Polynomial Regression in R | R Tutorial 5.12 | MarinStatsLectures

In this R video tutorial, we will learn how to fit the polynomial regression model and assess Polynomial Regression in R using the partial F-test with examples.

Polynomial regression is a form of regression analysis in which the relationship between the independent variable X and the dependent variable Y is modeled as an nth degree polynomial in x. Polynomial regression models are useful when the relationship between the independent variables(X) and the dependent variables(Y) is not linear.

These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio.

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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)

These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.

Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

[dioszegigergo]

Dear Marin and Ladan,

hats off! Clearly explained with such a deep knowledge and human understanding! Thank you very-very much! You=lm (teacher~knowledge+I(statwizard^3)), a talent="TRUE" in your field. Enjoy your life with your family and if you find the time and opportunity, the new series on guiding in the space of R, is highly welcome!

All the best,
Gergo Dioszegi

[alfredkik]

You videos helped me to complete my MSc degree successfully - thank you very much for your very informative videos!

[LuisFuentes]

Hello Mike,

With this video I've finished your course of videos of the introduction of R and I don't have the words to express my gratitude. Thanks to your amazing work I've entered the world of data science, and I will continue diving into this wonderful technique full of possibilities. Since I'm an student of Economics this will be incredibly useful.
You have helped me inmensely without asking for anything, as I'm sure you have thousands of other people who feel equally as thankful
The world needs more people like you, and I will try to continue the chain of helping others.

Sincerly from Universidad Carlos III, Madrid,
Luis

[nareshpandey]

Thank you for the best tutorial, you have provided the datasheet which made it more beneficial

[richaagrawal]

Hello Mike,
I am glad you managed to teach all of us with such explanatory and step by step approach. I watched all your series 5 videos and i wish more people can take advantage of your knowledge and skill. Thank you so much. Looking forward to more.
Regards
Richa

[hapsyottahuang]

Really excellent tutorail series. Thank you very much.

[neofjcn]

THANKS MAN, THIS VIDEO WAS SUPER USEFUL

[anuragkumar]

All the videos are very informative and interactive. Thanks for very much Professor. :)

[angeld]

Thank you so much. you are way better than my teacher

[huangb]

Thanks for your linear regression series. So helpful!

[anuragkumar]

Waiting for your new tutorials on R programming. :)

[ivanalejandrovaciohernande]

Hi, Mike, What test should I perform on data prior the selection of a Polynomial Model? great video man

[alirostami]

Excellent. Thank you so much for this helpful video. I'm waiting for a new tutorial video.

[munsirali]

Hello Respected Professor Mike Marin, I really appreciate your great tutorials about R. I have watched all of your lectures and paying you more and more gratitude for this great helpful lectures series. And hope it will be continue in future. Wish you have Happy and healthy life. Thank you very much, stay blessed!

[meeadhadi]

Hi. Thank you for your great explanation. The page for Dataset & R Script doesn't exist and the provided link doesn't work.

[marziehsafari]

That was interesting video comparing first- and second-order of polynomial for linear models, I really liked it. Although I am dealing with a mixed model right now and need to do the same comparison for the fist and second order of polynomial for it, and this does not work for me. Do you have some tutorial video for the mixed model as well? Thanks a lot.

[anthonyalanis]

I noticed that the summary output for the cubic model had large p-values for all the coefficients but the multiple R-square still seemed large, the residual error seemed low, and the overall F-statistic was large too, thus we would reject the null (all coefficients=0).
QUESTION: What should we say about each coefficient since their individual p-values are so high?

[Dr_Finbar]

When do you use an orthogonal polynomiall rather than a raw poly?

[ahmet]

Thank you for the nicely explained tutorial. I have a question regarding the Polynomial function. Why do we use the property raw=T in this case? I am currently trying to understand that the multicolinearity is a general problem in this situation since x and x^2 are correlated. The solution to this usually presented by defining raw=F. Therefore by considering only orhtogonal polynomials. But why would orthogonal polynomials only solve the problem of multicolinearity ? Im lost in this field. I hope you can help me out.

[dessywl]

Is polynomial regression same with polynomial orthogonal? Thanks!