  7. Allah D N

    Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.

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  8. Dario B

    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.

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  9. Nathan H

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

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  10. Charles C

    The course provides good insights for linear regression. Also, I think the professors are intended to statistics and its application in real life.

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  11. Gencay I

    10/10

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  12. Mindaugas Z

    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.

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  13. Daniel C J

    A great intro to linear regression, both from theoretical and practical point of view. Really enjoyed the course!

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  14. PRIYANKA D

    Exceptionally helpful for beginners due to perfect combination of theoretical and practical sessions.

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  15. YASHKUMAR R T

    Simple syllabus, but excellent explanation.

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  16. Sergio E T

    The applications of linear regression models are vast. This is a useful course.

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  17. Olga

    Great course!

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  18. Richard M

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

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  19. Aleix D

    Very good, and most important of all, very well explained!

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  20. GUO S

    Nice module! It is very clear.

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  21. Henri M

    Great contents and great teacher. I enjoyed it very much.

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  22. Alfredo J N

    Excellent. A well designed course and the explanation are very easy to understand

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  23. Anukul

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

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  24. FangYiWang

    A good course for Bayesian statistics.

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  25. Lalu P L

    Could be more informative

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  26. Guillermo U O G

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

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  27. Rui Z

    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.

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  28. Diego R G

    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).

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  29. Jacob T

    Incredible course with interesting projects and excellent explanation.

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  30. schlies

    Good videos and projects

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  31. Natalie R

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

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  32. Heungbak C

    Good lectures! I learned many thing from this course! Thanks

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  33. Naivadhya

    not so good

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  34. gerardo r g

    Excellent

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  35. Sherrod B

    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!

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  36. Siyao G

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

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  37. Eduardo M

    Excellent!

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  38. Tran T H

    It’s helpful for me!

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  39. Parab N S

    An excellent course by Professor Rundel on Linear Regression and Modelling

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  40. Chris A D

    This course explains the statistical aspects of linear regression. A detailed explanation of minute aspects of linear regressions. The quizzes and assignments are quite exciting. Recommend to anyone with little know (4/10) knowledge regarding Linear Regression.

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  41. Veliko D

    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.

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  42. Jingyi Y

    lectures are great. but no tutor answers my question posted in discussion board.

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  43. Michael O T

    It is a course with an excelent level, it is well evaluated and one can to learn a lot.

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  44. Giulia T

    Nice introduction to linear modelling! really easy to follow

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  45. Md N I S

    Worth it!

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  46. Valeriy K

    Another fantastic course by Duke staff. I’d love to thank Professor Cetinkaya Rundel for the passion that she shares. I really loved working on weekly labs and a final project. I learned a lot of tools and developed my own functions while solving the tasks.

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  47. Bhabani D

    Wonderful course

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  48. Aaradhya G

    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.

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  49. Nikhil K

    Not covered entire regression technique

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  50. Dongliang Z

    Excellent Course. Make anyone who is fear staticstics understand Regression.

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  51. Gor S

    The course is very beneficial both in terms of learning regression modeling and R programming.

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